Markdownify
A Model Context Protocol server that converts diverse file types, including PDFs, images, audio, and Office documents, into Markdown format. It also transforms web content like YouTube transcripts and Bing search results into readable text for model
What is Markdownify?
Markdownify is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to model context protocol server that converts diverse file types, including pdfs, images, audio, and office documents, into markdown format. it also transforms web content like youtube transcripts and b...
A Model Context Protocol server that converts diverse file types, including PDFs, images, audio, and Office documents, into Markdown format. It also transforms web content like YouTube transcripts and Bing search results into readable text for model
This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- A Model Context Protocol server that converts diverse file t
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx markdownify-mcp-serverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Markdownify
Markdownify is an MCP server that converts almost any file or web source into clean Markdown that AI models can easily read and reason over. It handles PDFs, Word documents, Excel spreadsheets, PowerPoint presentations, images (with OCR metadata), audio files (with transcription), YouTube video transcripts, Bing search result pages, and arbitrary web URLs — all through nine specialized tools exposed over the Model Context Protocol. Developers and researchers use it to feed rich, mixed-format content to AI assistants without manually copying or reformatting material.
Prerequisites
- Node.js 18+ and Bun runtime installed
- Python 3.9+ (for the bundled markitdown virtual environment)
- An MCP-compatible client such as Claude Desktop or Claude Code
- Optional: yt-dlp installed for YouTube audio transcription support
Clone the repository
Clone the Markdownify MCP server source code to your local machine.
git clone https://github.com/zcaceres/markdownify-mcp.git
cd markdownify-mcpInstall dependencies
Run the Bun install command. This installs Node dependencies and automatically creates a Python virtual environment with the markitdown library.
bun installBuild the server
Compile the TypeScript source to a distributable JavaScript bundle in the dist/ directory.
bun run buildSet allowed paths
Configure the MD_ALLOWED_PATHS environment variable to specify which directories on disk the server is permitted to read. Use your OS path separator (: on macOS/Linux, ; on Windows).
export MD_ALLOWED_PATHS="/Users/you/Documents:/Users/you/Downloads"Add the server to Claude Desktop
Edit your Claude Desktop configuration file to register the Markdownify MCP server as a subprocess. Replace the path with the absolute path to your cloned repository.
{
"mcpServers": {
"markdownify": {
"command": "node",
"args": ["/absolute/path/to/markdownify-mcp/dist/index.js"],
"env": {
"MD_ALLOWED_PATHS": "/Users/you/Documents:/Users/you/Downloads"
}
}
}
}Verify the tools are available
Restart Claude Desktop and check that the nine Markdownify tools appear (pdf_to_markdown, docx_to_markdown, youtube_to_markdown, bing_search_to_markdown, etc.).
Markdownify Examples
Client configuration
Full Claude Desktop JSON config to run Markdownify MCP server with path restrictions.
{
"mcpServers": {
"markdownify": {
"command": "node",
"args": ["/Users/you/markdownify-mcp/dist/index.js"],
"env": {
"MD_ALLOWED_PATHS": "/Users/you/Documents:/Users/you/Downloads",
"MARKITDOWN_PATH": ""
}
}
}
}Prompts to try
Example prompts to use with Markdownify once the MCP server is connected.
- "Convert /Users/me/Downloads/report.pdf to Markdown and summarize the key findings."
- "Get the transcript for https://www.youtube.com/watch?v=dQw4w9WgXcQ and list the main topics covered."
- "Search Bing for 'MCP server best practices 2025' and give me a bullet-point summary."
- "Read /Users/me/Documents/budget.xlsx and show me the total row values as a Markdown table."Troubleshooting Markdownify
Tool returns 'path not allowed' or 'access denied' errors
Add the directory containing your files to MD_ALLOWED_PATHS. The server blocks all paths not explicitly listed in this variable for security.
Audio transcription tools fail or return empty output
The published Docker image only includes PDF support. For audio transcription, use a local installation and ensure yt-dlp and ffmpeg are on your system PATH.
bun install fails with Python virtual environment errors
Ensure Python 3.9+ is installed and accessible as 'python3'. The install script creates a venv at .venv/ in the project root; if it fails, create it manually with 'python3 -m venv .venv && .venv/bin/pip install markitdown'.
Frequently Asked Questions about Markdownify
What is Markdownify?
Markdownify is a Model Context Protocol (MCP) server that model context protocol server that converts diverse file types, including pdfs, images, audio, and office documents, into markdown format. it also transforms web content like youtube transcripts and bing search results into readable text for model It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Markdownify?
Follow the installation instructions on the Markdownify GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Markdownify?
Markdownify works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Markdownify free to use?
Yes, Markdownify is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
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Set Up Markdownify in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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