DOCX to PDF via the Foxit PDF Services API: Python and cURL Walkthrough

This walkthrough covers the full DOCX-to-PDF flow on the Foxit PDF Services API with runnable Python and cURL for each call.

Automated document pipelines demand conversion tooling that accepts a file, queues a job, and returns clear status at every step. The Foxit PDF Services API gives you exactly that: a four-endpoint async flow covering upload, convert, poll, and download. Each step returns a typed payload, the task model exposes four explicit states with a numeric progress field, and error codes map cleanly to distinct recovery paths.

This tutorial walks through every step of that flow in Python 3 with the requests library, plus cURL equivalents for each call. You’ll have a runnable convert.py script you can drop into a pipeline today.

Prerequisites

Before you run a single line of this tutorial, get the following in place. Each item links to its canonical install or setup guide.

Python 3.8 or newer — verify with python3 --version. The script uses only standard library modules plus one external package, so any modern 3.x will do.
pip — bundled with Python 3.4+. Verify with python3 -m pip --version.
A virtual environment — isolates project dependencies so they don’t collide with system Python or other projects. See the venv tutorial for platform-specific activation commands.
The requests library — the only third-party dependency in this walkthrough. Installed inside the venv below.
A code editor — Visual Studio Code with the Python extension is a solid default, but PyCharm, Sublime Text, or any editor you like will work.
cURL — pre-installed on macOS and most Linux distros. Windows users can install from the official site or use WSL.
A Foxit Developer account — register for free (no credit card required). The Foxit Developer Portal provisions a default application with your CLIENT_ID and CLIENT_SECRET immediately after signup.

Set up the project workspace:

mkdir foxit-docx-to-pdf && cd foxit-docx-to-pdf
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install requests

Export your credentials as environment variables so the script never sees them as hardcoded strings:

export CLIENT_ID=your_client_id_here
export CLIENT_SECRET=your_client_secret_here

Your Python script reads them with `os.environ.get()`:

import os

CLIENT_ID = os.environ.get("CLIENT_ID")
CLIENT_SECRET = os.environ.get("CLIENT_SECRET")

BASE_URL = "https://na1.fusion.foxit.com"

All four API calls go to https://na1.fusion.foxit.com. The developer portal also offers a live sandbox and pre-built Postman collections if you want to verify calls in a GUI before scripting.

For a sample DOCX to work with right away, download input.docx directly from the foxitsoftware/developerapidemos GitHub repository and save it to your working directory.

How the Auth Model Works

The Foxit PDF Services API authenticates through named request headers. Pass client_id and client_secret directly on every call:

headers = {
    "client_id": CLIENT_ID,
    "client_secret": CLIENT_SECRET,
}

That single dict covers upload (multipart POST), polling (GET), and download (GET). The convert endpoint takes a JSON body, so it requires `Content-Type: application/json` as well:

json_headers = {
    "client_id": CLIENT_ID,
    "client_secret": CLIENT_SECRET,
    "Content-Type": "application/json",
}

The API expects the raw key/secret pair in those named headers. Wrapping credentials in an Authorization: Bearer header instead returns 400, since the required client_id and client_secret headers are missing.

Step 1 and Step 2: Upload the DOCX and Initiate Conversion

Step 1: Upload the DOCX File

POST /pdf-services/api/documents/upload accepts the file as multipart/form-data and returns a documentId that every subsequent call needs.

import requests

def upload_doc(file_path: str) -> str:
    url = f"{BASE_URL}/pdf-services/api/documents/upload"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
    }
    with open(file_path, "rb") as f:
        files = {"file": (os.path.basename(file_path), f)}
        response = requests.post(url, headers=headers, files=files)
    response.raise_for_status()
    return response.json()["documentId"]

cURL equivalent:

curl -X POST "https://na1.fusion.foxit.com/pdf-services/api/documents/upload" \
  -H "client_id: $CLIENT_ID" \
  -H "client_secret: $CLIENT_SECRET" \
  -F "[email protected]"

Uploaded files carry a 100 MB cap and are automatically deleted after 24 hours. A documentId scopes to the current upload session and expires with the source file, so treat it as ephemeral.

Step 2: Initiate the PDF Conversion

POST /pdf-services/api/documents/create/pdf-from-word accepts a JSON body with the documentId and returns a taskId. The API handles 10 to 10,000+ conversions per day across production pipelines, queuing jobs asynchronously to avoid blocking the connection until the PDF is ready.

import json

def convert_to_pdf(document_id: str) -> str:
    url = f"{BASE_URL}/pdf-services/api/documents/create/pdf-from-word"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
        "Content-Type": "application/json",
    }
    payload = {"documentId": document_id}
    response = requests.post(url, headers=headers, data=json.dumps(payload))
    response.raise_for_status()
    return response.json()["taskId"]

cURL equivalent:

curl -X POST "https://na1.fusion.foxit.com/pdf-services/api/documents/create/pdf-from-word" \
  -H "client_id: $CLIENT_ID" \
  -H "client_secret: $CLIENT_SECRET" \
  -H "Content-Type: application/json" \
  -d '{"documentId": "<your_document_id>"}'

The endpoint returns 202 Accepted, confirming the job is queued. It also accepts .doc, .rtf, .dot, .dotx, .docm, .dotm, and .wpd files through the same documentId input, so legacy Word formats work through the same pipeline.

Step 3: Polling the Task Status

GET /pdf-services/api/tasks/{task-id} returns four fields you need to act on in your polling loop:

status: one of PENDING, IN_PROGRESS, COMPLETED, or FAILED
progress: int32, 0 to 100
resultDocumentId: populated when status reaches COMPLETED
error: populated when status reaches FAILED

The task state machine advances in one direction: PENDING to IN_PROGRESS, then to either COMPLETED or FAILED.

import time

def poll_task(task_id: str, max_attempts: int = 30) -> str:
    url = f"{BASE_URL}/pdf-services/api/tasks/{task_id}"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
    }
    for attempt in range(max_attempts):
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        data = response.json()
        status = data.get("status")
        progress = data.get("progress", 0)
        print(f"Attempt {attempt + 1}: status={status}, progress={progress}%")
        if status == "COMPLETED":
            return data["resultDocumentId"]
        if status == "FAILED":
            raise RuntimeError(f"Conversion failed: {data.get('error')}")
        time.sleep(2)
    raise TimeoutError(f"Task {task_id} did not complete in {max_attempts} attempts")

Two-second polling intervals work across a wide range of document sizes, and polling more aggressively only consumes rate limit budget without affecting conversion time.

Step 4: Downloading the Converted PDF

GET /pdf-services/api/documents/{documentId}/download fetches the finished PDF. The path parameter in the API reference reads {documentId}, but the value you pass here is the resultDocumentId from the completed poll response. The server assigns that ID to the generated PDF output at conversion time, making it the correct identifier to use at this step.

Stream the response to disk with stream=True and iter_content(chunk_size=8192). Buffering a large PDF fully into memory before writing it causes problems on high-volume pipelines.

def download_result(result_document_id: str, output_path: str) -> None:
    url = f"{BASE_URL}/pdf-services/api/documents/{result_document_id}/download"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
    }
    with requests.get(url, headers=headers, stream=True) as response:
        response.raise_for_status()
        with open(output_path, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                if chunk:
                    f.write(chunk)

The cURL equivalent uses the --output flag to write directly to disk:

curl -X GET "https://na1.fusion.foxit.com/pdf-services/api/documents/<result_document_id>/download" \
  -H "client_id: $CLIENT_ID" \
  -H "client_secret: $CLIENT_SECRET" \
  --output output.pdf

To verify the output, check response.headers.get("Content-Type") for application/pdf, or inspect the first four bytes of the written file for the %PDF magic bytes if your pipeline requires format validation.

Error Handling for Production

The Foxit PDF Services API documentation covers 400, 404, 413, and 500 across the four endpoints. The 401 appears on authentication failures as a practical case even though it’s absent from the documented example responses. Each status code points to a specific root cause with a concrete recovery path:

400: malformed request body or unsupported file type. Validate the input file path and extension before calling upload_doc().
401: credential misconfiguration. Verify that CLIENT_ID and CLIENT_SECRET are exported in your shell and that the header names are lowercase client_id and client_secret.
404: the documentId has expired. The server deletes uploaded files after 24 hours, so the convert and download endpoints return 404 for any documentId past that window. Re-upload the source file and restart from the upload step. An expired or unknown taskId on the poll endpoint behaves differently: it returns HTTP 200 with status: "FAILED" and an error object whose message reads "task is not exist". The poll loop’s FAILED branch already catches that case.
413: file exceeds the 100 MB upload cap. Pre-check with os.path.getsize() before uploading, or split the document.
500: transient server error. Apply exponential backoff with a ceiling of 3 retries (wait times of 1s, 2s, and 4s).

def call_with_retry(fn, *args, max_retries: int = 3, **kwargs):
    for attempt in range(max_retries + 1):
        try:
            return fn(*args, **kwargs)
        except requests.HTTPError as e:
            code = e.response.status_code
            if code == 400:
                raise ValueError(
                    "Bad request. Confirm the input is a supported Word format."
                ) from e
            if code == 401:
                raise PermissionError(
                    "Authentication failed. Check CLIENT_ID and CLIENT_SECRET env vars."
                ) from e
            if code == 404:
                raise FileNotFoundError(
                    "Document or task expired (24h TTL). Re-upload and retry."
                ) from e
            if code == 413:
                raise OverflowError(
                    "File too large. The upload cap is 100 MB."
                ) from e
            if code == 500 and attempt < max_retries:
                wait = 2 ** attempt  # 1s, 2s, 4s
                print(f"Server error. Retrying in {wait}s ({attempt + 1}/{max_retries})")
                import time
                time.sleep(wait)
                continue
            raise

Pipeline authors should treat documentId values as ephemeral: each one expires with its source file after 24 hours, so pipeline code that caches documentId values between sessions will see 404s on every convert call, and re-uploading is always the correct recovery path.

The Complete Script

Set your environment variables, then run python convert.py input.docx output.pdf:

import os
import json
import time
import sys
import requests

CLIENT_ID = os.environ.get("CLIENT_ID")
CLIENT_SECRET = os.environ.get("CLIENT_SECRET")
BASE_URL = "https://na1.fusion.foxit.com"


def call_with_retry(fn, *args, max_retries: int = 3, **kwargs):
    for attempt in range(max_retries + 1):
        try:
            return fn(*args, **kwargs)
        except requests.HTTPError as e:
            code = e.response.status_code
            if code == 400:
                raise ValueError(
                    "Bad request. Confirm the input is a supported Word format."
                ) from e
            if code == 401:
                raise PermissionError(
                    "Authentication failed. Check CLIENT_ID and CLIENT_SECRET env vars."
                ) from e
            if code == 404:
                raise FileNotFoundError(
                    "Document or task expired (24h TTL). Re-upload and retry."
                ) from e
            if code == 413:
                raise OverflowError(
                    "File too large. The upload cap is 100 MB."
                ) from e
            if code == 500 and attempt < max_retries:
                wait = 2 ** attempt  # 1s, 2s, 4s
                print(f"Server error. Retrying in {wait}s ({attempt + 1}/{max_retries})")
                time.sleep(wait)
                continue
            raise


def upload_doc(file_path: str) -> str:
    url = f"{BASE_URL}/pdf-services/api/documents/upload"
    headers = {"client_id": CLIENT_ID, "client_secret": CLIENT_SECRET}
    with open(file_path, "rb") as f:
        files = {"file": (os.path.basename(file_path), f)}
        r = requests.post(url, headers=headers, files=files)
    r.raise_for_status()
    return r.json()["documentId"]


def convert_to_pdf(document_id: str) -> str:
    url = f"{BASE_URL}/pdf-services/api/documents/create/pdf-from-word"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
        "Content-Type": "application/json",
    }
    r = requests.post(url, headers=headers, data=json.dumps({"documentId": document_id}))
    r.raise_for_status()
    return r.json()["taskId"]


def poll_task(task_id: str, max_attempts: int = 30) -> str:
    url = f"{BASE_URL}/pdf-services/api/tasks/{task_id}"
    headers = {"client_id": CLIENT_ID, "client_secret": CLIENT_SECRET}
    for attempt in range(max_attempts):
        r = requests.get(url, headers=headers)
        r.raise_for_status()
        data = r.json()
        status = data.get("status")
        print(f"[{attempt + 1}/{max_attempts}] status={status}, progress={data.get('progress', 0)}%")
        if status == "COMPLETED":
            return data["resultDocumentId"]
        if status == "FAILED":
            raise RuntimeError(f"Conversion failed: {data.get('error')}")
        time.sleep(2)
    raise TimeoutError(f"Task {task_id} did not complete after {max_attempts} attempts")


def download_result(result_document_id: str, output_path: str) -> None:
    url = f"{BASE_URL}/pdf-services/api/documents/{result_document_id}/download"
    headers = {"client_id": CLIENT_ID, "client_secret": CLIENT_SECRET}
    with requests.get(url, headers=headers, stream=True) as r:
        r.raise_for_status()
        with open(output_path, "wb") as f:
            for chunk in r.iter_content(chunk_size=8192):
                if chunk:
                    f.write(chunk)


def convert_docx_to_pdf(input_path: str, output_path: str) -> None:
    print(f"Uploading {input_path}...")
    document_id = call_with_retry(upload_doc, input_path)
    print(f"Uploaded. documentId={document_id}")

    print("Initiating conversion...")
    task_id = call_with_retry(convert_to_pdf, document_id)
    print(f"Queued. taskId={task_id}")

    print("Polling for completion...")
    result_document_id = call_with_retry(poll_task, task_id)
    print(f"Completed. resultDocumentId={result_document_id}")

    print(f"Downloading to {output_path}...")
    call_with_retry(download_result, result_document_id, output_path)
    print("Done.")


if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: python convert.py <input.docx> <output.pdf>")
        sys.exit(1)
    convert_docx_to_pdf(sys.argv[1], sys.argv[2])

The Foxit PDF Services API also supports merging, compression, linearization, and OCR through additional endpoints. All of them share the same host and header-based auth pattern, so the functions you’ve built here extend naturally as your pipeline grows.

Create your free Foxit developer account and run your first conversion in under five minutes, with no credit card required at signup.

DOCX to PDF API FAQ

Does the Foxit PDF Services API support formats other than .docx?

Yes. The /pdf-services/api/documents/create/pdf-from-word endpoint accepts .doc, .docx, .rtf, .dot, .dotx, .docm, .dotm, and .wpd. The same four-step flow applies for all of them.

How long are uploaded files retained?

Uploaded documents are automatically deleted after 24 hours. Treat documentId values as ephemeral and re-upload whenever you need to convert a file after that window.

What happens if I poll the task endpoint faster than every 2 seconds?

Faster polling consumes rate limit budget without affecting conversion speed. The server determines conversion time based on document complexity and queue load, so polling intervals below 2 seconds add no throughput benefit.

Can I run multiple DOCX-to-PDF conversions in parallel?

Yes. Each upload returns an independent documentId and each conversion returns an independent taskId. Run concurrent conversions by launching multiple threads or async tasks, with each one tracking its own taskId. Python’s concurrent.futures.ThreadPoolExecutor is a straightforward way to manage this.

Where do I get my CLIENT_ID and CLIENT_SECRET?

From the Foxit Developer Portal dashboard, under the default application created at signup. Both values are available immediately after account creation.

Does the API require an OAuth token exchange?

The API authenticates through named request headers. Pass client_id and client_secret directly on every request, and the server reads those credentials on each call.

Document Generation Explained: How the Template-to-API Pipeline Actually Works

Manual document workflows break down fast as volume grows. This guide explains what document generation is, how template-driven APIs replace manual processes, and what the pipeline looks like from a Word template and JSON payload to a finished PDF.

You’ve inherited a document workflow built on Word macros, save-as duplication, and a shared drive folder someone named “FINAL_v3.” Every time a contract needs to go out, someone opens the master template, manually replaces the client name and date, exports to PDF, and emails it. Scaled to one deal a week, that works. Scaled to a thousand deals a quarter, it breaks down in ways that are hard to trace and painful to fix.

Document generation APIs make the relationship between input data and output document deterministic. This article covers how that pipeline is structured, what the token contract between template and data looks like, and what the actual API call looks like from a POST request to a decoded file.

What Document Generation Is

Document generation is the programmatic production of populated, formatted documents from a template and a structured data source. Three components are always present: a template (the structure and placeholders), a data payload (the values to inject), and a rendering engine (the API that merges the two and produces the final file).

These components map cleanly onto a separation of concerns. The template owner controls layout, language, and branding. The data owner controls what goes in each field. The rendering engine enforces the merge contract between them. When that separation holds, changing the template doesn’t require a code change, and changing the data schema requires only a template update.

Document generation occupies a distinct category from the adjacent tools that crowd the same search space. E-signature platforms collect binding signatures on completed documents. Document management systems store, version, and retrieve files. OCR and extraction tools pull structured data out of existing documents. Document generation puts data in.

Why Manual Document Workflows Break Under Load

Manual Word-based workflows fail in three predictable ways when volume grows.

Version drift happens when templates live in shared folders across teams. One team updates the disclaimer text, another adds a new liability clause, and a third is still using a version from Q3. After six months, your organization has five variants of the same contract template producing inconsistent output, with no reliable way to identify which version generated any given document.

Merge errors compound at scale. Copy-paste and mail-merge workflows both require human coordination of field-by-field substitution. When an invoice ships with the previous client’s name, or a renewal letter shows last year’s rates, the error traces back to a single manual step that had no validation layer. A 0.5% error rate is invisible at 20 documents a month. At 4,000 documents a month, it means 20 wrong documents going out the door.

Audit trail absence creates compliance exposure. A manually assembled document carries no machine-readable record of what data produced it or when. When a regulator asks which policy documents were generated from the October 2024 rate table, the answer requires a manual search through email threads and file name timestamps.

Programmatic generation makes each of these problems tractable. The same JSON payload processed against the same template version always produces the same output, and every generation event is a logged API call with a traceable input and output. The document generation software market is valued at $4.05B in 2025 and projected to grow at a 9.2% CAGR through 2035, driven by enterprise automation and compliance requirements that manual workflows can’t satisfy at scale.

How Template-Driven Generation Works: Tokens, Loops, and Conditionals

Foxit’s DocGen API uses standard Microsoft Word as the template authoring environment. Template authors work in Word, inserting double-bracket placeholders whose key names map directly to keys in the JSON payload supplied at generation time. This keeps template ownership with the people who understand the document content, with no proprietary editor and no additional software license required.

The basic token syntax covers three common cases:

{{ companyName }} renders the string value of companyName from the payload
{{ invoiceDate \@ MM/dd/yyyy }} applies a Word date picture string to the raw date value (the leading \@ is required; the friendly form without it renders blank)
{{ totalDue \# "$#,##0.00" }} formats a numeric value as currency using a Word numeric picture string (a friendly keyword like \# Currency is unsupported and renders blank)

A minimal JSON payload for a template containing those three tokens would be:

{
  "companyName": "Meridian Analytics",
  "invoiceDate": "2025-06-15",
  "totalDue": 8250.0
}

The rendering engine walks the template, matches each token to the corresponding key in the payload, and writes the formatted value into the output. Template authors work entirely in Word, while the engine handles token resolution, format application, and output assembly.

Repeating sections use loop delimiters to produce table rows that repeat for each element in a JSON array. Placing {{TableStart:lineItems}} before a table row and {{TableEnd:lineItems}} after it tells the engine to emit one row per object in the lineItems array. Both delimiters must sit in cells of the same Word table row. Inside that loop, {{ROW_NUMBER}} auto-increments across rows, and a footer row immediately below the loop can use {{=SUM(ABOVE) \# "$#,##0.00"}} to compute and format a column total, so a ten-line invoice produces a correctly numbered, fully summed table with no post-processing.

Conditional content uses Word’s native Field Code View (opened with ALT + F9) to write IF-field conditions that show or hide text blocks based on data values. A clause that should appear only when contractType equals "enterprise" lives inside a field condition, and the rendering engine evaluates it at generation time. There’s no separate scripting layer and no custom expression language to learn.

The three components converge at a single API endpoint: your base64-encoded DOCX template and JSON data payload go in together, and the generated document comes back in the same HTTP response.

The API Pipeline: From POST Request to Final Document

The Foxit DocGen API compresses the generation pipeline into a single synchronous call. You POST to one endpoint with your template and data, and you receive the generated document in the same HTTP response, with no separate template upload step, no job ID to poll, and no webhook to configure for individual document generation.

Before building a generation workflow, you can use the Analyze Document API to scan a DOCX template and return a list of all embedded tokens, which confirms the token-to-key mapping before you commit to a data schema. That’s a single POST to a separate endpoint on the same host, and it returns a structured list of placeholder names and their types.

For the generation call itself, the dev-tier endpoint is:

POST https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64

Authentication passes your client_id and client_secret as custom HTTP headers alongside Content-Type: application/json. You retrieve both credentials from the dashboard at account.foxit.com/site/sign-up after activating your free developer plan, with no OAuth exchange and no session setup required before your first call.

The request body takes three fields:

base64FileString is your DOCX template, base64-encoded. Keep the source .docx under 4 MB (the practical ceiling for a single request) since base64 encoding inflates the payload by roughly 33%. If a template runs large, embedded images are usually the cause, so compress them through Word’s Picture Format settings before exporting.
documentValues is the JSON object whose keys map to token names in the template
outputFormat is the string "pdf" or "docx", lowercase and exact (the API returns HTTP 500 for any other value, including "PDF" or "DOCX")

For an invoice template with a lineItems array, the full curl command carries your base64-encoded DOCX, the matching data object, and your credentials in the request headers:

curl -X POST "https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64" \
  -H "client_id: YOUR_CLIENT_ID" \
  -H "client_secret: YOUR_CLIENT_SECRET" \
  -H "Content-Type: application/json" \
  -d '{
    "base64FileString": "BASE64_ENCODED_DOCX_HERE",
    "documentValues": {
      "companyName": "Meridian Analytics",
      "invoiceDate": "2025-06-15",
      "totalDue": 8250.00,
      "lineItems": [
        { "description": "Platform License", "quantity": 1, "unitPrice": 8250.00 }
      ]
    },
    "outputFormat": "pdf"
  }'

The API returns a synchronous JSON response carrying a human-readable message, the fileExtension for naming the output file, and the base64FileString containing the generated document:

{
  "message": "PDF Document Generated Successfully",
  "fileExtension": "pdf",
  "base64FileString": "JVBERi0xLjQK..."
}

message gives you a status string for logging. fileExtension tells you whether you received "pdf" or "docx", which lets you construct the output filename programmatically without parsing the message string. base64FileString is the generated document. Your application decodes that value and routes the resulting bytes to storage, email delivery, a document management system, or whatever downstream step your workflow requires.

For teams evaluating the API before writing integration code, the Foxit developer portal includes a Postman collection with GenerateDocumentBase64 preconfigured. You can load the collection, paste your credentials and a test template, send the request, and confirm the response structure before writing a single line of application code. Foxit also provides SDKs for Node.js, Python, Java, C#, and PHP if you prefer a language-native integration path over raw HTTP.

Where Organizations Are Actually Using This

Five industries where the template-to-API pipeline produces direct operational value:

Insurance carriers face renewal cycles that generate thousands of policy documents each quarter. They pull policyholder data from their CRM, pass it as a JSON payload to the generation API, and produce populated renewal packets without manual assembly. Each document reflects current policy terms from the system of record, cutting the per-document preparation time from minutes of human work to milliseconds of API latency.

Healthcare providers need patient intake packets and HIPAA disclosure forms ready before each appointment. Clinics pull patient demographic and consent data from their EHR system, generate the packet at appointment scheduling time, and deliver it to the patient portal. The data source is the live EHR record, so the document always reflects current information.

Government agencies and courts produce case-related documents (orders, notices, motions) with fixed structure and variable case data. API-based generation means each document draws directly from structured court records, reducing transcription errors and producing a machine-readable audit trail that links every output document to the specific data that created it.

High-tech and SaaS companies trigger NDA and quote generation directly from their CRM or CPQ tools. A deal record in Salesforce or HubSpot becomes the JSON payload, and the generation API produces a finalized, formatted document without a manual drafting step. The document lands in the deal room minutes after the pricing conversation closes.

Education institutions generate hundreds or thousands of admission offer letters during enrollment periods. Student data from the Student Information System becomes the payload, and each letter reflects the correct program, scholarship amount, and enrollment deadline for that individual student. What a staff member would take days to produce manually runs as a scheduled batch job.

What to Evaluate When Choosing a Document Generation API

Output format coverage deserves attention before you commit to an API. Receiving both PDF and DOCX from the same endpoint matters when your workflow requires human review before a document is finalized. PDF suits direct delivery. DOCX suits draft-and-review cycles where a lawyer or editor needs to modify the generated document before it goes to signature. APIs that produce only PDF force you to finalize at generation time, which eliminates the review step entirely.

The execution model determines whether an API fits your latency requirements. Synchronous APIs return the document in the same HTTP response, which works for real-time generation triggered by a user action or a CRM event. Asynchronous APIs accept a job and require polling or a webhook to retrieve the result, which works better for batch jobs processing thousands of documents in a run. Confirm which model the API offers before you design your integration, because retrofitting from synchronous to async (or vice versa) affects how you handle errors, retries, and downstream routing. Foxit’s DocGen API is synchronous, so individual requests resolve in a single HTTP round trip.

Template portability determines your long-term maintenance cost. A template stored as a standard DOCX file is editable by anyone with Word, version-controllable in Git, and portable across environments. A template stored in a proprietary format requires the vendor’s editor for every update, and losing access to that editor means losing the ability to maintain your own document logic. Word-based templates also let business users own content changes without involving a developer.

Compliance posture matters as soon as your documents contain PII, PHI, or financial data. Confirm the provider’s certifications before sending real records through the API. SOC 2, GDPR compliance, and HIPAA certification are the relevant checks for most enterprise document workflows. A provider’s architecture (multi-tenant SaaS vs. single-tenant hosted) also affects how you address data residency requirements.

Developer onboarding cost is a real selection criterion. A free tier with immediate credential access, a working Postman collection, and SDK support for your language stack lets your team validate fit in hours. A procurement process that requires a sales conversation before you can run a test extends your evaluation cycle by weeks. Foxit’s developer plan is free, credit-card-free, and gives you dashboard access and credentials immediately, so your team can make an informed build-vs-buy decision based on a working integration rather than a demo.

Getting Started: Generate Your First Document Today

A working end-to-end generation pipeline takes under an hour to set up:

Go to account.foxit.com/site/sign-up and activate a free Developer plan. Dashboard access and credentials are immediate, with no credit card and no sales call required.
Retrieve your Client ID and Client Secret from the dashboard.
Grab a ready-to-use template, or author your own. Download invoice_simple.docx for the smallest possible smoke test, or invoice_table.docx if you want the full loop, {{ROW_NUMBER}}, and {{=SUM(ABOVE)}} round-trip. To build your own instead, open Microsoft Word, add two or three {{ tokenName }} placeholders, and save as DOCX. Base64-encode the file using base64 -i template.docx on macOS or Linux, or [Convert]::ToBase64String([IO.File]::ReadAllBytes("template.docx")) in Windows PowerShell.
Open the Postman collection linked on the Foxit API page, load the GenerateDocumentBase64 request, paste your base64-encoded template into base64FileString, and add a JSON object to documentValues with keys matching your placeholder names. Set outputFormat to "pdf" and send the request. Copy the base64FileString value from the JSON response and decode it. You now have a generated PDF.

From this point, connecting to a real data source is the only remaining step. Pull a record from your CRM, a row from your database, or a response from an upstream API, map its fields to the token names in your template, and pass the result as documentValues. That connection makes the pipeline production-ready, and every document it generates becomes a deterministic, auditable function of the data that produced it.

Activate your free Foxit Developer plan (no credit card, no sales call) and run your first document generation call in minutes at account.foxit.com/site/sign-up.

Document Generation Pipeline FAQ

What is document generation?

Document generation is the programmatic production of populated, formatted documents from a template plus a structured data source. Three components are always present: a template holding the layout and placeholders, a JSON data payload holding the values, and a rendering engine that merges the two into a final PDF or DOCX. The same payload against the same template always produces the same output, which makes the process deterministic and auditable.

How is document generation different from e-signature or OCR tools?

They occupy adjacent but distinct categories. E-signature platforms like DocuSign collect binding signatures on documents that already exist. Document management systems store, version, and retrieve files. OCR and extraction tools pull structured data out of existing documents. Document generation does the opposite — it puts data in, producing a new populated document from a template. Many workflows chain them: generate a contract, then route it for signature.

What does a Foxit DocGen API request and response look like?

You POST to GenerateDocumentBase64 with three fields: base64FileString (your base64-encoded DOCX template), documentValues (the JSON object whose keys match your tokens), and outputFormat ("pdf" or "docx", lowercase). Authentication uses client_id and client_secret headers. The synchronous response returns a message, a fileExtension, and a base64FileString containing the generated document, which your app decodes and routes downstream.

How do you create repeating table rows in a document generation template?

Use loop delimiters. Place {{TableStart:lineItems}} before a Word table row and {{TableEnd:lineItems}} after it, with both delimiters in cells of the same row. The engine emits one row per object in the lineItems array. Inside the loop, {{ROW_NUMBER}} auto-increments, and a footer row can use {{=SUM(ABOVE) \# "$#,##0.00"}} to compute and format a column total — so a ten-line invoice renders fully numbered and summed with no post-processing.

Can the document generation API return both PDF and DOCX?

Yes. The Foxit DocGen API returns either format from the same endpoint based on the outputFormat value. PDF suits direct delivery where the document is final at generation time. DOCX suits draft-and-review cycles, where a lawyer or editor needs to modify the generated file before it goes to signature. APIs that emit PDF only force you to finalize at generation and eliminate that review step.

PDF Translation with Verifiable Quality: Build a Confidence-Scored Pipeline with Foxit API and Straker.ai

Most machine translation tools hand back a translated PDF with no signal about which parts to trust — a real problem for contracts, medical forms, and regulatory filings. This guide shows how to build a pipeline that scores every segment before the final render, using Foxit for structural extraction and layout-preserving rendering and Straker.ai for translation plus per-segment quality scoring.

Most machine translation tools give you a translated file and nothing else. They do not tell you which parts are correct and which parts are wrong. For a simple blog post, that is fine. For a contract, a medical form, or a legal notice, it is a real problem. A bad translation can sit in the final PDF for days before anyone notices, often only after the document has already been signed or sent.

Teams today are translating more documents, into more languages, and faster than ever. Legal, finance, healthcare, HR, and insurance teams all deal with PDFs where one wrong word can cause a lot of damage: a broken contract, a failed audit, or even a safety issue. Most translation tools were not built to catch these mistakes. They just move text from one language to another. When quality checks happen at all, they usually mean a person reading the final PDF line by line and hoping they spot the errors.

This article shows how to build a better setup. You will learn how to build a PDF translation pipeline that gives every segment a quality score before the final PDF is created. Instead of hoping the translation is right, the pipeline tells you which parts to trust, which parts to review, and which parts to send back to a human translator. All of this happens automatically on every run.

Architecture at a Glance

Before going deeper, it helps to see the full pipeline in one picture. The diagram below traces a source PDF through every stage: extract, translate, score, route, and render. Each box is a single responsibility handled by a single service, with the routing layer acting as the glue you control.

The pipeline has two external services:

Foxit PDF Translation API handles anything PDF-specific. It pulls the structured text out of the source document with element IDs attached, then renders the final PDF back in the original layout (multi-column text, tables, font substitution, image positions) using the approved translations.
Straker AI translates each source segment AND scores the translation in the same request. It returns the target text, a numeric score on a 0.0 to 1.0 scale, and a categorical label (best, good, acceptable, bad) for every element ID. This step is pluggable, so you can swap Straker for DeepL, Google Cloud Translation, AWS Translate, or an in-house NMT if you already have a contract with one of them. The contract between this step and the rest of the pipeline is a flat dict of element IDs to translated text plus per-segment scores.

and one piece of code you own:

Routing layer is your business logic. It reads the score, decides whether the segment auto-accepts, flags for human review, or escalates to a translator, and then hands the approved set to Foxit’s render call.

With the shape of the pipeline on the table, the rest of the article works through each piece in order, starting with why per-segment quality scoring is worth the integration effort in the first place.

The Quality Gap

You ship a translated PDF to a legal team. Three days later, compliance flags a clause in the German version. The term “indemnification” was rendered as “Entschädigung” (compensation) rather than “Freistellung” (hold harmless). Your MT pipeline returned a 200 status. Nobody’s alerting on that delta.

Raw machine translation output carries no quality signal by default. Every segment comes back translated, and your pipeline treats them identically regardless of whether the model was confident or guessing. For marketing copy that’s an acceptable tradeoff, but for a loan covenant, a clinical trial protocol, or a regulatory filing, a 95%-accurate translation can still be contractually or legally dangerous because the 5% failure may concentrate precisely in the high-stakes clauses.

A confidence score, in the translation QA context, is a per-segment numeric signal from a verification engine. It tells you how reliable each translated unit is on a scale your system can act on programmatically. High-confidence segments auto-accept, medium-confidence ones queue for post-edit review, and low-confidence segments escalate directly to a human translator before they ever reach the final document.

The compound problem for PDFs specifically is that most translation pipelines strip document structure before the MT engine even sees the text. The extraction step flattens multi-column layouts, collapses table cells, and drops font metadata. By the time you get a translated output, you’ve lost both layout fidelity and any quality signal. The rendered PDF looks wrong and you have no programmatic way to know which segments caused it.

Foxit’s PDF Translation Trial API extracts structured text from a source PDF with element IDs preserved, so the layout blueprint travels alongside the text through the entire workflow. You hand the source segments to Straker AI, which returns the translated text plus a per-segment numeric score and a quality label in a single call. (If you already run DeepL, Google Cloud Translation, AWS Translate, or an in-house NMT Engine, you can drop it in at this step without changing the rest of the pipeline.) Your routing logic decides which segments pass, which get flagged, and which escalate to human review. Foxit’s render endpoint then re-assembles the PDF in the original layout using the accepted translations, giving you a layout-preserved translated PDF with a documentable quality trail attached to every segment.

How the Pipeline Works

Foxit and Straker are two independent APIs that you wire together. Foxit owns PDF structure, extracting structured text keyed by element ID and re-rendering the final PDF in the original layout. Straker AI handles translation and per-segment quality scoring in a single request, returning the translated text alongside a numeric score and a quality label. You own the routing decision that sits between the scores and the render call.

The pipeline runs in seven steps:

Foxit covers steps 1-3 and 6-7 (PDF structure and rendering). Straker AI covers step 4, producing translations and per-segment quality scores in one round-trip. Step 5 is your business logic.

The Foxit PDF Translation API defines steps 2, 3, and 6. The upload and download calls use the general PDF Services endpoints. Straker AI is a separate API at https://api-verify.straker.ai. You submit XLF 1.2 files containing source segments and Straker returns the translated target_text per segment plus a numeric score (0.0 to 1.0) and a quality label (best, good, acceptable, bad). Because Foxit’s ExtractedText.json is a flat { "elementId": "text" } map, and XLF trans-unit IDs round-trip through Straker’s external_id field unchanged, the element IDs Foxit emits are the same IDs that come back with translations and scores attached. That alignment is what makes programmatic routing possible.

One clarification for readers who’ve seen the Foxit-Straker partnership announcement: that partnership covers Foxit eSignature Services, enabling end users to translate and sign documents in the eSign product. That’s an end-user feature. The PDF Translation Trial API used here is a separate developer surface. Its OpenAPI spec (v2.2.0) contains zero Straker references, and the preprocess-pdf documentation explicitly instructs developers to “translate the text in ExtractedText.json using your preferred translation tool.” You wire the two APIs together manually. This tutorial uses Straker AI as the default translation engine because it produces translations and quality scores in the same call, but you can substitute DeepL, Google Cloud Translation, AWS Translate, or your own NMT at step 4 without changing the Foxit calls.

Credentials and Setup

Get your Foxit credentials at app.developer-api.foxit.com/pricing. The free Developer plan gives you 20 AI credits per month with no credit card and no sales call required. Once you’ve signed in, your Client ID and Client Secret appear in the developer dashboard. Every Foxit API call requires both in the request headers as client_id and client_secret (lowercase snake_case). Export them in your shell as FOXIT_CLIENT_ID and FOXIT_CLIENT_SECRET so the code below reads them from the environment rather than hard-coding secrets.

For Straker, sign up at straker.ai/ai-platform/verify for API access. Straker issues a UUID-style API token that you send as a bearer token on every call (Authorization: Bearer <your-token>). The API lives at https://api-verify.straker.ai and its full reference is published at api-verify.straker.ai/docs. Export your token as STRAKER_API_KEY for the code below. You can confirm the token works and check your balance with a quick GET /user/balance. Both services offer trial access, so you can build and test the full pipeline before any procurement conversation.

Before you finalize your language matrix, check both APIs for supported languages. Foxit’s render endpoint accepts 23 target language codes (en, zh, zh_tw, fr, de, es, it, pt, nl, ja, ko, th, vi, hi, ru, ar, tr, pl, sv, no, nb, da, and fi). Straker AI identifies languages by UUID rather than ISO code. You fetch the full list with GET /languages and look up the UUID for your target (for example, 917FF728-0725-A033-1278-33025F49CA40 is French (France), 917FF7D8-9107-0BF8-97EE-065C20F453DE is German). The intersection of the two sets determines your production language coverage.

If you already have a contract with DeepL, Google Cloud Translation, AWS Translate, or an in-house NMT service, you can swap that engine in at step 4. The pipeline contract upstream (Foxit element IDs mapped to source strings) and downstream (a dict of {element_id: {score, quality, target_text}} feeding the router) does not change. The code below uses Straker AI by default because the same API returns the translation and the quality signal in one call.

Building the PDF Translation Pipeline

The complete seven-step pipeline runs in Python using requests, json, zipfile, os, and the standard-library xml.etree.ElementTree for building XLF. The first snippet covers Foxit steps 1-3 (upload, structural extraction, and preprocessing).

import requests
import json
import zipfile
import io
import time

FOXIT_BASE = "https://na1.fusion.foxit.com/pdf-services/api"
HEADERS = {
    "client_id": "YOUR_CLIENT_ID",
    "client_secret": "YOUR_CLIENT_SECRET"
}

def poll_task(task_id: str) -> dict:
    """Poll GET /tasks/{task_id} until COMPLETED or FAILED."""
    while True:
        r = requests.get(f"{FOXIT_BASE}/tasks/{task_id}", headers=HEADERS)
        r.raise_for_status()
        data = r.json()
        status = data.get("status")
        if status == "COMPLETED":
            return data
        if status == "FAILED":
            raise RuntimeError(f"Task {task_id} failed: {data.get('error')}")
        # PENDING or IN_PROGRESS: wait and retry
        time.sleep(3)

# Step 1: Upload source PDF
with open("source.pdf", "rb") as f:
    upload_resp = requests.post(
        f"{FOXIT_BASE}/documents/upload",
        headers=HEADERS,
        files={"file": ("source.pdf", f, "application/pdf")}
    )
upload_resp.raise_for_status()
source_document_id = upload_resp.json()["documentId"]

# Step 2: Structural Extract (async - must complete before preprocess)
extract_resp = requests.post(
    f"{FOXIT_BASE}/documents/pdf-structural-extract",
    headers=HEADERS,
    json={"documentId": source_document_id}
)
extract_resp.raise_for_status()  # 202 Accepted
extract_task_id = extract_resp.json()["taskId"]

extract_result = poll_task(extract_task_id)
extracted_doc_id = extract_result["resultDocumentId"]

# Step 3: Preprocess (synchronous - returns 200, no polling needed)
preprocess_resp = requests.post(
    f"{FOXIT_BASE}/documents/translation/preprocess-pdf",
    headers=HEADERS,
    json={"documentId": extracted_doc_id}
)

# Errors from preprocess-pdf per the Foxit spec:
#   400 VALIDATION_ERROR      - "Document ID is required"
#   500 INTERNAL_SERVER_ERROR - "Failed to preprocess document"
preprocess_resp.raise_for_status()
preprocess_result_id = preprocess_resp.json()["resultDocumentId"]

# Download the ZIP containing ExtractedText.json and StructureInfo.json
zip_resp = requests.get(
    f"{FOXIT_BASE}/documents/{preprocess_result_id}/download",
    headers=HEADERS
)
zip_resp.raise_for_status()

with zipfile.ZipFile(io.BytesIO(zip_resp.content)) as zf:
    extracted_text = json.loads(zf.read("ExtractedText.json"))
    # StructureInfo.json: do not modify - the render step requires it untouched
    # structure_info = json.loads(zf.read("StructureInfo.json"))

# extracted_text is now {"elementId1": "original text", "elementId2": "original text", ...}

The preprocess step is synchronous, which means you get a 200 OK directly with the resultDocumentId. No polling required. The ZIP it produces contains two files: ExtractedText.json maps every element ID to its original text, and StructureInfo.json carries the full layout blueprint (bounding boxes, font metadata, column positions). You pass StructureInfo.json to the render step unmodified. Modifying it breaks the render because it’s the mechanism that makes layout preservation possible.

The second snippet covers steps 4-7, calling Straker AI to translate and score every segment in one round-trip, routing by score, rendering the translated PDF, and downloading the result. Straker’s AI Translation and Quality Evaluation workflow accepts a source-only XLF and returns a translated target_text per segment alongside the numeric score and the quality label, so the same response feeds both the translation choice and the routing decision.

import xml.etree.ElementTree as ET

STRAKER_BASE = "https://api-verify.straker.ai"
STRAKER_TOKEN = "STRAKER_API_KEY"
STRAKER_HEADERS = {"Authorization": f"Bearer {STRAKER_TOKEN}"}

# Straker identifies languages by UUID. Look these up once via GET /languages
# and cache them. Full list: https://api-verify.straker.ai/languages
STRAKER_LANG_FRENCH = "917FF728-0725-A033-1278-33025F49CA40"
STRAKER_LANG_GERMAN = "917FF7D8-9107-0BF8-97EE-065C20F453DE"

# Workflow UUID for "AI Translation and Quality Evaluation". Fetch the full
# list of workflows once via GET /workflow and cache the UUID for the one you
# want; this workflow produces both the translation and the per-segment score.
STRAKER_WORKFLOW_AI_TRANSLATE_AND_EVAL = "390b47a9-d5dc-46ae-92e2-56c43d128c44"


def build_xlf_1_2_source_only(source_lang: str, target_lang: str,
                              sources: dict) -> bytes:
    """
    Build a minimal XLF 1.2 document with source segments and empty targets.
    trans-unit/@id preserves Foxit's element IDs; Straker surfaces the same
    value as `external_id` on the segments it returns, so the keys round-trip.
    """
    ns = "urn:oasis:names:tc:xliff:document:1.2"
    ET.register_namespace("", ns)
    xliff = ET.Element(f"{{{ns}}}xliff", {"version": "1.2"})
    file_el = ET.SubElement(xliff, f"{{{ns}}}file", {
        "source-language": source_lang,
        "target-language": target_lang,
        "datatype": "plaintext",
        "original": "foxit-extract",
    })
    body = ET.SubElement(file_el, f"{{{ns}}}body")
    for element_id, source_text in sources.items():
        unit = ET.SubElement(body, f"{{{ns}}}trans-unit", {"id": element_id})
        ET.SubElement(unit, f"{{{ns}}}source").text = source_text
        ET.SubElement(unit, f"{{{ns}}}target")  # empty - Straker fills it in
    return b'<?xml version="1.0" encoding="UTF-8"?>\n' + ET.tostring(xliff, encoding="utf-8")


# Step 4: Translate and score every segment with Straker AI in one call.
def translate_and_score_with_straker(sources: dict, source_lang_code: str,
                                     target_lang_uuid: str) -> dict:
    """
    Submit source-only XLF to Straker's AI Translation + Quality Evaluation
    workflow. Returns a dict keyed by Foxit element ID ->
    {"score": float|None, "quality": str, "target_text": str}.
    """
    xlf_bytes = build_xlf_1_2_source_only(source_lang_code, "fr", sources)

    # 4a. Create the project on the AI Translation + Quality Evaluation
    # workflow. confirmation_required=false commits the token cost
    # immediately; set to true to review cost and call POST /project/confirm
    # before processing begins.
    create_resp = requests.post(
        f"{STRAKER_BASE}/project",
        headers=STRAKER_HEADERS,
        files={"files": ("segments.xlf", xlf_bytes, "application/xliff+xml")},
        data={
            "languages": target_lang_uuid,
            "title": "Foxit PDF translation batch",
            "workflow_id": STRAKER_WORKFLOW_AI_TRANSLATE_AND_EVAL,
            "confirmation_required": "false",
        },
    )
    create_resp.raise_for_status()
    project_id = create_resp.json()["project_id"]

    # 4b. Poll the project until it reports COMPLETED.
    while True:
        status_resp = requests.get(
            f"{STRAKER_BASE}/project/{project_id}", headers=STRAKER_HEADERS
        )
        status_resp.raise_for_status()
        project = status_resp.json()["data"]
        if project["status"] == "COMPLETED":
            break
        if project["status"] in ("FAILED", "PROCESSING_FAILED", "CANCELED"):
            raise RuntimeError(f"Straker project {project_id} failed")
        time.sleep(3)

    # 4c. Fetch the per-segment translations + scores. file_uuid is returned
    # in the project payload.
    file_uuid = project["source_files"][0]["file_uuid"]
    seg_resp = requests.get(
        f"{STRAKER_BASE}/project/{project_id}/segments/{file_uuid}/{target_lang_uuid}",
        headers=STRAKER_HEADERS,
    )
    seg_resp.raise_for_status()

    results = {}
    for seg in seg_resp.json()["segments"]:
        element_id = seg["external_id"]  # matches the Foxit key we packed into XLF
        t = seg["translation"]
        results[element_id] = {
            "score": t["score"],          # float 0.0 to 1.0, or None
            "quality": t["quality"],      # "best" | "good" | "acceptable" | "bad"
            "target_text": t["target_text"],  # Straker's translation
        }
    return results

scored = translate_and_score_with_straker(
    extracted_text,
    source_lang_code="en",
    target_lang_uuid=STRAKER_LANG_FRENCH,
)

# Step 5: Route by score and quality label (developer-controlled business logic).
HIGH_THRESHOLD = 0.85
LOW_THRESHOLD = 0.65

accepted = {}
flagged_for_review = {}
rejected = {}

for element_id, verdict in scored.items():
    score = verdict["score"] or 0.0
    if verdict["quality"] == "best" or score >= HIGH_THRESHOLD:
        accepted[element_id] = verdict["target_text"]
    elif verdict["quality"] == "bad" or score < LOW_THRESHOLD:
        rejected[element_id] = {"original": extracted_text[element_id],
                                 "score": score, "quality": verdict["quality"]}
    else:
        flagged_for_review[element_id] = {"translation": verdict["target_text"],
                                           "score": score, "quality": verdict["quality"]}

# Build the render payload. Foxit's render expects every key from the original
# ExtractedText.json. Accepted segments use the scored translation; flagged and
# rejected segments fall back to the original source text so the layout is not
# broken by missing keys. In production, replace the fallback with human-
# reviewed text once it is available, or hold the render step until review
# completes.
render_payload = {}
for element_id, original_text in extracted_text.items():
    if element_id in accepted:
        render_payload[element_id] = accepted[element_id]
    else:
        render_payload[element_id] = original_text

# Step 6: Render (async)
# translatedFile is the modified ExtractedText.json with translated values, same keys
translated_json_bytes = json.dumps(render_payload).encode("utf-8")

render_resp = requests.post(
    f"{FOXIT_BASE}/documents/translation/render-pdf",
    headers=HEADERS,
    data={
        "sourceDocumentId": source_document_id,
        "preprocessResultDocumentId": preprocess_result_id,
        "targetLanguage": "fr"
        # Optional: "pageRangeStart": 1, "pageRangeEnd": 10
    },
    files={"translatedFile": ("ExtractedText.json", translated_json_bytes, "application/json")}
)

# Errors from render-pdf per the Foxit spec:
#   400 VALIDATION_ERROR    - "Either translatedFile or translatedTextDocumentId must be provided"
#   400 VALIDATION_ERROR    - "Unsupported target language: xx"
#   500 RENDER_START_FAILED - "Failed to start render: service unavailable"
render_resp.raise_for_status()
render_task_id = render_resp.json()["taskId"]

render_result = poll_task(render_task_id)
output_doc_id = render_result["resultDocumentId"]

# Step 7: Download translated PDF
pdf_resp = requests.get(
    f"{FOXIT_BASE}/documents/{output_doc_id}/download",
    headers=HEADERS
)
pdf_resp.raise_for_status()
with open("translated_output.pdf", "wb") as f:
    f.write(pdf_resp.content)

print(f"Done. Accepted: {len(accepted)}, Flagged: {len(flagged_for_review)}, Rejected: {len(rejected)}")

The render call is multipart/form-data. You pass sourceDocumentId (the original PDF’s document ID from step 1), preprocessResultDocumentId (from step 3), targetLanguage (one of the 23 supported codes), and translatedFile (the modified ExtractedText.json with translated values and original keys). The alternative is uploading the translated JSON first via the upload endpoint and passing its ID as translatedTextDocumentId instead. At least one of the two must be present, or you’ll get a 400 VALIDATION_ERROR.

The render operation is asynchronous. It returns 202 Accepted immediately with a taskId, and the actual rendering runs in the background on Foxit’s side. You must poll GET /tasks/{taskId} on a fixed interval, every 3 seconds is the recommended cadence, until the status flips to COMPLETED before you try to download the output. Skipping the poll, or treating the initial 202 response as if it were a finished render, will cause the program to crash and interrupt the rest of the pipeline because the result document is not yet written when the task is still IN_PROGRESS. The poll_task helper from the first snippet already implements this loop with a 3-second time.sleep between checks and surfaces a FAILED status as a RuntimeError, so reuse it here rather than reading render_resp.json() directly. The same polling discipline applies to the structural extract step (step 2), which is also asynchronous.

Scoring and Routing

Straker AI generates both the translation and the quality signal in this pipeline. Foxit’s responses carry document IDs and task statuses; the translation choice and the per-segment score are entirely Straker’s contribution.

Each segment in the /project/{id}/segments/{file_id}/{language_id} response carries three values you care about. target_text is Straker’s translation. score is a float between 0.0 and 1.0 (it may be null for segments where the model has no confidence signal). quality is a categorical label Straker assigns alongside the numeric score (best, good, acceptable, or bad). You can route on either signal, or combine them. The table below shows a combined policy calibrated for compliance-sensitive documents. These are starting points; your production system should calibrate per language pair and domain, since a French legal contract demands different thresholds than a Spanish marketing brochure.

Straker verdict	Action	Rationale
`quality == "best"` or `score >= 0.85`	Auto-accept, include in render	High confidence output; suitable for fully automated workflows
`quality in ("good", "acceptable")` or `0.65 - 0.84`	Flag segment by element ID for post-edit review	Medium confidence; a human reviewer checks the flagged segments before the final render runs
`quality == "bad"` or `score < 0.65`	Reject segment, escalate to human translator	Low confidence output; the model is unreliable for this segment

The element ID key structure matters here. Foxit’s ExtractedText.json keys are packed into XLF trans-unit IDs, and Straker surfaces the same value in its response’s external_id field. That means every entry in your flagged_for_review dictionary carries enough information for a reviewer to open the source document, find the exact element by ID, and return an approved translation. You write the approved translation back into the same key, then trigger the render step. This produces a documentable audit trail. For every element ID in the output PDF, you can show the original text, Straker’s translation, the Straker score and quality label, and whether a human approved it. In regulated industries (finance, legal, healthcare), that’s the evidence your compliance team needs to sign off on an automated localization workflow, and it aligns with ISO 18587, the international standard for post-editing of machine translation output.

Straker AI can also route low-confidence output to expert reviewers automatically when configured through the Straker platform. Check straker.ai/ai-platform/verify for the workflow configuration options.

Layout Preservation

Foxit’s render step preserves multi-column text flow, embedded table cell structure, images at their original positions, headers and footers, and font substitution for target-language character sets. That means CJK scripts (Japanese, Chinese, Korean) render correctly with appropriate glyph substitution, and Arabic output renders right-to-left without manual post-processing.

StructureInfo.json is what makes this possible. When the preprocess step runs, it produces both the text map (which you hand to Straker) and the layout blueprint (which you hand back to Foxit unmodified at render time). The render engine maps translated text back to the original element positions using this blueprint, reflowing text within the same bounding boxes. Because the structure data travels alongside the text through the entire pipeline, Foxit never needs to reconstruct the layout from scratch.

Generic MT pipelines export raw text, losing all spatial relationships, translate it, then attempt to rebuild the PDF from nothing. Tables merge into continuous text, columns collapse to a single flow, and CJK font substitution fails because the rebuilding step has no record of what fonts were originally in use.

Limitations to Test

Text expansion is the first limitation worth stress-testing. English to German translation typically increases text length by 20-35%, and English to Arabic can run even longer. Foxit’s render engine handles reflow within bounding boxes, but extreme length changes in tight table cells or narrow columns may overflow. Test with your actual document types before you commit to a production deployment.

Complex layout edge cases are the second limitation. Overlapping text boxes, embedded SVG charts with text labels, and PDFs with non-standard encoding may produce imperfect renders. The structural extraction step covers standard PDF text elements well, but edge-case layouts require manual review of the rendered output before you sign off on the pipeline for a given document class.

Try It Now

Sign up for Foxit’s free Developer plan and a Straker AI account, grab credentials for both, and run the pipeline from the section above against a real document. An invoice, a multi-page contract, or a regulatory filing works well for testing because each has tables, mixed-column layouts, and high-stakes text segments.

After the render completes, verify four things in the output PDF:

Tables retain cell structure
Multi-column text flows correctly in the target language
Images remain in their original positions
Fonts render correctly for the target script

Cross-reference the confidence scores from Straker against the rendered segments to calibrate your production thresholds. You may find that legal terminology in German warrants a 0.90 auto-accept threshold while product description text in French is fine at 0.80.

The complete Foxit Translation Trial API reference covers the full parameter list and response schema for preprocess-pdf and render-pdf. The Foxit Structural Extraction Trial API reference documents the structural extract endpoint. Straker’s translation and scoring API documentation lives at straker.ai/ai-platform/verify.

Looking ahead, Straker’s dashboard lists a native Foxit integration as Coming Soon (no release date announced at the time of writing), described as a workflow to translate PDF contracts with Foxit, verify them with experts, and finalize them for signing. When it ships, it’s likely to compress several of the manual steps above into a single call. The underlying mechanics (structural extract, translation, per-segment scoring, routing, render) will remain the same logical stages, so the pipeline you build today stays a useful mental model for reasoning about the native version when it arrives.

For production-scale implementation patterns and how Straker’s translation and verification layer integrates into enterprise localization pipelines, register for the upcoming joint Foxit + Straker.ai webinar with Lee Konstanty from Straker. Get your Foxit API credentials | Get started with Straker AI

PDF Translation API FAQ

What is a PDF translation API with confidence scoring?

A PDF translation API with confidence scoring is a service that translates PDF documents and returns a per-segment quality signal alongside each translation. Instead of handing back a single translated file, the API tells you which segments are high-confidence (safe to auto-accept), which are medium-confidence (queue for human review), and which are low-confidence (escalate to a translator). This pipeline combines Foxit’s PDF Translation Trial API for structural extraction and layout-preserving rendering with Straker.ai for translation and scoring in a single call.

How does the Foxit and Straker.ai PDF translation pipeline work?

The pipeline runs in seven steps: upload the source PDF to Foxit, run structural extraction to get element-ID-keyed text, preprocess to produce ExtractedText.json and StructureInfo.json, send segments to Straker AI’s “AI Translation and Quality Evaluation” workflow which returns translated text plus a 0.0–1.0 score and a quality label, route each segment programmatically by score, then call Foxit’s render endpoint to rebuild the PDF in the original layout. Foxit owns PDF structure, Straker owns translation and scoring, and your code owns the routing decision.

Why do PDFs need per-segment translation quality scores?

For marketing copy, raw machine translation output is usually fine. For contracts, medical forms, clinical trial protocols, or regulatory filings, a 95%-accurate translation can still be legally dangerous because the 5% failure may land on a high-stakes clause — like “indemnification” rendered as “Entschädigung” (compensation) instead of “Freistellung” (hold harmless). Per-segment confidence scores let you route low-confidence segments to human reviewers before they reach the final document, producing the audit trail compliance teams need under standards like ISO 18587.

Can I use DeepL, Google Translate, or AWS Translate instead of Straker.ai?

Yes. The translation step is pluggable. The contract upstream — Foxit element IDs mapped to source strings — and downstream — a dict of element IDs to translated text feeding the render call — does not change if you swap the engine. DeepL, Google Cloud Translation, AWS Translate, or an in-house NMT engine all work. The trade-off is that Straker AI returns translation plus quality score in one call, while other engines require a separate verification step if you want confidence signals.

How does Foxit preserve PDF layout during translation?

Foxit’s preprocess step produces two files: ExtractedText.json with element-ID-keyed text, and StructureInfo.json with the full layout blueprint (bounding boxes, font metadata, column positions, image locations). You modify only ExtractedText.json with translations and pass StructureInfo.json to the render endpoint untouched. The render engine reflows translated text within the original bounding boxes, handles font substitution for CJK and Arabic scripts, and preserves multi-column layouts, tables, and image positions — without rebuilding the PDF from scratch.

What target languages does the Foxit PDF Translation API support?

Foxit’s render endpoint accepts 23 target language codes: en, zh, zh_tw, fr, de, es, it, pt, nl, ja, ko, th, vi, hi, ru, ar, tr, pl, sv, no, nb, da, and fi. Straker AI identifies languages by UUID rather than ISO code, fetched via GET /languages. Your production language coverage is the intersection of both sets — check both APIs before finalizing your language matrix.

How do I set confidence score thresholds for auto-accept versus human review?

A reasonable starting policy for compliance-sensitive documents: auto-accept segments with quality == “best” or score >= 0.85, flag for post-edit review at 0.65–0.84 or quality in (“good”, “acceptable”), and reject for human translation at score < 0.65 or quality == “bad”. These are starting points — calibrate per language pair and domain. A French legal contract may warrant a 0.90 auto-accept threshold while a Spanish marketing brochure is fine at 0.80. Run the pipeline against a representative sample of your real documents and tune from there.

Extract Anything from Any PDF: Inside Foxit’s Advanced Extraction Engine

Basic PDF extraction libraries break on scanned documents, complex tables, and form fields, leaving downstream pipelines starved of clean data. Foxit’s PDF Structural Extraction API combines OCR, layout recognition, and AI parsing to return all twelve PDF element types as structured JSON, ready for RAG, BI, and CRM workflows.

Your PDF extraction pipeline passes unit tests against the sample invoices you built it on. Then production arrives and you’re looking at 47% garbled output on the Q4 contract batch because half those documents are scanned TIFFs wrapped in a PDF envelope, and your extraction library has no concept of what an image-only page actually is.

The failure modes are specific. PyMuPDF’s get_text() returns empty strings on scanned PDFs because it reads content streams directly, and image-only pages carry no text stream. pdfplumber’s table detection merges rows when column widths span non-uniform grids, which is standard in any financial statement that mixes summary and line-item rows on the same page. Embedded images containing meaningful text (stamped signatures, engineering drawing annotations, letterhead logos) get silently dropped. The library extracts coordinates for the XObject reference but does nothing with the raster data inside. Form fields built on non-standard annotation types (AcroForms using widget annotations with custom action streams) lose their values entirely when you serialize to text.

The architectural distinction that creates this problem is the difference between content serialization and semantic extraction. A PDF converter reads a content stream and writes out whatever character sequences it finds in rendering order. An extraction engine understands the spatial relationships between those character sequences: that two columns of text at x=72 and x=320 are parallel body copy, that the row at y=210 belongs to the table starting at y=180, that the text block repeating on every page is a header carrying lower retrieval weight in a RAG index. Output that lacks spatial and semantic classification looks correct on screen but breaks every downstream consumer that depends on structure.

BI dashboards require numbers tied to the right row labels. AI ingestion pipelines require heading hierarchy to chunk accurately. CRMs require form field values extracted from AcroForm widget dictionaries, delivered with field names intact. The delta between what basic extraction libraries return and what those systems can actually consume is where document pipeline engineering hours accumulate.

How Foxit’s PDF Structural Extraction Engine Works Under the Hood

Foxit exposes this capability as the PDF Structural Extraction (Trial) endpoint inside the PDF Services API (POST /pdf-services/api/documents/pdf-structural-extract). Trial status means the schema is versioned at v1.0.7 and may evolve, but the contract is stable enough to build against today, and the endpoint runs against the production base URL at developer-api.foxit.com.

The engine runs three coordinated layers. The OCR layer operates on rasterized page content, recognizing characters from image-based PDFs and scanned documents across 200+ languages. The layout recognition layer applies spatial analysis to identify column boundaries, reading order, table cell boundaries, figure regions, and header/footer zones. The AI-based parsing layer classifies extracted objects semantically, resolving ambiguous blocks (a text run that spans two layout columns, or a figure caption that reads syntactically like a section heading) into typed elements.

All three layers run inside Foxit’s core PDF engine, which powers 700 million+ users across 20+ years of production deployments. That engine has native awareness of PDF internal structures: content streams, XObject dictionaries, AcroForm field trees, and annotation layers. The OCR layer operates on the same internal page representation the rendering engine uses, so it handles annotated PDFs where text overlaps image regions, and form fields where the visual display and stored value diverge.

The same Structural Extraction endpoint is also Step 1 of Foxit’s PDF Translation (Trial) workflow, which signals that the extraction output is structured enough to backbone a full rewrite-and-rerender pipeline.

NVIDIA’s July 2025 NeMo Retriever research on PDF extraction showed that specialized OCR-based pipelines outperform general-purpose vision-language models on retrieval recall and throughput for complex elements including tables, charts, and infographics. VLMs produce plausible-looking output on clean documents but degrade on exactly the edge cases (multi-column scans, mixed-content pages, annotated overlays) that a specialized pipeline handles systematically.

The Full Object Map: All 12 Extractable PDF Element Types

The Structural Extraction schema v1.0.7 defines twelve element types in the type enum: title, head, paragraph, table, image, headerFooter, form, hyperlink, footnote, sidebar, annotation, and formula.

The API exposes no per-object filter parameters. The only request body fields are documentId (required) and password (optional, for protected PDFs). The engine extracts the full element graph and returns everything in one asynchronous round-trip. You filter client-side on the returned JSON. The design is correct for the workload because partial extraction would require re-running layout recognition per request, costing more compute than transmitting the full element set in a single ZIP.

The result is a ZIP archive. At minimum it contains StructureInfo.json, whose top-level analyzeResult object holds version, pages, elements, and info. Documents that contain figures or tables also produce additional binary files (image renditions and table renditions) alongside the JSON, referenced from individual elements so the JSON payload stays manageable on large documents.

Each element in the document-wide flat elements array carries its own id, type, content, region (with page and an 8-point boundingBox polygon), and score confidence value. A table element adds its cell grid. A form element adds field data. An image element points to its binary file in the ZIP. Because title, head, and paragraph elements appear in document reading order in the elements array, they chunk cleanly on semantically correct boundaries, which is what a RAG index needs to return complete, coherent passages.

Each type maps directly to a downstream use case: table feeds financial reporting pipelines, form drives automated CRM data entry, image routes to computer vision workflows or document archives, annotation builds compliance audit trails, and head combined with paragraph elements in reading order feeds RAG ingestion.

API Walkthrough: The Four-Step Async PDF Extraction Flow

There’s no synchronous path. You upload, get a task ID, poll until completion, then download the result ZIP. Every request carries two headers: client_id and client_secret (lowercase snake_case, as specified in the API spec’s security schemes). Both come from the Developer Portal’s default application. Pass them as named HTTP headers on every request and do not use Authorization: Bearer.

The four-step sequence runs as follows:

The four-step sequence diagram uses two headers on every request: client_id and client_secret. Create a free developer account at account.foxit.com/site/sign-up (no credit card required, no sales call). Once you’re in, the credentials live under the default application in the Developer Portal. Copy the Client ID and Client Secret pair and treat them like any other API secret. Pass them as named HTTP headers on every call (lowercase snake_case, not Authorization: Bearer).

Step 1: Upload the PDF to POST /pdf-services/api/documents/upload as multipart/form-data with the file under field name file. The 100MB ceiling is enforced with a 413 and error code MAX_UPLOAD_SIZE_EXCEEDED. The response body returns { "documentId": "doc_abc123" }.
Step 2: Starts extraction with POST /pdf-services/api/documents/pdf-structural-extract, passing { "documentId": "doc_abc123" }. Add a "password" field for protected PDFs. The response is 202 Accepted with { "taskId": "task_xyz789" }.
Step 3: Polls GET /pdf-services/api/tasks/{task-id}. The TaskResponse carries taskId, status, progress (0-100 integer), resultDocumentId, and an optional error object. The status enum values are PENDING, IN_PROGRESS, COMPLETED, and FAILED. Portal narrative copy occasionally uses “PROCESSING,” but the schema enum value is IN_PROGRESS. Match your code against the enum. Poll until COMPLETED and capture resultDocumentId.
Step 4: Downloads with GET /pdf-services/api/documents/{resultDocumentId}/download, which streams the ZIP archive. The optional filename query parameter overrides the default filename.

The complete cURL sequence for all four steps:

# Step 1: Upload
curl -X POST "https://na1.fusion.foxit.com/pdf-services/api/documents/upload" \
  -H "client_id: YOUR_CLIENT_ID" \
  -H "client_secret: YOUR_CLIENT_SECRET" \
  -F "file=@invoice_batch.pdf"

# {"documentId":"doc_abc123"}

# Step 2: Start extraction
curl -X POST "https://na1.fusion.foxit.com/pdf-services/api/documents/pdf-structural-extract" \
  -H "client_id: YOUR_CLIENT_ID" \
  -H "client_secret: YOUR_CLIENT_SECRET" \
  -H "Content-Type: application/json" \
  -d '{"documentId":"doc_abc123"}'

# 202 Accepted: {"taskId":"task_xyz789"}

# Step 3: Poll task status
curl "https://na1.fusion.foxit.com/pdf-services/api/tasks/task_xyz789" \
  -H "client_id: YOUR_CLIENT_ID" \
  -H "client_secret: YOUR_CLIENT_SECRET"

# {"taskId":"task_xyz789","status":"COMPLETED","progress":100,"resultDocumentId":"result_def456"}

# Step 4: Download the result ZIP
curl "https://na1.fusion.foxit.com/pdf-services/api/documents/result_def456/download" \
  -H "client_id: YOUR_CLIENT_ID" \
  -H "client_secret: YOUR_CLIENT_SECRET" \
  -o extraction_result.zip

The Python version with a polling loop and ZIP parsing:

import requests, json, time, zipfile
BASE_URL = "https://na1.fusion.foxit.com/pdf-services/api"
HEADERS  = {"client_id": "YOUR_CLIENT_ID", "client_secret": "YOUR_CLIENT_SECRET"}

# Step 1: Upload
with open("invoice_batch.pdf", "rb") as f:
    doc_id = requests.post(
        f"{BASE_URL}/documents/upload", headers=HEADERS, files={"file": f}
    ).json()["documentId"]

# Step 2: Start extraction
task_id = requests.post(
    f"{BASE_URL}/documents/pdf-structural-extract",
    headers={**HEADERS, "Content-Type": "application/json"},
    json={"documentId": doc_id},
).json()["taskId"]

# Step 3: Poll until COMPLETED or FAILED
while True:
    task = requests.get(f"{BASE_URL}/tasks/{task_id}", headers=HEADERS).json()
    if task["status"] == "COMPLETED":
        result_doc_id = task["resultDocumentId"]
        break
    if task["status"] == "FAILED":
        raise RuntimeError(f"Extraction failed: {task.get('error')}")
    time.sleep(2)

# Step 4: Download the result ZIP and save it locally for inspection,
# then parse StructureInfo.json from the saved file
response = requests.get(
    f"{BASE_URL}/documents/{result_doc_id}/download", headers=HEADERS
)
with open("advanced-extraction-result.zip", "wb") as f:
    f.write(response.content)

with zipfile.ZipFile("advanced-extraction-result.zip") as zf:
    json_name = next(n for n in zf.namelist() if n.endswith("StructureInfo.json"))
    result = json.loads(zf.read(json_name))["analyzeResult"]

print(f"Schema: {result['version']['schema']}, Elements: {len(result['elements'])}")

On a clean run you should see output like Schema: 1.0.7, Elements: 9 for a small invoice batch. You’ll also find a fresh advanced-extraction-result.zip next to your script. That ZIP holds the full API response, including StructureInfo.json and any rendered image or table binaries, so you can inspect everything the engine returned and not just the parsed JSON.

First, set up and activate a Python virtual environment in your project folder. The official venv guide covers the exact commands for macOS, Linux, and Windows.

Once the virtualenv is active, the sample only needs one third-party package. Drop this into a requirements.txt next to your script and install it with pip install -r requirements.txt:

requests>=2.31.0

If you’re on macOS, use Homebrew Python (brew install python) rather than the system Python from the Xcode command-line tools. The Xcode build is linked against LibreSSL, which is enough to make a correct sample fail.
The ZIP contains a StructureInfo.json file whose top-level object wraps everything under analyzeResult. Inside that wrapper you get a version object, a pages array, a flat elements array, and an info block with analysis metadata. Each element carries its own id, type, content, region (with page and an 8-point boundingBox polygon [x1,y1,x2,y2,x3,y3,x4,y4]), and a score confidence value:

{
  "analyzeResult": {
    "version": {
      "schema": "1.0.7",
      "software": "FoxitPDFAnalyzer",
      "model": "idp-analysis"
    },
    "pages": [
      {
        "pageNumber": 1,
        "size": { "width": 612, "height": 792, "unit": "point" },
        "state": "success"
      }
    ],
    "elements": [
      {
        "id": "title1",
        "type": "title",
        "content": {
          "text": "Q3 Revenue Summary",
          "style": {
            "fontName": "Helvetica",
            "fontSize": 24.0,
            "fontWeight": 0,
            "fontItalic": false
          }
        },
        "region": {
          "page": 1,
          "boundingBox": [72, 47, 317, 47, 317, 80, 72, 80]
        },
        "score": 0.76
      }
    ],
    "info": {
      "basicInfo": {
        "softwareVersion": "1.6.0",
        "analyzedPageCount": 1,
        "elementCounts": { "title": 1 }
      },
      "extendedMetadata": {
        "pageCount": 1,
        "isEncrypted": false,
        "hasAcroform": false,
        "language": "en"
      }
    }
  }
}

Elements of type table, image, and form carry additional type-specific payload on top of this base shape, and any rendered image or table binary lands as a sibling file inside the ZIP referenced from the element.

HTTP errors return a standard error envelope:

{ "code": "VALIDATION_ERROR", "message": "documentId is required" }

The documented error codes include VALIDATION_ERROR (400), MAX_UPLOAD_SIZE_EXCEEDED (413), DOCUMENT_NOT_FOUND (404), STORAGE_ERROR, and INTERNAL_SERVER_ERROR (500).

Password-protected PDFs that arrive with no password parameter reach the processing stage before failing. That failure surfaces in the task status poll response after status reaches FAILED, so your error handler must inspect the task response body in addition to the HTTP status codes from the initial POST calls:

{
  "taskId": "task_xyz789",
  "status": "FAILED",
  "progress": 0,
  "error": {
    "code": "INTERNAL_SERVER_ERROR",
    "message": "Document is password-protected"
  }
}

Wiring Extracted PDF Data Into Your Workflow

Pattern 1: AI/RAG pipeline. Filter the flat elements array to title, head, and paragraph types. Chunk by heading hierarchy, iterating over the array in the order the engine returned it (document reading order is preserved across columns and pages). Embed each chunk and index in Pinecone, pgvector, or your vector store of choice. Correct reading order, as provided by the extraction engine, is the prerequisite for accurate RAG retrieval on multi-column and paginated documents. When chunks split mid-thought because a layout detector merged two columns, retrieval recall drops and answer quality follows.

Pattern 2: BI reporting. Filter elements by type == "table" client-side, then convert each table’s cell structure into a pandas DataFrame:

import pandas as pd

# `result` is the `analyzeResult` object loaded from StructureInfo.json
tables = [e for e in result["elements"] if e["type"] == "table"]

for i, tbl in enumerate(tables):
    # Cells live at content.body.cells[]. Each cell carries rowIndex,
    # columnIndex, and a nested paragraph whose content.text holds the value.
    body = tbl["content"]["body"]
    grid = [["" for _ in range(body["columnCount"])] for _ in range(body["rowCount"])]
    for cell in body.get("cells", []):
        text = cell.get("paragraph", {}).get("content", {}).get("text", "")
        grid[cell["rowIndex"]][cell["columnIndex"]] = text
    df = pd.DataFrame(grid[1:], columns=grid[0])  # first row as header
    print(f"Table {i}: {df.shape[0]} rows x {df.shape[1]} cols")
    # df.to_gbq("finance.q3_revenue", project_id="your-project")  # BigQuery
    # df.to_sql("q3_revenue", engine)                             # Postgres / Snowflake

The row and column indices from the extraction schema map directly to DataFrame positions, so you get a correctly-structured table with zero manual parsing.

Pattern 3: n8n automation. The four-step flow maps to a chain of HTTP Request nodes in n8n. The first node uploads to POST .../upload and passes documentId through the item. The second sends POST .../pdf-structural-extract and captures taskId. A Loop Over Items construct with an HTTP Request node calling GET .../tasks/{taskId} on a two-second interval checks status until COMPLETED, then routes to the download node. The final HTTP Request node calls GET .../documents/{resultDocumentId}/download, and a Code node using n8n’s binary data helpers unpacks the ZIP and parses the JSON for routing to a Salesforce, HubSpot, Postgres, or Airtable node. The polling requirement makes this a multi-node workflow, but you write zero custom glue code and gain n8n’s built-in error routing and retry handling.

PDF Extraction Tools Compared: Foxit vs. Adobe, Google, Amazon, and Azure

Tool	Underlying Approach	Ecosystem Lock-in	Handles Scanned PDFs	Pricing Model	Setup Overhead	Status
Foxit Structural Extraction	Proprietary OCR + layout recognition + AI (integrated core engine)	Cloud-agnostic REST API	Yes (dedicated OCR layer)	Subscription, no per-page credits	Low (2 credential headers, 4 REST calls)	Trial (schema v1.0.7)
Adobe PDF Extract API	Adobe Sensei ML, reading order + renditions	Adobe Document Services	Yes	Contact sales	Medium (Adobe SDK + ecosystem)	GA
Google Document AI	Cloud ML + generative AI, Document Object Model	Google Cloud required	Yes	Per-page pay-as-you-go	Medium-high (GCP + IAM)	GA
Amazon Textract	Deep learning OCR, key-value and table extraction	AWS-native	Partial (strong on forms, weaker on complex layouts)	Per-page pay-as-you-go	Medium (AWS + IAM)	GA
Azure Document Intelligence	Prebuilt + custom ML models	Azure ecosystem	Yes (prebuilt models)	Per-page + model training costs	High for custom models	GA

Google Document AI and Azure Document Intelligence win on ecosystem integration if you’re all-in on those clouds. Adobe wins on PDF structural fidelity for workflows already inside the Adobe Document Services ecosystem. Amazon Textract excels on standardized form documents where its pre-trained schema fits the input. These are real advantages, and the comparison is honest only when those contexts are acknowledged.

Foxit’s case is strongest when you need a cloud-agnostic REST API with zero ecosystem dependency, full object coverage across all twelve element types, and enterprise throughput (10 to 10,000+ PDFs/day) with SOC 2, GDPR, and HIPAA compliance built in. The Structural Extraction status is a real trade-off to factor in. The schema at v1.0.7 is callable and stable enough for pipeline integration today, but GA competitors carry a finalized contract. Pin your parser to the version field in the response and you’re insulated from schema evolution.

Your First PDF Extraction API Call, Right Now

Go to developer-api.foxit.com, create a free developer account (no credit card required), and copy your Client ID and Client Secret from the default application. Use the built-in API Playground or import the Postman collection from the Developer Portal to run the four-step sequence: upload a real document (an invoice, a multi-page contract, or a scanned form), call pdf-structural-extract with the returned documentId, poll tasks/{taskId} until COMPLETED, then download via documents/{resultDocumentId}/download.

Unzip the result, open StructureInfo.json, and check three things: analyzeResult.version.schema should report 1.0.7, analyzeResult.elements[] should contain at least one table element and one form element if your source document includes those, and the ZIP root should contain the corresponding binary files for any image-type elements. That verification confirms the full extraction pipeline is wired correctly end-to-end.

The same endpoint pattern scales to enterprise volumes. Increase upload and poll concurrency horizontally and the architecture stays identical, with no schema changes, no infrastructure modifications, and no per-page credit consumption to track.

The engineering gap between what basic extraction libraries return and what downstream systems actually consume is where document pipeline hours accumulate. Structural Extraction closes that gap at the API layer, so the complexity stays in the engine and out of your codebase. Get started at developer-api.foxit.com.

PDF Structural Extraction FAQ

What is PDF structural extraction?

PDF structural extraction is the process of identifying and classifying the semantic elements inside a PDF, such as titles, paragraphs, tables, forms, images, and annotations, rather than just pulling raw text. Foxit’s PDF Structural Extraction API returns twelve distinct element types as structured JSON, preserving spatial relationships, reading order, and table cell grids so downstream systems like RAG pipelines, BI dashboards, and CRMs can consume the data without manual parsing.

Can Foxit's API extract text from scanned PDFs?

Yes. Foxit’s PDF Structural Extraction engine includes a dedicated OCR layer that recognizes characters from image-based and scanned PDFs across 200+ languages. The OCR runs on the same internal page representation as the rendering engine, so it handles edge cases like text overlapping image regions, stamped signatures, and engineering drawing annotations that basic libraries like PyMuPDF silently drop.

How does Foxit's PDF extraction API differ from Adobe, Google Document AI, and Amazon Textract?

Foxit’s API is cloud-agnostic with no ecosystem lock-in, requiring just two credential headers and four REST calls. Adobe PDF Extract requires the Adobe Document Services ecosystem, Google Document AI requires GCP and IAM setup, and Amazon Textract requires AWS infrastructure. Foxit also uses subscription-based pricing without per-page credits, while Google, AWS, and Azure all charge per page.

What PDF elements can Foxit's Structural Extraction API identify?

The API identifies twelve element types: title, head, paragraph, table, image, headerFooter, form, hyperlink, footnote, sidebar, annotation, and formula. Each element returns with its content, an 8-point bounding box polygon, page location, and a confidence score. Tables include full cell grids with row and column indices, forms include field data, and images are extracted as separate binary files inside the result ZIP.

How do I call the Foxit PDF Structural Extraction API?

The API uses a four-step asynchronous flow: upload the PDF via POST /documents/upload to get a documentId, start extraction with POST /documents/pdf-structural-extract, poll GET /tasks/{taskId} every two seconds until status is COMPLETED, then download the result ZIP via GET /documents/{resultDocumentId}/download. Authentication uses two headers, client_id and client_secret, available from the default application in the Foxit Developer Portal.

Is the Foxit PDF Structural Extraction API ready for production use?

The endpoint is currently in Trial status with schema version v1.0.7, meaning the contract is stable but may evolve. It runs on the production base URL at developer-api.foxit.com and is built on Foxit’s core PDF engine, which powers 700 million+ users across 20+ years of deployments. For production pipelines, pin your parser to the version field in the response to insulate against future schema changes.

Foxit MCP Server: Give AI Agents Direct Access to 30+ PDF Tools via Model Context Protocol

Learn how the Foxit MCP Server lets AI agents handle PDF conversion, OCR, merge, signing, and document workflows.

Building a document automation agent with raw REST calls means writing the same boilerplate every time: upload a file, poll for task completion, download the result, handle errors, and manage auth tokens across multiple endpoints. For PDF operations, that loop repeats for every conversion, OCR call, or merge operation in your pipeline. The Foxit PDF API MCP Server collapses those loops into 30+ directly callable tools, with the MCP Server handling upstream REST complexity internally.

This guide covers how the server registers, what it exposes, how Foxit’s eSign and DocGen REST APIs extend the same agent session into signing and document generation workflows, and a concrete four-step workflow you can replicate against your own documents.

MCP Architecture in 90 Seconds

The MCP specification defines three roles. The Host is the LLM runtime (Claude Desktop, VS Code with GitHub Copilot, or Cursor) that manages the conversation and decides when to call tools. The Server is the capability provider, a process that advertises tools over the MCP protocol and executes them against some underlying service. Tools are the individual callable operations each server exposes, defined by a JSON schema the host uses to understand inputs and outputs.

Foxit occupies both sides of this architecture. Foxit PDF Editor ships as an MCP Host, the first PDF application to do so, connecting outward to external MCP servers like Gmail or Salesforce so its AI assistant can reach those services. The Foxit PDF API MCP Server works in the other direction, exposing Foxit’s cloud PDF Services API as 30+ tools for any MCP Host to call.

The MCP Server exposes PDF Services operations covering conversion between formats, content extraction, OCR, merge, split, compress, flatten, linearize, compare, watermark, form data import/export, security, and property inspection. Foxit’s eSign API and DocGen API are separate REST services that are not part of the MCP Server, so they are not exposed as MCP tools. A single agent workflow can still reach them, but through the agent’s own code-execution layer rather than through the MCP protocol, a distinction the eSign section explains in detail. The MCP tools handle PDF processing, while code the agent runs handles signing and template generation.

Prerequisites and Configuration

You need three things before registering the server:

A Foxit developer account (free plan at developer-api.foxit.com, no credit card required) to obtain a client_id and client_secret
Python 3.11+ with the uv package manager (or Node.js 18+ with pnpm for the TypeScript version)
An MCP-compatible host such as Claude Desktop, VS Code, or Cursor

Clone the repo from github.com/foxitsoftware/foxit-pdf-api-mcp-server, then register it in your host’s MCP config. The walkthrough below uses Claude Desktop, but the same command, args, and env values work in any MCP host. In Claude Desktop, open Settings, select the Developer tab, and click Edit Config.

Then open claude_desktop_config.json with any text edito(stored at ~/Library/Application Support/Claude/ on macOS or %APPDATA%\Claude\ on Windows).

Add the Foxit server under the mcpServers key:

{
  "mcpServers": {
    "foxit-pdf": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/foxit-pdf-api-mcp-server",
        "run",
        "foxit-pdf-api-mcp-server"
      ],
      "env": {
        "FOXIT_CLOUD_API_HOST": "https://na1.fusion.foxit.com/pdf-services",
        "FOXIT_CLOUD_API_CLIENT_ID": "your_client_id",
        "FOXIT_CLOUD_API_CLIENT_SECRET": "your_client_secret"
      }
    }
  }
}

Set FOXIT_CLOUD_API_CLIENT_ID and FOXIT_CLOUD_API_CLIENT_SECRET as environment variables on your system before the host process launches. Passing credentials through prompt context is a security risk your production setup should address. The client_id and client_secret from your developer portal authenticate all MCP tool calls to the PDF Services API. Adding eSign to the same agent session requires its own OAuth2 token exchange (covered in the next section), keeping the two credential scopes isolated.

After saving, completely quit and reopen Claude Desktop so it loads the config and launches the server as a local subprocess over standard input and output, the transport the Foxit server uses.

On restart, you should see the foxit MCP as Running in the local MCP servers in the developer tab. If you go the Customize tab then open the Connectors and click foxit-pdf to see the tools that the Foxit MCC has access to, you should see the 30+ tools registered.

If the connector never appears, the server failed to launch, and Claude’s logs at ~/Library/Logs/Claude/mcp*.log usually point to the cause, commonly a missing uv binary or a wrong --directory path.

To call a tool, type a natural-language request such as “Convert this Word file to PDF and compress it.” The agent selects pdf_from_word and pdf_compress, and Claude Desktop shows an approval prompt with the exact tool name and arguments before each call runs; the tool’s JSON result then streams back into the conversation.

That per-call approval is your audit point, since it surfaces precisely which tool the agent chose and what it passed.

If you would rather run the server in VS Code, the equivalent entry goes in .vscode/mcp.json under a top-level servers key, with an added "type": "stdio" field so VS Code launches the server the same way:

{
  "servers": {
    "foxit-pdf": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/foxit-pdf-api-mcp-server",
        "run",
        "foxit-pdf-api-mcp-server"
      ],
      "env": {
        "FOXIT_CLOUD_API_HOST": "https://na1.fusion.foxit.com/pdf-services",
        "FOXIT_CLOUD_API_CLIENT_ID": "your_client_id",
        "FOXIT_CLOUD_API_CLIENT_SECRET": "your_client_secret"
      }
    }
  }
}

You can also run MCP: Add Server from the Command Palette (Cmd+Shift+P or Ctrl+Shift+P), choose Command (stdio), and pick Workspace to write the entry into .vscode/mcp.json or Global to store it in your user profile. Once saved, VS Code shows inline Start, Stop, and Restart actions above the server entry and lists it under the MCP SERVERS – INSTALLED view, where a green indicator and the discovered tool count confirm the connection.

PDF Services MCP Tools: Full Catalog

The 30+ tools organize into seven functional categories. Most tools expect a documentId returned by a prior upload_document call, and return a resultDocumentId you pass to download_document when you want the output locally. The exception is pdf_from_url, which accepts a URL directly.

Document Lifecycle

upload_document: upload a PDF, Office file, image, HTML file, or plain text file; returns a documentId for subsequent operations
download_document: retrieve a processed result to a local file path
delete_document: clean up stored files from cloud storage

PDF Creation (file to PDF)

pdf_from_word, pdf_from_excel, pdf_from_ppt: convert Office documents to PDF
pdf_from_text, pdf_from_image, pdf_from_html: convert plaintext, image files, or HTML to PDF
pdf_from_url: fetch a live URL and convert the rendered page to PDF

PDF Conversion (PDF to file)

pdf_to_word, pdf_to_excel, pdf_to_ppt: extract editable Office formats from a PDF
pdf_to_text, pdf_to_html, pdf_to_image: export text, HTML, or image representations

Manipulation

pdf_merge: combine multiple PDFs into one
pdf_split: split by page ranges, page count, or every page individually
pdf_extract: pull a subset of pages from a PDF
pdf_compress: reduce file size by 30-70% depending on content type
pdf_flatten: convert form fields and annotations to static content (required for compliance archiving workflows)
pdf_linearize: optimize for Fast Web View so browsers can stream PDF pages incrementally
pdf_watermark: apply text or image watermarks with configurable position, opacity, and rotation
pdf_manipulate: rotate, delete, or reorder pages

Analysis

pdf_compare: diff two PDFs and return a color-coded annotation document showing changes
pdf_ocr: convert scanned or image-based PDFs to searchable text with multi-language support
pdf_structural_analysis: detect document structure (titles, headings, paragraphs, tables with cell grids, images, form fields, hyperlinks, and metadata) with bounding boxes, following the Foxit PDF structural extraction engine schema. The result is JSON packaged inside a downloadable ZIP, not a return of named business entities; it reports layout and structure, and turning that into fields like party names is the job of the agent’s LLM, which performs the semantic extraction over that JSON

Security and Forms

pdf_protect: add password protection with 128-bit or 256-bit AES encryption and granular permission flags
pdf_remove_password: strip password protection from a document
export_pdf_form_data: extract form field values as JSON
import_pdf_form_data: populate form fields from a JSON payload

Properties

get_pdf_properties: return page count, page dimensions, PDF version, encryption status, digital signature info, embedded files, font inventory, and document metadata

The most-used operation in production document pipelines is pdf_from_word. Your agent uploads a DOCX file, gets back a documentId, then calls pdf_from_word with that ID. The underlying PDF Services API runs the conversion asynchronously, but the MCP Server handles polling internally and delivers the final result directly to your agent.

MCP tool call:

{
  "name": "pdf_from_word",
  "input": {
    "documentId": "doc_abc123"
  }
}

MCP tool response:

{
  "success": true,
  "taskId": "task_xyz789",
  "resultDocumentId": "doc_result456",
  "message": "Word document converted to PDF successfully. Download using documentId: doc_result456"
}

Pass doc_result456 to download_document to write the output PDF to disk, or feed it directly into another tool call like pdf_structural_analysis or pdf_compress as the next step in a chain.

Extending to eSign: Foxit’s Signing API as a Complementary REST Layer

After PDF processing via MCP tools, the next stage of the workflow dispatches a document for signature through Foxit’s eSign REST API, which lives at https://na1.foxitesign.foxit.com. This guide uses the na1 (US) region throughout.

Foxit also operates regional eSign hosts for the EU (eu1.foxitesign.foxit.com), Canada (na2.foxitesign.foxit.com), and Australia (au1.foxitesign.foxit.com). The endpoints and payloads are identical; only the host changes, so pick the host that matches your data residency requirements.

The eSign API is not part of the Foxit MCP Server, so it is not an MCP tool, and that distinction matters for how the agent reaches it. Most MCP hosts cannot make arbitrary HTTP calls on their own, so the agent does not reach eSign “through MCP.” Instead, the agent invokes eSign from its own code-execution layer, whether that is a code interpreter the host provides, an agent framework that runs Python, or a custom tool you register that wraps the eSign calls. The cleanest production pattern is to wrap the eSign operations you need as custom MCP tools so the host calls them the same way it calls the PDF tools; the production considerations section returns to this. The code below is what that layer runs.

Authentication uses OAuth2 client_credentials. The eSign token exchange is a distinct flow from the PDF Services header auth that backs your MCP tools:

import requests

resp = requests.post(
    "https://na1.foxitesign.foxit.com/api/oauth2/access_token",
    data={
        "client_id": ESIGN_CLIENT_ID,
        "client_secret": ESIGN_CLIENT_SECRET,
        "grant_type": "client_credentials",
        "scope": "read-write"
    }
)
access_token = resp.json()["access_token"]

The Foxit eSign API developer guide uses “folder” terminology throughout. The key endpoints in an automated signing flow are:

POST /api/folders/createfolder: create a signing folder from one or more PDF documents, with signers, subject, and message
POST /api/folders/sendDraftFolder: dispatch a draft folder to its signers
POST /api/templates/createtemplate: save a reusable template from a PDF with pre-placed signature fields (instantiate a folder from it later via POST /api/templates/createFolder)
GET /api/folders/viewActivityHistory?folderId={id}: retrieve the activity audit trail for a folder once it has been sent (a draft that has never been shared returns an error)
Webhook channels for status callbacks: register a callback URL to receive real-time events when signers view, sign, or decline

A createfolder call takes the PDF output from your MCP pipeline, uploaded to eSign’s document storage after download_document retrieves it, and sets up the signing workflow:

POST /api/folders/createfolder
Authorization: Bearer {access_token}
Content-Type: application/json

{
  "folderName": "Acme Corp Contract - Q3 2025",
  "sendNow": false,
  "fileUrls": ["https://your-storage.example.com/acme_contract_final.pdf"],
  "fileNames": ["acme_contract_final.pdf"],
  "parties": [
    {
      "firstName": "John",
      "lastName": "Smith",
      "emailId": "[email protected]",
      "permission": "FILL_FIELDS_AND_SIGN",
      "sequence": 1
    }
  ]
}

Set sendNow to false to create a draft folder, then dispatch it with a separate call to /api/folders/sendDraftFolder. Alternatively, set sendNow to true to create and send in a single call. For files not accessible via URL, add "inputType": "base64" and pass the documents as a base64FileString array instead of fileUrls; omitting inputType makes the API reject the base64 payload as empty.

Foxit’s eSign API ships with HIPAA, eIDAS, ESIGN Act, UETA, 21 CFR Part 11, FERPA, and FINRA compliance built in. Audit trail records carry signer location, IP address, recipient identity, event timestamp, consent confirmation, security level, and complete folder history. For legal defensibility in regulated industries, capture and store these fields in your own data layer, because relying solely on Foxit’s folder history API for compliance record-keeping introduces a single point of failure in your audit chain.

End-to-End Workflow: AI Agent Automates a Sales Contract

Picture a sales ops agent that starts from a single natural language goal, “Generate a contract for Acme Corp, $48,000 ARR, and send it to [email protected] for signature.” Nothing about the tool sequence is hard-coded. The MCP Server advertises its PDF tools to the host on connection, so the agent can read the goal, recognize that it has a template to render and a document to route for signature, and decide which operations to call and in what order. The PDF steps run as MCP tool calls; the DocGen and eSign steps run from the agent’s code layer. The sequence below is one plausible run the agent might choose, not a fixed script you wire up in advance.

To get a PDF to work with, the agent first reaches for MCP tools. It calls upload_document with the DOCX contract template, receives documentId: "doc_abc", and calls pdf_from_word. The MCP Server handles the async conversion internally and returns resultDocumentId: "doc_pdf" once it completes.

Needing to know what is inside that PDF, the agent calls pdf_structural_analysis with documentId: "doc_pdf". The tool does not hand back named entities like “party” or “ARR.” It returns a resultDocumentId pointing to a ZIP archive, so the agent calls download_document to retrieve it, unzips it, and reads the structural JSON, which describes headings, paragraphs, and table cells with their positions. The agent’s LLM is what performs the semantic extraction: it reads the structural JSON and pulls “Acme Corp” out of a heading or a contract value out of a table cell, confirming the fields it needs are present. The tool hands back structure; the model turns that structure into meaning. If you want the API to return business entities directly rather than leaning on the model to interpret layout, that is the job of Foxit’s iDox.ai Document API, a separate service built for entity and PII extraction.

With the field values in hand, the agent generates the finished contract through the DocGen API, posting to /document-generation/api/GenerateDocumentBase64 with the values merged into the template via {{dynamic_tags}} syntax. DocGen is synchronous, so the call returns the finalized PDF in the response body, with Acme Corp’s name, the $48,000 ARR figure, and the correct dates populated. No polling step is involved.

Finally, the agent routes the document for signature. It authenticates against the eSign OAuth2 endpoint, uploads the DocGen output, creates a signing folder via /api/folders/createfolder with [email protected] as the signer, and dispatches it via /api/folders/sendDraftFolder.

What ties this together is that the model decides the order from the goal, not a script. The PDF steps resolve to MCP tool calls the host already knows about; the DocGen and eSign steps run through the agent’s code layer, since those APIs are not MCP tools. The agent chains the output of one step into the input of the next, and the only orchestration you maintain is whatever exposes that code layer to the model, ideally a set of custom tools rather than ad hoc scripting.

Production Considerations: Error Handling, Rate Limits, and Data Governance

When you call PDF Services through the MCP Server, async polling happens inside the server process. Your agent receives a final resultDocumentId only after the task completes. When you call the raw PDF Services REST API directly, every operation returns a taskId you poll manually. The pattern below applies exponential backoff with a ceiling of 10 seconds per interval and a 30-second total timeout:

import time, requests

API_HOST = "https://na1.fusion.foxit.com/pdf-services"
auth_headers = {
    "client_id": "your_client_id",
    "client_secret": "your_client_secret"
}

def poll_task(task_id: str, max_wait: int = 30) -> str:
    delay = 1
    elapsed = 0
    while elapsed < max_wait:
        resp = requests.get(
            f"{API_HOST}/api/tasks/{task_id}",
            headers=auth_headers
        )
        data = resp.json()
        if data["status"] == "COMPLETED":
            return data["resultDocumentId"]
        time.sleep(delay)
        elapsed += delay
        delay = min(delay * 2, 10)
    raise TimeoutError(f"Task {task_id} timed out after {max_wait}s")

Because eSign and DocGen are not MCP tools, decide deliberately how the agent reaches them. Letting the model emit raw HTTP from a free-form code interpreter is brittle and hard to audit. The more durable pattern is to wrap the specific eSign and DocGen operations you use, such as create-folder, send-folder, and generate-document, as custom MCP tools with typed inputs. The host then calls them through the same protocol it uses for the PDF tools, the credentials stay in the tool process rather than in the prompt, and the agent’s choices become inspectable tool calls instead of opaque scripts.

The output of pdf_structural_analysis deserves its own caution. The structural JSON for a long contract can run to many thousands of elements, and feeding the entire file into the model can quietly blow past its context window, which tends to surface as a truncated or confused extraction rather than a clean error. Have the code that unzips the archive filter the JSON before the model sees it, keeping only the element types and pages that matter (for a contract, usually the heading blocks and the relevant table), rather than passing the whole document through.

The free developer plan at developer-api.foxit.com covers development and testing volumes. Production workloads above the free-tier threshold require a volume plan requested through the Developer Portal.

For data governance, all API traffic runs over TLS 1.2+, and documents at rest use AES-256 encryption. Foxit’s API security documentation covers SOC 2 Type II audit status, HIPAA BAA support, GDPR, CCPA, eIDAS, ESIGN Act, UETA, 21 CFR Part 11, FERPA, and FINRA requirements. Customer data runs in logically segmented environments. For healthcare, legal, or financial services pipelines, confirm your data residency requirements before connecting production document flows, then choose the matching regional eSign host noted earlier, since the host you call determines where data is processed.

PDF API MCP Server FAQs

What is the Foxit PDF API MCP Server?

The Foxit PDF API MCP Server is an open-source Model Context Protocol server that exposes Foxit’s cloud PDF Services API as 30+ callable tools. Any MCP-compatible AI agent host, including Claude Desktop, VS Code with GitHub Copilot, and Cursor, can invoke these tools directly.

What PDF operations does the Foxit MCP Server support?

The server supports conversion (Word, Excel, PowerPoint, image, HTML, and URL to PDF and back), OCR, merge, split, extract, compress, flatten, linearize, watermark, compare, form data import/export, password protection, and full document property inspection across seven functional tool categories.

How does the Foxit MCP Server handle authentication?

PDF Services tools authenticate via a client_id and client_secret set as environment variables before the MCP host launches. The eSign API uses a separate OAuth2 client_credentials token exchange against https://na1.foxitesign.foxit.com/api/oauth2/access_token. The two credential scopes are isolated by design.

Does the Foxit MCP Server work with Claude Desktop and VS Code?

Yes. The server registers using a standard mcp.json config block for VS Code with GitHub Copilot or a claude_desktop_config.json block for Claude Desktop. The same config structure works for Cursor. All three hosts discover the server’s tools automatically on connection.

Is the Foxit PDF API MCP Server free to use?

The Foxit developer account is free with no credit card required and covers development and testing volumes. Production workloads above the free-tier threshold require a volume plan through the Developer Portal.

Run Your First Tool Call Now

Getting a working MCP tool call takes under 15 minutes:

Create a free developer account at developer-api.foxit.com (no credit card, instant access). Copy your client_id and client_secret from the dashboard.
Set the three environment variables:

export FOXIT_CLOUD_API_HOST="https://na1.fusion.foxit.com/pdf-services"
export FOXIT_CLOUD_API_CLIENT_ID="your_client_id"
export FOXIT_CLOUD_API_CLIENT_SECRET="your_client_secret"

Clone the repo, register it using the config block from the Prerequisites section, restart your MCP host, and invoke pdf_from_url with any public URL. You’ll have a confirmed PDF output in your working directory. The Developer Portal also includes a live API Playground for validating request payloads against the PDF Services API before wiring them into an agent.

For a full signing workflow, the minimum viable addition to the MCP setup is authenticating against the eSign OAuth2 endpoint and posting to /api/folders/createfolder with a static PDF. DocGen field population, pdf_structural_analysis extraction, and webhook callbacks extend the same pattern incrementally from there.

Get your free API access at developer-api.foxit.com.

Automate Dynamic PDF Generation with the Foxit DocGen API: Word Templates, JSON Data, and Real API Calls

Skip the HTML-to-PDF headaches. Use Foxit’s DocGen API to turn Word templates and JSON data into clean, formatted PDFs with one API call.

If you’ve tried to generate a contract or invoice from HTML, you’ve probably burned hours on page-break-inside: avoid declarations that Chrome renders one way and a headless browser renders another. Headers and footers require separate print-media queries, and by the time you’ve got a repeating table header working correctly across pages, you’ve invested a full day of engineering into CSS that exists solely to trick a browser into behaving like a printer.

HTML documents reflow content into a viewport while PDF documents have fixed page geometry. Forcing one model into the other produces predictable failure modes: footnotes that collide with page footers, tables that split at the worst possible row, custom fonts that substitute silently, and signature blocks that drift off-page on longer documents.

There’s a larger practical cost too. For most teams, the authoritative source for enterprise document templates is already a Word file. Your legal team owns the NDA in .docx format. Finance owns the invoice in .docx format. Every structural change flows through Word because that’s where the tracked changes, formatting history, and review process live. Maintaining a parallel HTML version of each template doubles your maintenance surface from day one.

Foxit’s DocGen API eliminates that parallel entirely. You keep your templates as .docx files, embed data tags directly in Word, POST the base64-encoded template and a JSON payload to a single REST endpoint, and receive the rendered PDF (or DOCX) in the response body. You eliminate the browser rendering engine, the print-media CSS layer, and the overhead of a second template format.

How the Foxit DocGen API Works

The core model is a single synchronous POST to the GenerateDocumentBase64 endpoint at developer-api.foxit.com. Your request body carries three fields:

base64FileString: your .docx template, base64-encoded
documentValues: a JSON object containing your merge data
outputFormat: either "pdf" or "docx"

The API processes the template, resolves every tag against your data, and returns a JSON response containing base64FileString (the rendered document) and a message field confirming success or describing a failure. The exchange is fully synchronous, so you receive the finished document in the same HTTP response with no job ID to poll and no webhook to configure.

Authentication uses two HTTP headers: client_id and client_secret. Both come from the Foxit Developer Portal when you create an account. The free Developer plan provides 500 credits per year with no credit card required, and each GenerateDocumentBase64 call consumes exactly one credit. The Startup plan ($1,750/year) provides 3,500 credits. The Business plan ($4,500/year) covers 150,000 credits for production workloads. For context, Nutrient’s API starts at $75 for 1,000 credits, and Apryse requires a sales conversation before you can access pricing at all.

The complete call flow runs from template file to PDF on disk.

You can explore every endpoint in the live API playground at developer-api.foxit.com, and the portal includes a Postman collection you can import to run authenticated requests without writing a line of code first.

Build a Word Template with DocGen Tags

Open any .docx file in Microsoft Word and type your tags as plain text directly in the document. The DocGen API uses double-brace syntax: {{field_name}}. Tags go anywhere Word accepts text: headings, body paragraphs, table cells, headers, footers, or text boxes.

Scalar field tags resolve directly to the matching key from your documentValues JSON. A document header with {{customer_name}}, {{invoice_number}}, and {{invoice_date}} pulls those three values straight from the top-level keys of your payload.

For arrays, you wrap a single table row (the data row, not the header row) with {{TableStart:array_name}} and {{TableEnd:array_name}} markers. The wrapped row acts as a template row, and the API renders one output row per item in the JSON array. An invoice line-items table in Word looks like this:

Description	Qty	Unit Price	Total
`{{TableStart:line_items}}{{description}}`	`{{qty}}`	`{{unit_price}}`	`{{total}}{{TableEnd:line_items}}`

Within the array row, ROW_NUMBER auto-increments with each rendered row. A SUM(ABOVE) field placed in the row directly below the {{TableEnd:line_items}} marker calculates a column total across all rendered data rows.

For nested JSON objects, use dot-notation in your tags. A shipping address block references {{shipping.street}}, {{shipping.city}}, and {{shipping.postal_code}}, mapping to properties nested inside a shipping object in your payload. The nesting can go multiple levels deep, so {{customer.address.city}} resolves against documentValues.customer.address.city.

For a working starting point, grab the downloadable invoice template from the foxit-demo-templates repo. The file is well under the 4 MB upload limit and demonstrates every pattern this article uses: scalar tags, {{TableStart:line_items}} / {{TableEnd:line_items}} with {{ROW_NUMBER}}, currency and date format switches, and subtotal / tax / total fields below the line-items table.

One sizing constraint applies while you build your own template. DocGen rejects uploads larger than 4 MB, so if you embed product photos, scanned letterhead, or full font subsets, compress the images before saving, drop embedded fonts where you can rely on system fonts, or split a large template into smaller per-section templates that you generate and merge separately.

Make Your First API Call: Generate a PDF from JSON

Run a quick pre-flight check before the first call to catch the issues that derail most clean-account run-throughs:

Account created and client_id / client_secret copied from the Developer Portal API Keys section
Sample template saved locally as invoice_template.docx in the directory you’ll run the script from
Template file size confirmed under 4 MB (ls -lh invoice_template.docx on macOS or Linux, right-click → Properties on Windows)

With those in place, confirm your credentials work with a cURL call. The Foxit Developer Portal includes a Postman collection for this, but a quick cURL request against the API catches auth issues before any code runs:

curl -X POST "https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64" \
  -H "client_id: YOUR_CLIENT_ID" \
  -H "client_secret: YOUR_CLIENT_SECRET" \
  -H "Content-Type: application/json" \
  -d '{"base64FileString":"","documentValues":{},"outputFormat":"pdf"}'

A 401 here means invalid credentials. A 400 with a message about the template confirms your headers are accepted and you can proceed to the full call.

Save your .docx template as invoice_template.docx in the same directory as this script, then run the complete generation:

import requests
import base64

CLIENT_ID = "your_client_id"
CLIENT_SECRET = "your_client_secret"
API_URL = "https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64"

# Read and encode the template
with open("invoice_template.docx", "rb") as f:
    template_b64 = base64.b64encode(f.read()).decode("utf-8")

# Build the data payload
document_values = {
    "customer_name": "Acme Corporation",
    "invoice_number": "INV-2025-0042",
    "invoice_date": "07/15/2025",
    "due_date": "08/14/2025",
    "line_items": [
        {
            "description": "API Integration Consulting",
            "qty": 8,
            "unit_price": 195.00,
            "total": 1560.00
        },
        {
            "description": "Document Automation Setup",
            "qty": 1,
            "unit_price": 750.00,
            "total": 750.00
        }
    ],
    "subtotal": 2310.00,
    "tax_rate": 0.08,
    "tax_amount": 184.80,
    "total_due": 2494.80
}

# Construct the request body
payload = {
    "base64FileString": template_b64,
    "documentValues": document_values,
    "outputFormat": "pdf"
}

headers = {
    "client_id": CLIENT_ID,
    "client_secret": CLIENT_SECRET,
    "Content-Type": "application/json"
}

response = requests.post(API_URL, json=payload, headers=headers)

if response.status_code == 200:
    result = response.json()
    pdf_bytes = base64.b64decode(result["base64FileString"])
    if pdf_bytes[:5] != b"%PDF-":
        raise ValueError("Response did not contain a valid PDF")
    with open("invoice_output.pdf", "wb") as out:
        out.write(pdf_bytes)
    print("PDF written to invoice_output.pdf")
else:
    print(f"Error {response.status_code}: {response.json().get('message')}")

The success response is a JSON object with three keys: base64FileString (the rendered PDF, base64-encoded), fileExtension ("pdf"), and message ("PDF Document Generated Successfully"). Decoding and writing the bytes to disk gives you a complete, formatted PDF with every tag replaced by its corresponding data value. If you omit a key from documentValues, the API renders the corresponding tag as an empty string, producing a blank field in the output.

Advanced Data Scenarios: Arrays, Nested Objects, and Built-In Functions

The two-row invoice above works, but most production documents have more complex data shapes. Three patterns cover the majority of real-world cases.

For multi-row tables, the line_items array in the Python snippet above already shows the basic structure. To generate five rows, pass five objects in the array. The Word template row tagged with {{TableStart:line_items}} and {{TableEnd:line_items}} repeats exactly once per array item:

{
  "line_items": [
    {
      "description": "UX Design Review",
      "qty": 4,
      "unit_price": 150.0,
      "total": 600.0
    },
    {
      "description": "Backend API Development",
      "qty": 12,
      "unit_price": 185.0,
      "total": 2220.0
    },
    {
      "description": "Database Schema Migration",
      "qty": 3,
      "unit_price": 200.0,
      "total": 600.0
    },
    {
      "description": "QA Testing",
      "qty": 6,
      "unit_price": 95.0,
      "total": 570.0
    },
    {
      "description": "Deployment and Documentation",
      "qty": 2,
      "unit_price": 175.0,
      "total": 350.0
    }
  ]
}

The API generates exactly five table rows. Swap in 50 items and you get 50 rows, with page breaks handled by Word’s native pagination logic.

For nested objects, the DocGen API resolves dot-notation paths against the full depth of your JSON structure. A shipping confirmation template referencing {{customer.address.city}} works against this payload without any flattening on your end:

{
  "customer": {
    "name": "Sarah Chen",
    "email": "[email protected]",
    "address": {
      "street": "742 Evergreen Terrace",
      "city": "Portland",
      "state": "OR",
      "postal_code": "97201"
    }
  }
}

In the Word template, {{customer.name}}, {{customer.address.city}}, and {{customer.address.postal_code}} each resolve to the correct nested value. You can reference the same nested object from multiple locations in the template, and the API populates each instance independently.

For numeric and date formatting, the DocGen API respects Word’s native field switch syntax. Adding \# Currency to a tag formats a numeric value as a currency string, so {{unit_price \# Currency}} renders 195.00 as \$195.00. Date fields accept \@ "MM/dd/yyyy" to control output format, so {{invoice_date \@ "MM/dd/yyyy"}} formats an ISO date string to 07/15/2025. To auto-calculate a column total, place a SUM(ABOVE) field in the Word table row immediately below {{TableEnd:line_items}} and the API evaluates it against the rendered data rows.

Error Handling and Production Readiness

The DocGen API returns a focused set of HTTP status codes. A 200 confirms successful generation. A 401 means your client_id or client_secret headers are invalid, and the fix is to re-copy the credentials from the Developer Portal. A 400 covers three cases. The first is a malformed request body, for example a missing base64FileString or outputFormat. The second is structural issues with the template itself, such as a {{TableStart}} marker placed outside its table row. The third is an oversize template; DocGen rejects .docx uploads larger than 4 MB, and the fix is to compress embedded images, drop embedded fonts, or split the template before re-encoding. The message field in every non-200 response body gives you the specific reason, so log it rather than discarding the response object.

A production wrapper handles all three cases and adds exponential backoff for transient server errors:

import requests
import base64
import time

def generate_document(client_id, client_secret, template_path,
                      document_values, output_format="pdf"):
    API_URL = "https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64"

    with open(template_path, "rb") as f:
        template_b64 = base64.b64encode(f.read()).decode("utf-8")

    payload = {
        "base64FileString": template_b64,
        "documentValues": document_values,
        "outputFormat": output_format
    }
    headers = {
        "client_id": client_id,
        "client_secret": client_secret,
        "Content-Type": "application/json"
    }

    max_retries = 3
    for attempt in range(max_retries):
        try:
            response = requests.post(API_URL, json=payload,
                                     headers=headers, timeout=30)

            if response.status_code == 200:
                return base64.b64decode(response.json()["base64FileString"])

            if response.status_code == 401:
                raise ValueError("Authentication failed: re-check client_id and client_secret")

            if response.status_code == 400:
                msg = response.json().get("message", "Bad request")
                raise ValueError(f"Request error: {msg}")

            if response.status_code >= 500:
                if attempt < max_retries - 1:
                    wait = 2 ** attempt
                    print(f"Server error ({response.status_code}), retrying in {wait}s...")
                    time.sleep(wait)
                    continue
                raise RuntimeError(f"Server error after {max_retries} attempts")

        except requests.exceptions.Timeout:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
                continue
            raise

    raise RuntimeError("Max retries exceeded")

The wrapper raises immediately on 4xx responses because retrying a credential error or a malformed request produces the same result. Exponential backoff applies only to 5xx responses and timeouts, where the issue is transient.

Once generate_document() returns raw PDF bytes, routing them downstream takes three lines:

import boto3

s3 = boto3.client("s3")
pdf_bytes = generate_document(CLIENT_ID, CLIENT_SECRET, "invoice_template.docx", document_values)
s3.put_object(Bucket="my-documents-bucket", Key="invoices/INV-2025-0042.pdf", Body=pdf_bytes)

To attach the output to an email, pass pdf_bytes directly as the smtplib attachment payload. To collect a signature on the generated document, base64-encode the bytes and POST them to Foxit’s eSign API with the signer’s email address in the request body. The full eSign API reference is at docs.developer-api.foxit.com.

Common Mistakes

A short list of the issues that account for almost every failed first run.

Smart-quote autocorrect on braces. Word’s AutoCorrect can convert the second { of {{ into a curly-quote glyph, which breaks tag parsing silently. Disable “Straight quotes with smart quotes” under AutoCorrect Options, or paste tags as plain text.
Token case sensitivity. {{Customer_Name}} and {{customer_name}} are different keys. Match the casing in your JSON exactly.
TableStart and TableEnd must sit in the same Word table row. Splitting them across two rows, or placing either marker outside the table, leaves the loop unrendered with no error.
Template over 4 MB. The API rejects oversize uploads with a 400. Compress embedded images, drop embedded fonts where system fonts will do, or split the template into smaller pieces.
Missing payload key. The API renders an unmatched tag as an empty string rather than failing, so a 200 response does not guarantee every field is populated. Spot-check the rendered PDF as part of any pipeline test.
Auth header typos. Headers are client_id and client_secret in snake_case. Client-Id, ClientId, or X-Client-Id all return 401.

Run the Full Invoice Example End-to-End Right Now

Create a free account directly at account.foxit.com/site/sign-up. This skips the pricing-page redirect you hit from the marketing site and drops you straight into the account form.

Open account.foxit.com/site/sign-up and complete the form (no credit card required).
After verification, sign in to the Developer Portal and the Developer plan (500 credits per year) is active by default.
Open the API Keys section and copy your client_id and client_secret.

With credentials in hand, run the example end-to-end:

Download invoice_full.docx from the foxit-demo-templates repo and save it locally as invoice_template.docx in your working directory. The file is well under the 4 MB upload limit and exercises every tag pattern this article covers.
Paste your credentials into the CLIENT_ID and CLIENT_SECRET variables in the Python script from the previous section.
Edit the document_values dictionary with your own customer name, invoice number, and line items.
Run the script and open invoice_output.pdf.

The free Developer plan’s 500 annual credits cover this tutorial dozens of times over before you spend anything. The full API reference at docs.developer-api.foxit.com covers every endpoint parameter, the complete tag specification, all supported output formats, and the full GenerateDocumentBase64 request and response schema.

Get started with a free account (no credit card required) and generate your first dynamic PDF in under 10 minutes.

Generate Dynamic PDFs from JSON using Foxit APIs

See how easy it is to generate PDFs from JSON using Foxit’s Document Generation API. With Word as your template engine, you can dynamically build invoices, offer letters, and agreements—no complex setup required. This tutorial walks through the full process in Python and highlights the flexibility of token-based document creation.

Generate Dynamic PDFs from JSON using Foxit APIs

One of the more fascinating APIs in our library is the Document Generation API. This document generation API lets you create dynamic PDFs or Word documents using your own data as templates. That may sound simple – and the code you’re about to see is indeed simple – but the real power lies in how flexible Word can be as a template engine. This API could be used for:

Creating invoices
Creating offer letters
Creating dynamic agreements (which can integrate with our eSign API)

All of this is made available via a simple API and a “token language” you’ll use within Word to create your templates. Whether you’re feeding in data from a database, a form submission, or a JSON API response, the process looks the same from your Python script. Let’s take a look at how this is done.

Credentials

Before we go any further, head over to our developer portal and grab a set of free credentials. This will include a client ID and secret values – you’ll need both to make use of the API.

Don’t want to read all of this? You can also follow along by video:

Using the API

The Document Generation API flow is a bit different from our PDF Services APIs in that the execution is synchronous. You don’t need to upload your document beforehand or download a result. You simply call the API (passing your data and template) and the result has your new PDF (or Word document). With it being this simple, let’s get into the code.

Loading Credentials

My script begins by loading in the credentials and API root host via the environment:

CLIENT_ID = os.environ.get('CLIENT_ID')
CLIENT_SECRET = os.environ.get('CLIENT_SECRET')
HOST = os.environ.get('HOST')

As always, try to avoid hard coding credentials directly into your code.

Calling the API

The endpoint only requires you to pass the output format, your data, and a base64 version of your file. “Your data” can be almost anything you like—though it should start as an object (i.e., a dictionary in Python with key/value pairs). Beneath that, anything goes: strings, numbers, arrays of objects, and so on.

Here’s a Python wrapper showing this in action:

def docGen(doc, data, id, secret):
    
    headers = {
        "client_id":id,
        "client_secret":secret
    }

    body = {
        "outputFormat":"pdf",
        "documentValues": data,  
        "base64FileString":doc
    }

    request = requests.post(f"{HOST}/document-generation/api/GenerateDocumentBase64", json=body, headers=headers)
    return request.json()

And here’s an example calling it:

with open('../../inputfiles/docgen_sample.docx', 'rb') as file:
    bd = file.read()
    b64 = base64.b64encode(bd).decode('utf-8')

data = {
    "name":"Raymond Camden", 
    "food": "sushi",
    "favoriteMovie": "Star Wars",
    "cats": [
        {"name":"Elise", "gender":"female", "age":14 },
        {"name":"Luna", "gender":"female", "age":13 },
        {"name":"Crackers", "gender":"male", "age":13 },
        {"name":"Gracie", "gender":"female", "age":12 },
        {"name":"Pig", "gender":"female", "age":10 },
        {"name":"Zelda", "gender":"female", "age":2 },
        {"name":"Wednesday", "gender":"female", "age":1 },
    ],
}

result = docGen(b64, data, CLIENT_ID, CLIENT_SECRET)

You’ll note here that my data is hard-coded. In a real application, this would typically be dynamic—read from the file system, queried from a database, or sourced from any other location.

The result object contains a message representing the success or failure of the operation, the file extension for the result, and the base64 representation of the result. To turn that base64 string back into a file, decode it first:

b64_bytes = result["base64FileString"].encode('ascii')
binary_data = base64.b64decode(b64_bytes)

Most likely you’ll always be outputting PDFs, so here’s a simple bit of code that stores the result:

with open('../../output/docgen_sample.pdf', 'wb') as file:
    file.write(binary_data)
    print('Done and stored to ../../output/docgen_sample.pdf')

There’s a bit more to the API than I’ve shown here so be sure to check the docs, but now it’s time for the real star of this API, Word.

Using Word as a Template

I’ve probably used Microsoft Word for longer than you’ve been alive and I’ve never really thought much about it. But when you begin to think of a simple Word document as a template, all of a sudden the possibilities begin to excite you. In our Document Generation API, the template system works via simple “tokens” in your document marked by opening and closing double brackets.

Consider this block of text:

See how name is surrounded by double brackets? And food and favoriteMovie? When this template is sent to the API along with the corresponding values, those tokens are replaced dynamically. In the screenshot, notice how favoriteMovie is bolded. That’s fine. You can use any formatting, styling, or layout options you wish.

That’s one example, but you also get some built-in values as well. For example, including today as a token will insert the current date, and can be paired with date formatting to specify how the date looks:

Remember the array of cats from earlier? You can use that to create a table in Word like this:

Notice that I’ve used two new tags here, TableStart and TableEnd, both of which reference the array, cats. Then in my table cells, I refer to the values from that array. Again, the color you see here is completely arbitrary and was me making use of the entirety of my Word design skills.

Here’s the template as a whole to show you everything in context:

The Result

Given the code shown above with those values, and given the Word template just shared, once passed to the API, the following PDF is created:

What About Converting PDF to JSON?

So far we’ve been going one direction: JSON data in, PDF out. But what if you need to go the other way—extract structured content from a PDF and work with it in your application?

Foxit’s PDF Services API includes an Extract endpoint that handles exactly this. You upload a PDF, specify whether you want TEXT, IMAGE, or PAGE-level data, and the API returns the extracted content. The text output is particularly useful if you want to feed the result into a data pipeline, search index, or AI workflow.

Here’s a quick look at how extraction works in Python. First, upload your PDF:

def uploadDoc(path, id, secret):
    headers = {
        "client_id":id,
        "client_secret":secret
    }
    with open(path, 'rb') as f:
        files = {'file': (path, f)}
        request = requests.post(f"{HOST}/pdf-services/api/documents/upload", files=files, headers=headers)
    return request.json()

doc = uploadDoc("../../inputfiles/input.pdf", CLIENT_ID, CLIENT_SECRET)

Then call the Extract endpoint with the document ID and the type of content you want. The result comes back in a structured format you can parse, store, or pass along to other tools—including an LLM if you’re building an AI document pipeline.

You can read a full walkthrough in our PDF text extraction guide.

Ready to Try?

If this looks cool, be sure to check the docs for more information about the template language and API. Sign up for some free developer credentials and reach out on our developer forums with any questions.

If you’re building AI agents or LLM-powered workflows, Foxit also offers an MCP server that lets you connect your agents directly to Foxit PDF Services—so your AI tools can generate, extract, and process documents without any custom glue code.

Want the code? Get it on GitHub (Python).

If you are more of a Node person, check out that version. Get it on GitHub (Node.js).

Building Auditable, AI-Driven Document Workflows with Foxit APIs

We had an incredible time at API World 2025 connecting with developers, sharing ideas, and seeing how Foxit APIs power everything from AI-driven resume builders to interactive doodle apps. In this post, we’ll walk through the same hands-on workflow Jorge Euceda demoed live on stage—showing how to build an auditable, AI-powered document automation system using Foxit PDF Services and Document Generation APIs.

How to Build an AI Resume Analyzer with Python & Foxit APIs (API World 25′)

This year’s API World was packed with energy—and it was amazing meeting so many developers face-to-face at the Foxit booth. We spent three days trading ideas about document automation, AI workflows, and integration challenges.

Our team hosted a hands-on workshop and sponsored the API World Hackathon, where developers submitted 16 high-quality projects built with Foxit APIs. Submissions ranged from:

Automated legal-advice generators
Compatibility-rating apps that analyze your personality match
AI-powered resume optimizers that tailor your CV to dream-job descriptions
Collaborative doodle games that turn drawings into shareable PDFs

Each project offered a new perspective on what’s possible with Foxit APIs—and we loved seeing the creativity.

Among all the sessions, Jorge Euceda’s workshop stood out as a crowd favorite. It showed how to make AI document decisions auditable, explainable, and replayable using event sourcing and two key Foxit APIs. That’s exactly what we’ll walk through below.

Replicate the Full Demo

Click here to grab the project overview file.

Prefer to follow along with the live session instead of reading step-by-step?
Watch Jorge’s complete “AI-Powered Resume to Report” presentation from API World 2025.
It includes every step shown below—plus real-time API responses.

What You’ll Build

A complete, auditable workflow:

Resume Upload → Extract Resume Data → AI Candidate Scoring → Generate HR Report → Event Store

This workshop is designed for technical professionals and managers who want to learn how to use application programming interfaces (APIs) and explore how AI can enhance document workflows. Attendees will get hands-on experience with Foxit’s PDF Services (extraction/OCR) and Document Generation APIs, and see how event sourcing turns AI decisions into an auditable, replayable ledger.

By the end, you’ll have a Python-based demo that extracts data from a PDF resume, analyzes it against a policy, and generates a polished HR Report PDF with a traceable event log.

Getting Set Up

To follow along, you’ll need:

Access to a terminal with a Python 3.9+ Environment and internet connectivity
Visual Studio Code or your preferred IDE
Basic familiarity with REST/JSON (helpful but not required)

Install Dependencies

python -V
# virtual environment setup, requests installation
python3 -m venv myenv
source myenv/bin/activate
pip3 install requests

Download the project’s zip file below

Project Source Code

Now extract the files somewhere in your computer, open in Visual Studio Code or your preferred IDE.

You may use any sample resume PDF for inputs/input_resume.pdf. A sample one is provided, but you may leverage any resume PDF you wish to generate a report on.

Create a Foxit Account for credentials

Create a Free Developer Account now or navigate to our getting started guide, which will go over how to create a free trial.

Hands-On Walkthrough

Step 1 – Open the Project

Now that you’ve downloaded the workshop source code, navigate to the resume_to_report.py file, which will serve as our main entry point.

Once dependencies are installed and the ZIP file extracted, open your workspace and run:

python3 resume_to_report.py

You should see console logs showing:

An AI Report printed as JSON
A generated PDF (outputs/HR_Report.pdf)
An event ledger (outputs/events.json) with traceable actions

Step 2 — Inspect the outputs

Open the generated HR report to review:

Candidate name and phone
Overall fit score
Matching skills & gaps
Summary and policy reference in the footer

Then open events.json to see your audit trail—each entry captures the AI’s decision context.

{
  "eventType": "DecisionProposed",
  "traceId": "8d1e4df6-8ac9-4f31-9b3a-841d715c2b1c",
  "payload": {
    "fitScore": 82,
    "policyRef": "EvaluationPolicy#v1.0"
  }
}

This is your audit trail.

Step 3 — Replay & Explain a Policy Change

Replay demonstrates why event-sourcing matters:

Edit inputs/evaluation_policy.json: add a hard requirement (e.g., "kubernetes") or adjust the job_description emphasis.
Re-run the script with the same resume.
Compare:
- New decision and updated PDF content
- Event log now reflects the updated rationale (PolicyLoaded snapshot → new DecisionProposed with the same traceId lineage)
Emphasize: The input resume hasn’t changed; only policy did — the event ledger explains the difference.

Policy: Drive Auditable & Replayable Decisions

The AI assistant uses a JSON policy file to control how it scores, caps, and summarizes results. Every policy snapshot is logged as its own event, creating a replayable audit trail for governance and compliance.

{
  "policyId": "EvaluationPolicy#v1.0",
  "job_description": "Looking for a software engineer with expertise in C++, Python, and AWS cloud services. Experience building scalable applications in agile teams; familiarity with DevOps and CI/CD.",
  "overall_summary": "Make the summary as short as possible",
  "hard_requirements": ["C++", "python", "aws"]
}

Notes:

policyId appears in both the report and event log.
job_description defines what the AI is looking for.
Changing these values creates a new traceable event.

Generate a Polished Report

Next, use the Foxit Document Generation API to fill your Word template and create a formatted PDF report.

Open inputs/hr_report_template.docx, you will find the following HR reporting template with placeholders for the fields we will be entering:

Tips:

Include lightweight branding (logo/header) to make the generated PDF presentation-ready.
Include a footer with traceable Policy ID and Trace ID Events

Results and Audit Trail

Here’s what the final HR Report PDF looks like:

Every decision has a Trace ID and Policy Ref, so you can recreate the report at any time and verify how the AI arrived there.

Why Event-Sourced AI Matters

This pattern does more than score resumes—it proves that AI decisions can be transparent, deterministic, and trustworthy.
By using Foxit APIs to extract, analyze, and generate documents, developers can bring auditability to any workflow that relies on machine logic.

Key Takeaways

Auditability – Every AI step emits a verifiable event.
Replayability – Change a policy and regenerate for deterministic results.
Explainability – Decisions carry policy and trace references for clear “why.”
Automation – PDF Services and Document Generation handle the document lifecycle end-to-end.

Try It Yourself

Ready to build your own auditable AI workflow?

Demo and Source Code: document-workflows-with-foxit.pages.dev
Foxit Developer Portal: developer-api.foxit.com
API Docs: docs.developer-api.foxit.com
Watch the Full Presentation: Euceda’s API World session

Closing Thought

At API World, we set out to show how Foxit APIs can power real, transparent AI workflows—and the community response was incredible. Whether you’re building for HR, legal, finance, or creative industries, the same pattern applies:

Make your AI explain itself.

Start with the Foxit APIs, experiment with policies, and turn every AI decision into a traceable event that builds trust.

Convert Office Docs to PDFs Automatically with Foxit PDF Services API

See how to build a powerful, automated workflow that converts Office documents (Word, Excel, PowerPoint) into PDFs. This step-by-step guide uses the Foxit PDF Services API, the Pipedream low-code platform, and Dropbox to create a seamless “hands-off” document processing system. We’ll walk through every step, from triggering on a new file to uploading the final PDF.

Convert Office Docs to PDFs Automatically with Foxit PDF Services API

With our REST APIs, it is now possible for any developer to set up an integration and document workflow using their language of choice. But what about workflow automations? Luckily, this is even simpler (of course, depending on platform) as you can rely on the workflow service to handle a lot the heavy lifting of whatever automation needs you may have. In this blog post, I’m going to demonstrate a workflow making use of Pipedream. Pipedream is a low-code platform that lets you build flexible workflows by piecing together various small atomic steps. It’s been a favorite of mine for some time now, and I absolutely recommend it. But note that what I’ll be showing here today could absolutely be done on other platforms, like n8n.

Want the televised version? Catch the video below:

Our Office Document to PDF Workflow

Our workflow is based on Dropbox folders and handles automatic conversion of Office docs to PDFs. To support that, it does the following:

Listen for new files in a Dropbox folder
Do a quick sanity check (is it in the input subdirectory and an Office file)
Download the file to Pipedream
Send it to Foxit via the Upload API
Kick off the appropriate conversion based on the Office type
Check status via the Status API
When done, download the result to Pipedream
And finally, push it up to Dropbox in an output subdirectory

Here’s a nice graphical representation of this workflow:

Before we get into the code, note that workflow platforms like Pipedream are incredibly flexible. When I build workflows with platforms like this I try to make each step as atomic, and focused as possible. I could absolutely have built a shorter, more compact version of this workflow. However, having it broken out like this makes it easier to copy and modify going forward (which is exactly how this one came about, it was based on a simpler, earlier version).

Ok, let's break it down, step-by-step.

Getting Triggered

In Pipedream, workflows begin with a trigger. While there are many options for this, my workflow uses a "New File From Dropbox" trigger. I logged into Dropbox via Pipedream so it had access to my account. I then specified a top level folder, "Foxit", for the integration. Additionally, there are two more important settings:

Recursive – this tells the trigger to file for any new file under the root directory, "Foxit". My Dropbox Foxit folder has both an input and output directory.
Include Link – this tells Pipedream to ensure we get a link to the new file. This is required to download it later.

Filtering the Document Flow

The next two steps are focused on filtering and stopping the workflow, if necessary. The first, end_if_output, is a built-in Pipedream step that lets me provide a condition for the workflow to end. First, I'll check the path value from the trigger (the path of the new file) and if it contains "output", this means it's a new file in the output directory and the workflow should not run.

The next filter is a code step that handles two tasks. First, it checks whether the new file is a supported Office type—.docx, .xlsx, or .pptx—using our APIs. If the extension isn’t one of these, the workflow ends programmatically.

Later in the workflow, I’ll also need that same extension to route the request to the correct endpoint. So the code handles both: validation and preservation of the extension.

import os 

def handler(pd: "pipedream"):
  base, extension = os.path.splitext(pd.steps['trigger']['event']['name'])

  if extension == ".docx":
    api = "/pdf-services/api/documents/create/pdf-from-word"
  elif extension == ".xlsx":
    api = "/pdf-services/api/documents/create/pdf-from-excel"
  elif extension == ".pptx":
    api = "/pdf-services/api/documents/create/pdf-from-ppt"
  else:
    return pd.flow.exit(f"Exiting workflow due to unknow extension: {extension}.")

  return { "api":api }

As you can see, if the extension isn't valid, I'm exiting the workflow using pd.flow.exit (while also logging out a proper message, which I can check later via the Pipedream UI). I also return the right endpoint if a supported extension was used. This will be useful later in the flow.

Download and Upload API Data

The next two steps are primarily about moving data from the input source (Dropbox) to our API (Foxit).

The first step, download_to_tmp, uses a simple Python script to transfer the Dropbox file into the /tmp directory for use in the workflow

import requests

def handler(pd: "pipedream"):
    download_url = pd.steps["trigger"]["event"]["link"]
    file_path = f"/tmp/{pd.steps['trigger']['event']['name']}"

    with requests.get(download_url, stream=True) as response:
      response.raise_for_status()
      with open(file_path, "wb") as file:
          for chunk in response.iter_content(chunk_size=8192):
            file.write(chunk)
            
    return file_path

Notice at the end that I return the path I used in Pipedream. This action then leads directly into the next step of uploading to Foxit via the Upload API:

import os 
import requests 

def handler(pd: "pipedream"):
  clientid = os.environ.get('FOXIT_CLIENT_ID')
  secret = os.environ.get('FOXIT_CLIENT_SECRET')
  HOST = os.environ.get('FOXIT_HOST')
  
  headers = {
    "client_id":clientid,
    "client_secret":secret
  }

  with open(pd.steps['download_to_tmp']['$return_value'], 'rb') as f:
    files = {'file': (pd.steps['download_to_tmp']['$return_value'], f)}

    request = requests.post(f"{HOST}/pdf-services/api/documents/upload", files=files, headers=headers)

    return request.json()

The result of this will be a documentId value that looks like so:

{
  "documentId": "<string>"
}

Pipedream lets you define environment variables and I've made use of them for my Foxit credentials and host. Grab your own free credentials here!

Converting the Document Using the Foxit API

The next step will actually kick off the conversion. My workflow supports three different input types (Word, PowerPoint, and Excel). These map to three API endpoints. But remember that earlier we sniffed the extension of our input and set the endpoint there. Since all three APIs work the same, that's literally all we need to do – hit the endpoint and pass the document value from the previous step.

import os 
import requests 

def handler(pd: "pipedream"):

  clientid = os.environ.get('FOXIT_CLIENT_ID')
  secret = os.environ.get('FOXIT_CLIENT_SECRET')
  HOST = os.environ.get('FOXIT_HOST')
  
  headers = {
    "client_id":clientid,
    "client_secret":secret,
    "Content-Type":"application/json"
  }

  body = {
    "documentId": pd.steps['upload_to_foxit']['$return_value']['documentId']
  }

  api = pd.steps['extension_check']['$return_value']['api']
  
  print(f"{HOST}{api}")
  request = requests.post(f"{HOST}{api}", json=body, headers=headers)
  return request.json()

The result of this call, and nearly all of the Foxit APIs, will be a task:

{
  "taskId": "<string>"
}

Checking Your Document API Status

The next step is one that may take a few seconds – checking the job status. Foxit's endpoint returns a value like so:

{
  "taskId": "<string>",
  "status": "<string>",
  "progress": "<int32>",
  "resultDocumentId": "<string>",
  "error": {
    "code": "<string>",
    "message": "<string>"
  }
}

To use this, I just hit the API, check for status, and if it’s not done, wait five seconds and call it again. Here’s the Python code for this:

import os 
import requests 
from time import sleep 

def handler(pd: "pipedream"):

  clientid = os.environ.get('FOXIT_CLIENT_ID')
  secret = os.environ.get('FOXIT_CLIENT_SECRET')
  HOST = os.environ.get('FOXIT_HOST')
  
  headers = {
    "client_id":clientid,
    "client_secret":secret,
    "Content-Type":"application/json"
  }

  done = False
  while done is False:

    request = requests.get(f"{HOST}/pdf-services/api/tasks/{pd.steps['create_conversion_job']['$return_value']['taskId']}", headers=headers)
    status = request.json()
    if status["status"] == "COMPLETED":
      done = True
      return status
    elif status["status"] == "FAILED":
      print("Failure. Here is the last status:")
      print(status)
      return pd.flow.exit("Failure in job")
    else:
      print(f"Current status, {status['status']}, percentage: {status['progress']}")
      sleep(5)

As shown, errors are simply logged by default—but you could enhance this by adding notifications, such as emailing an admin, sending a text message, or other alerts.

On success, the final output is passed along, including the key value we care about: resultDocumentId.

Download and Upload – Again

Ok, if the workflow has gotten this far, it's time to finish the process. The next step handles downloading the result from Foxit using the download endpoint:

import requests
import os

def handler(pd: "pipedream"):
  clientid = os.environ.get('FOXIT_CLIENT_ID')
  secret = os.environ.get('FOXIT_CLIENT_SECRET')
  HOST = os.environ.get('FOXIT_HOST')

  headers = {
    "client_id":clientid,
    "client_secret":secret,
  }

  # Given a file of input.docx, we need to use input.pdf
  base_name, _ = os.path.splitext(pd.steps['trigger']['event']['name'])
  path = f"/tmp/{base_name}.pdf"
  print(path) 
  
  with open(path, "wb") as output:
		
    bits = requests.get(f"{HOST}/pdf-services/api/documents/{pd.steps['check_job']['$return_value']['resultDocumentId']}/download", stream=True, headers=headers).content 
    output.write(bits)
            
    return {
      "filename":f"{base_name}.pdf",
      "path":path
    }

Note that I'm using the base name of the input, which is basically the filename minus the extension. So for example, input.docx will become input, which I then slap a pdf extension on to create the filename used to store locally to Pipedream.

Finally, I push the file back up to Dropbox, but for this, I can use a built-in Pipedream step that can upload to Dropbox. Here's how I configured it:

Path: Once again, Foxit
File Name: This one's a bit more complex, I want to store the value in the output subdirectory, and ensure the filename is dynamic. Pipedream lets you mix and match hard-coded values and expressions. I used this to enable that: output/{{steps.download_result_to_tmp.$return_value.filename}}. In this expression the portion inside the double bracket will be dynamic based on the PDF file generated previously.
File Path: This is an expression as well, pointing to where I saved the file previously: {{steps.download_result_to_tmp.$return_value.path}}
Mode: Finally, the mode attribute specifies what to do on a conflict. This setting will be based on whatever your particular workflow needs are, but for my workflow, I simply told Dropbox to overwrite the existing file.

Here's how that step looks configured in Pipedream:

Conclusion

Believe it or not, that's the entire workflow. Once enabled, it runs in the back ground and I can simply place any files into my Dropbox folder and my Office docs will be automatically converted. What's next? Definitely get your own free credentials and check out the docs to get started. If you run into any trouble at all, hit is up on the forums and we'll be glad to help!

Introducing PDF APIs from Foxit

Get started with Foxit’s new PDF APIs—convert Word to PDF, generate documents, and embed files using simple, scalable REST APIs. Includes sample Python code and walkthrough.

Introducing PDF APIs from Foxit

At the end of June, Foxit introduced a brand-new suite of tools to help developers work with documents. These APIs cover a wide range of features, including:

- Convert between Office document formats and PDF files seamlessly
- Optimize, manipulate, and secure PDFs with advanced APIs
- Generate dynamic documents using Microsoft Word templates
- Extract text and images from PDFs with powerful tools
- Embed PDFs into web pages in a context-aware, controlled manner
- Integrate with eSign APIs for streamlined signature workflows

These APIs are simple to use, and best of all, follow the “don’t surprise me” principal of development. In this post, I’m going to demonstrate one simple example – converting a Word document to PDF – but you can rest assured that nearly all the APIs will follow incredibly similar patterns. I’ll be using Python for my examples here, but will link to a Node.js version of the same example. And given that we’re talking REST APIs here, any language is welcome to join the document party. Let’s dive in.

Credentials

Before we go any further, head over to our developer portal and grab a set of free credentials. This will include a client ID and secret values you’ll need to make use of the API.

Don’t want to read all of this? You can also follow along by video:

API Flow

As I mentioned above, most of the PDF Services APIs will follow a similar flow. This comes down to:

Upload your input (like a Word document)
Kick off a job (like converting to PDF)
Check the job (hey, how ya doin?)
Download the result

Or, in pretty graphical format –

The great thing is, once you’ve completed one integration (this post focuses on converting Word to PDF), switching to another is easy—and much of your existing code can be reused. A lazy developer is happy developer! Let’s get started.

Loading Credentials

My script begins by loading the credentials and API root host via the environment:

CLIENT_ID = os.environ.get('CLIENT_ID')
CLIENT_SECRET = os.environ.get('CLIENT_SECRET')
HOST = os.environ.get('HOST')

It’s never a good idea to hard-code credentials in your code. But if you do it this one time, I won’t tell. Honest.

Uploading Your Input

As I mentioned, in this example we’ll be making use of the Word to PDF API. Our input will be a Word document, which we’ll upload to Foxit using the upload API. This endpoint is fairly simple – aside from your credentials, all you need to provide is the binary data of the input file. Here’s the method I created to make this process easier:

def uploadDoc(path, id, secret):
    
    headers = {
        "client_id":id,
        "client_secret":secret
    }

    with open(path, 'rb') as f:
        files = {'file': (path, f)}

        request = requests.post(f"{HOST}/pdf-services/api/documents/upload", files=files, headers=headers)
        return request.json()

And here’s how it’s used:

doc = uploadDoc("../../inputfiles/input.docx", CLIENT_ID, CLIENT_SECRET)
print(f"Uploaded doc to Foxit, id is {doc['documentId']}")

The upload API only returns one value, a documentId, which we can use in future calls.

Starting the Job

Each API operation is a job creator. By this I mean you call the endpoint and it begins your action. For Word to PDF, the only required input is the document ID from the previous call. We can build a nice little wrapper function like so:

def convertToPDF(doc, id, secret):
    
    headers = {
        "client_id":id,
        "client_secret":secret,
        "Content-Type":"application/json"
    }

    body = {
        "documentId":doc	
    }

    request = requests.post(f"{HOST}/pdf-services/api/documents/create/pdf-from-word", json=body, headers=headers)
    return request.json()

And then call it like so:

task = convertToPDF(doc["documentId"], CLIENT_ID, CLIENT_SECRET)
print(f"Created task, id is {task['taskId']}")

The result of this call, if no errors were found, isa taskId. We can use this to gauge how the job’s performing. Let’s do that now.

Job Checking

Ok, so the next part can be a bit tricky depending on your language of choice. We need to use the task status endpoint to determine how the job is performing. How often we do this, how quickly and so forth, will depend on your platform and needs. For our little sample script here, everything is running at once. I wrote a function that will check the status. If the job isn’t finished (whether successful or not), it pauses briefly before trying again. While this approach isn’t the most sophisticated, it should work well enough for basic testing:

def checkTask(task, id, secret):

    headers = {
        "client_id":id,
        "client_secret":secret,
        "Content-Type":"application/json"
    }

    done = False
    while done is False:

        request = requests.get(f"{HOST}/pdf-services/api/tasks/{task}", headers=headers)
        status = request.json()
        if status["status"] == "COMPLETED":
            done = True
            # really only need resultDocumentId, will address later
            return status
        elif status["status"] == "FAILED":
            print("Failure. Here is the last status:")
            print(status)
            sys.exit()
        else:
            print(f"Current status, {status['status']}, percentage: {status['progress']}")
            sleep(5)

As you can see, I’m using a while loop that—at least in theory—will continue running until a success or failure response is returned, with a five-second pause between each call. You can adjust that interval as needed—test different values to see what works best for your use case. Typically, most API calls should complete in under ten seconds, so a five-second delay felt like a reasonable default.

Each call to the endpoint returns a task status result. Here’s an example:

{
    'taskId': '685abc95a0d113558e4204d7', 
    'status': 'COMPLETED', 
    'progress': 100, 
    'resultDocumentId': '685abc952475582770d6917b'
}

The important part here is the status. But you could also use progress to give some feedback to the code waiting for results. Here’s my code calling this:

result = checkTask(task["taskId"], CLIENT_ID, CLIENT_SECRET)
print(f"Final result: {result}")

Downloading Your Result

The last piece of the puzzle is simply saving the result. If you noticed above, the task returned a resultDocumentId value. Taking that, and the [Download Document](NEED LINK) endpoint, we can build a utility to store the result like so:

def downloadResult(doc, path, id, secret):
    
    headers = {
        "client_id":id,
        "client_secret":secret
    }

    with open(path, "wb") as output:
        
        bits = requests.get(f"{HOST}/pdf-services/api/documents/{doc}/download", stream=True, headers=headers).content 
        output.write(bits)

And finally, call it:

downloadResult(result["resultDocumentId"], "../../output/input.pdf", CLIENT_ID, CLIENT_SECRET)
print("Done and saved to: ../../output/input.pdf")

And that’s it! While this script could certainly benefit from more robust error handling, it demonstrates the basic flow. As mentioned, most of our APIs follow this same logic.

Next Steps

Want the complete scripts? Get it on GitHub.

Want it in Node.js? Get it on GitHub.

Rather try this yourself? Sign up for a free developer account now. Need help? Head over to our developer forums and post your questions and comments.

Category: Automated Document Pipelines

Prerequisites

How the Auth Model Works

Step 1 and Step 2: Upload the DOCX and Initiate Conversion

Step 1: Upload the DOCX File

Step 2: Initiate the PDF Conversion

Step 3: Polling the Task Status

Step 4: Downloading the Converted PDF

Error Handling for Production

The Complete Script

DOCX to PDF API FAQ

What Document Generation Is

Why Manual Document Workflows Break Under Load

How Template-Driven Generation Works: Tokens, Loops, and Conditionals

The API Pipeline: From POST Request to Final Document

Where Organizations Are Actually Using This

What to Evaluate When Choosing a Document Generation API

Getting Started: Generate Your First Document Today

Document Generation Pipeline FAQ

Architecture at a Glance

The Quality Gap

How the Pipeline Works

Credentials and Setup

Building the PDF Translation Pipeline

Scoring and Routing

Layout Preservation

Limitations to Test

Try It Now

PDF Translation API FAQ

How Foxit’s PDF Structural Extraction Engine Works Under the Hood

The Full Object Map: All 12 Extractable PDF Element Types

API Walkthrough: The Four-Step Async PDF Extraction Flow

Wiring Extracted PDF Data Into Your Workflow

PDF Extraction Tools Compared: Foxit vs. Adobe, Google, Amazon, and Azure

Your First PDF Extraction API Call, Right Now

PDF Structural Extraction FAQ

MCP Architecture in 90 Seconds

Prerequisites and Configuration

PDF Services MCP Tools: Full Catalog

Extending to eSign: Foxit’s Signing API as a Complementary REST Layer

End-to-End Workflow: AI Agent Automates a Sales Contract

Production Considerations: Error Handling, Rate Limits, and Data Governance

PDF API MCP Server FAQs

Run Your First Tool Call Now

How the Foxit DocGen API Works

Build a Word Template with DocGen Tags

Make Your First API Call: Generate a PDF from JSON

Advanced Data Scenarios: Arrays, Nested Objects, and Built-In Functions

Error Handling and Production Readiness

Common Mistakes

Run the Full Invoice Example End-to-End Right Now

Generate Dynamic PDFs from JSON using Foxit APIs

Credentials

Using the API

Loading Credentials

Calling the API

Using Word as a Template

The Result

What About Converting PDF to JSON?

Ready to Try?

How to Build an AI Resume Analyzer with Python & Foxit APIs (API World 25′)

Replicate the Full Demo

What You’ll Build

Getting Set Up

Hands-On Walkthrough

Step 1 – Open the Project

Step 2 — Inspect the outputs

Step 3 — Replay & Explain a Policy Change

Policy: Drive Auditable & Replayable Decisions

Generate a Polished Report

Results and Audit Trail

Why Event-Sourced AI Matters

Try It Yourself

Closing Thought

Convert Office Docs to PDFs Automatically with Foxit PDF Services API

Our Office Document to PDF Workflow

Getting Triggered

Filtering the Document Flow

Download and Upload API Data

Converting the Document Using the Foxit API