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Automate File-to-Markdown Conversion with the Free tomdnow API

Use the free tomdnow API to programmatically convert PDF, DOCX, HWP, and 20+ formats to Markdown. Includes curl and Python examples.

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Why Use an API for Markdown Conversion?

Converting a single file through the web interface is easy. But what about converting 500 PDF reports? Or automatically converting every document uploaded to your internal system? Or building a pipeline that feeds converted Markdown directly into an AI tool?

That is where the tomdnow API comes in. It is free, requires no credit card, and supports the same 20+ file formats as the web interface. You get 50 conversions per day on the free tier.

Getting Your API Key

Before making any conversion requests, you need an API key. Getting one takes a single HTTP request:

curl -X POST https://api.tomdnow.com/api/v1/keys \
  -H "Email: you@example.com"

The response includes your API key and tier information:

{
  "api_key": "tmd_abc123...",
  "tier": "free",
  "daily_limit": 50
}

Save your API key securely. You will include it in every subsequent request using the X-Api-Key header.

Converting a File with curl

The conversion endpoint accepts a file upload and returns the Markdown content along with metadata. Here is a basic example:

curl -X POST https://api.tomdnow.com/api/v1/convert \
  -H "X-Api-Key: tmd_your_api_key" \
  -F "file=@document.pdf"

The response contains the converted Markdown and file metadata:

{
  "markdown": "# Document Title\n\nConverted content here...",
  "metadata": {
    "title": "document",
    "type": "pdf",
    "size": 102400
  },
  "api_usage": {
    "tier": "free",
    "daily_remaining": 49
  }
}

You can convert any supported format by changing the file. The API automatically detects the file type:

# Convert a Word document
curl -X POST https://api.tomdnow.com/api/v1/convert \
  -H "X-Api-Key: tmd_your_api_key" \
  -F "file=@report.docx"

# Convert a Korean HWP document
curl -X POST https://api.tomdnow.com/api/v1/convert \
  -H "X-Api-Key: tmd_your_api_key" \
  -F "file=@공문서.hwp"

# Convert an Excel spreadsheet
curl -X POST https://api.tomdnow.com/api/v1/convert \
  -H "X-Api-Key: tmd_your_api_key" \
  -F "file=@data.xlsx"

Converting Files with Python

For more complex workflows, Python with the requests library is a natural fit. Here is a function that converts a single file:

import requests

API_KEY = "tmd_your_api_key"
API_URL = "https://api.tomdnow.com/api/v1/convert"

def convert_to_markdown(file_path: str) -> str:
    """Convert a file to Markdown using the tomdnow API."""
    with open(file_path, "rb") as f:
        response = requests.post(
            API_URL,
            headers={"X-Api-Key": API_KEY},
            files={"file": f},
        )
    response.raise_for_status()
    return response.json()["markdown"]

# Usage
markdown = convert_to_markdown("report.pdf")
print(markdown)

Batch Processing Multiple Files

The real power of the API is batch conversion. Here is a Python script that converts every file in a directory and saves the Markdown output:

import os
import requests
from pathlib import Path

API_KEY = "tmd_your_api_key"
API_URL = "https://api.tomdnow.com/api/v1/convert"

SUPPORTED = {
    ".pdf", ".docx", ".pptx", ".xlsx", ".xls",
    ".hwp", ".hwpx", ".html", ".epub", ".csv",
    ".json", ".xml", ".msg", ".ipynb", ".rtf", ".txt",
}

def convert_file(file_path: str) -> str:
    """Convert a single file and return Markdown content."""
    with open(file_path, "rb") as f:
        resp = requests.post(
            API_URL,
            headers={"X-Api-Key": API_KEY},
            files={"file": f},
        )
    resp.raise_for_status()
    return resp.json()["markdown"]

def batch_convert(input_dir: str, output_dir: str):
    """Convert all supported files in input_dir to Markdown."""
    os.makedirs(output_dir, exist_ok=True)

    for file_path in Path(input_dir).iterdir():
        if file_path.suffix.lower() not in SUPPORTED:
            continue

        print(f"Converting: {file_path.name}")
        try:
            markdown = convert_file(str(file_path))
            out_path = Path(output_dir) / f"{file_path.stem}.md"
            out_path.write_text(markdown, encoding="utf-8")
            print(f"  Saved: {out_path.name}")
        except requests.HTTPError as e:
            print(f"  Error: {e}")

# Convert all files in ./documents/ to ./markdown/
batch_convert("./documents", "./markdown")

This script processes every supported file in the input directory and writes the Markdown output to a separate folder. It skips unsupported file types and handles errors gracefully.

Integration Ideas

Once you have API access, here are practical ways to integrate file-to-Markdown conversion into your workflows:

CI/CD Pipeline

Add a conversion step to your CI pipeline. When someone commits a PDF or Word document to the repository, automatically convert it to Markdown and commit the result:

# In your CI script
for file in docs/sources/*.pdf; do
  md_file="docs/generated/$(basename "$file" .pdf).md"
  curl -s -X POST https://api.tomdnow.com/api/v1/convert \
    -H "X-Api-Key: $TOMDNOW_API_KEY" \
    -F "file=@$file" | jq -r '.markdown' > "$md_file"
done

Obsidian Import Script

Build a script that watches a folder for new documents and automatically converts them into your Obsidian vault:

import time
from pathlib import Path
from watchdog.observers import Observer
from watchdog.events import FileCreatedHandler

class ConvertHandler(FileCreatedHandler):
    def on_created(self, event):
        if Path(event.src_path).suffix.lower() in SUPPORTED:
            markdown = convert_file(event.src_path)
            stem = Path(event.src_path).stem
            out = Path("~/obsidian-vault/Imports") / f"{stem}.md"
            out.expanduser().write_text(markdown)

observer = Observer()
observer.schedule(ConvertHandler(), "./watch-folder")
observer.start()

AI Pipeline Preprocessing

Convert documents to Markdown before feeding them to an AI API:

# Convert and then send to OpenAI
markdown = convert_file("quarterly-report.pdf")

from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": f"Summarize this report:\n\n{markdown}"
    }],
)
print(response.choices[0].message.content)

Rate Limits and Best Practices

  • Free tier: 50 requests per day. No credit card required.
  • File size limit: 10 MB per file.
  • Add delays in batch scripts to stay well within rate limits. A one-second delay between requests is sufficient.
  • Cache results so you do not convert the same file twice.
  • Check daily_remaining in the response to monitor your usage.

Get your free API key and start automating at tomdnow.com.