productivitydeveloper-tools

Markdownify MCP▌

by zcaceres

Convert almost anything to Markdown. Transforms PDFs, images, web pages, DOCX, XLSX, and other formats into clean Markdo

Convert almost anything to Markdown. Transforms PDFs, images, web pages, DOCX, XLSX, and other formats into clean Markdown that AI assistants can read and analyze. 2,400+ GitHub stars.

github stars

★ 2.4K

GitHub

2,400+ GitHub starsSupports 6+ file formatsYouTube transcript extraction

best for

/ Content creators processing diverse file formats
/ Researchers analyzing documents with AI assistants
/ Developers building document processing workflows

capabilities

/ Convert PDFs to Markdown
/ Transform images to readable text
/ Extract YouTube video transcripts
/ Convert Office documents (DOCX, XLSX, PPTX)
/ Turn web pages into Markdown
/ Transcribe audio files to text

what it does

Converts various file formats (PDFs, images, DOCX, XLSX, web pages, YouTube videos) into clean Markdown that AI assistants can read and analyze.

about

Markdownify MCP is a community-built MCP server published by zcaceres that provides AI assistants with tools and capabilities via the Model Context Protocol. Convert almost anything to Markdown. Transforms PDFs, images, web pages, DOCX, XLSX, and other formats into clean Markdo It is categorized under productivity, developer tools.

how to install

You can install Markdownify MCP in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Markdownify MCP is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Markdownify MCP Server

Help! I need someone with a Windows computer to help me add support for Markdownify-MCP on Windows. PRs exist but I cannot test them. Post here if interested.

Markdownify is a Model Context Protocol (MCP) server that converts various file types and web content to Markdown format. It provides a set of tools to transform PDFs, images, audio files, web pages, and more into easily readable and shareable Markdown text.

Features

Convert multiple file types to Markdown:
- PDF
- Images
- Audio (with transcription)
- DOCX
- XLSX
- PPTX
Convert web content to Markdown:
- YouTube video transcripts
- Bing search results
- General web pages
Retrieve existing Markdown files

Getting Started

Clone this repository
Install dependencies:
```
pnpm install
```

Note: this will also install uv and related Python depdencies.

Build the project:
```
pnpm run build
```
Start the server:
```
pnpm start
```

Development

Use pnpm run dev to start the TypeScript compiler in watch mode
Modify src/server.ts to customize server behavior
Add or modify tools in src/tools.ts

Usage with Desktop App

To integrate this server with a desktop app, add the following to your app's server configuration:

{
  "mcpServers": {
    "markdownify": {
      "command": "node",
      "args": [
        "{ABSOLUTE PATH TO FILE HERE}/dist/index.js"
      ],
      "env": {
        // By default, the server will use the default install location of `uv`
        "UV_PATH": "/path/to/uv"
      }
    }
  }
}

Available Tools

youtube-to-markdown: Convert YouTube videos to Markdown
pdf-to-markdown: Convert PDF files to Markdown
bing-search-to-markdown: Convert Bing search results to Markdown
webpage-to-markdown: Convert web pages to Markdown
image-to-markdown: Convert images to Markdown with metadata
audio-to-markdown: Convert audio files to Markdown with transcription
docx-to-markdown: Convert DOCX files to Markdown
xlsx-to-markdown: Convert XLSX files to Markdown
pptx-to-markdown: Convert PPTX files to Markdown
get-markdown-file: Retrieve an existing Markdown file. File extension must end with: *.md, *.markdown.

OPTIONAL: set MD_SHARE_DIR env var to restrict the directory from which files can be retrieved, e.g. MD_SHARE_DIR=[SOME_PATH] pnpm run start

Contributing

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

License

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