Pandoc MCP Server

Pandoc MCP Server

📖 Overview

Pandoc MCP Server is a Model Context Protocol (MCP) implementation that converts Markdown text into various document formats using Pandoc. It provides a standardized interface for document conversion, uploading to Google Cloud Storage (GCS), and returning publicly accessible signed URLs.

🚀 Features

This server provides the following capabilities through MCP tools:

🔧 Prerequisites

You'll need one of the following:

• Docker: Docker installed and running (recommended)
• Python: Python 3.12+ with pip

Additionally, you'll need:

• Google Cloud Storage (GCS) Bucket: A GCS bucket where the server can upload converted files
• Google Cloud Credentials: Application Default Credentials (ADC) configured in your environment

You can configure ADC by running:

gcloud auth application-default login

For local Python setup, you'll also need:

• Pandoc: The Pandoc document converter
• LaTeX: A LaTeX distribution (like TeX Live or MiKTeX) for PDF conversion

⚙️ Setup & Configuration

Environment Configuration

1. Create your environment file:

   cp .env.example .env
   

2. Edit the .env file with your GCS bucket name:

   GCS_BUCKET_NAME=your-gcs-bucket-name
   

🏃‍♂️ Running the Server

Option 1: Docker (Recommended)

The Docker build must be run from the project root directory (klavis/):

# Navigate to the root directory of the project
cd /path/to/klavis

# Build the Docker image
docker build -t pandoc-mcp-server -f mcp_servers/pandoc/Dockerfile .

# Run the container (with Google Cloud credentials mounted)
docker run -p 5000:5000 --rm -v ~/.config/gcloud:/root/.config/gcloud pandoc-mcp-server

To use your local .env file instead of building it into the image:

docker run -p 5000:5000 --rm --env-file mcp_servers/pandoc/.env -v ~/.config/gcloud:/root/.config/gcloud pandoc-mcp-server

Option 2: Python Virtual Environment

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the server
python server.py

Once running, the server will be accessible at http://localhost:5000.

🔌 API Usage

The server implements the Model Context Protocol (MCP) standard. Here's an example of how to call a tool:

import httpx

async def call_pandoc_tool():
    url = "http://localhost:5000/execute"
    payload = {
        "tool_name": "convert_markdown_to_file",
        "tool_args": {
            "markdown_text": "# Hello World\n\nThis is a test document.",
            "output_format": "pdf"
        }
    }
    
    async with httpx.AsyncClient() as client:
        response = await client.post(url, json=payload)
        result = response.json()
        return result

📋 Common Operations

Converting Markdown to PDF

payload = {
    "tool_name": "convert_markdown_to_file",
    "tool_args": {
        "markdown_text": "# Hello World\n\nThis is a test document.",
        "output_format": "pdf"
    }
}

Converting Markdown to DOCX

payload = {
    "tool_name": "convert_markdown_to_file",
    "tool_args": {
        "markdown_text": "# Hello World\n\nThis is a test document.",
        "output_format": "docx"
    }
}

🛠️ Troubleshooting

Docker Build Issues

• File Not Found Errors: If you see errors like failed to compute cache key: failed to calculate checksum of ref: not found, this means Docker can't find the files referenced in the Dockerfile. Make sure you're building from the root project directory (klavis/), not from the server directory.

Common Runtime Issues

• GCS Bucket Not Found: Verify your GCS bucket exists and you have the correct permissions
• Google Cloud Authentication: Ensure your ADC is properly configured
• PDF Generation Issues: Avoid using emojis or complex characters in markdown text when converting to PDF, as they might cause issues with LaTeX
• Missing LaTeX: For local setup, ensure a LaTeX distribution is installed if you need PDF conversion

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.