Pandoc MCP Server

What does “Pandoc” MCP Server do?

The Pandoc MCP Server is designed to bridge AI assistants and external document conversion capabilities by leveraging the power of Pandoc—a universal document converter. By exposing Pandoc’s functionality through the Model Context Protocol (MCP), this server enables AI-driven automation of format transformations, such as converting Markdown to PDF or HTML, within larger development workflows. It empowers language models and their clients to perform document conversions programmatically, simplifying content management tasks, automating document pipelines, and enhancing the productivity of teams that work with diverse file types and documentation standards.

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No explicit tools are documented in the repository’s server.py file or documentation.

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Use Cases of this MCP Server

• Automated Document Conversion: Developers can automate the process of converting documents between formats (e.g., Markdown to PDF/HTML) within AI-driven workflows.
• Content Standardization Pipelines: Ensures that documentation or reports across a codebase remain in a standardized format by integrating with AI assistants for on-the-fly conversions.
• Technical Writing Assistance: AI models can utilize the Pandoc server to preview, render, or export user-generated technical content in various formats for sharing or publishing.
• Seamless Integration with CI/CD: Enables continuous integration pipelines to automatically convert and check documentation formats as part of release processes.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Open your Windsurf configuration file.
3. Add the Pandoc MCP Server under the mcpServers object using the configuration below.
4. Save the file and restart Windsurf.
5. Verify the server is running and accessible.

"mcpServers": {
  "pandoc-mcp": {
    "command": "npx",
    "args": ["@pandoc/mcp-server@latest"]
  }
}

Claude

1. Confirm Node.js is installed.
2. Locate your Claude configuration file.
3. Insert the Pandoc MCP Server entry as shown.
4. Save and restart Claude.
5. Check for successful integration.

"mcpServers": {
  "pandoc-mcp": {
    "command": "npx",
    "args": ["@pandoc/mcp-server@latest"]
  }
}

Cursor

1. Install Node.js if not already present.
2. Edit the Cursor configuration file.
3. Add the Pandoc MCP Server configuration.
4. Save changes and restart Cursor.
5. Ensure the server is recognized.

"mcpServers": {
  "pandoc-mcp": {
    "command": "npx",
    "args": ["@pandoc/mcp-server@latest"]
  }
}

Cline

1. Install Node.js.
2. Open the Cline configuration file.
3. Add the Pandoc MCP Server settings.
4. Save and restart Cline.
5. Confirm operation.

"mcpServers": {
  "pandoc-mcp": {
    "command": "npx",
    "args": ["@pandoc/mcp-server@latest"]
  }
}

Securing API Keys

To secure API keys, use environment variables in your configuration:

"pandoc-mcp": {
  "command": "npx",
  "args": ["@pandoc/mcp-server@latest"],
  "env": {
    "API_KEY": "${PANDOC_API_KEY}"
  },
  "inputs": {
    "api_key": "${PANDOC_API_KEY}"
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "pandoc-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “pandoc-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server provides a clear overview and setup instructions, but is missing detailed documentation on prompts, tools, and resources. Its utility is apparent for document conversion, but the lack of specifics limits its immediate out-of-the-box usefulness.

MCP Score

Rating: 3/10 — The server gives a good sense of purpose and integration steps, but the lack of code, LICENSE, and detailed MCP primitives holds it back from broader usability.