by zcaceres · MCP Server · ★ 2.7k
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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: Note: this will also install and related Python depdencies. Build the project: Start the server: Development Use to start the TypeScript compiler in watch mode Modify to customize server behavior Add or modify tools in Usage with Desktop App To integrate this server wi
| Stars | 2,673 |
| Forks | 222 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 57.8/100 |
| Open Issues | 6 |
| Last Updated | 2026-05-11 |
| Created | 2024-12-18 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~184k |
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markdownify-mcp is A Model Context Protocol server for converting almost anything to Markdown. It is categorized as a MCP Server with 2.7k GitHub stars.
markdownify-mcp is primarily written in TypeScript. It covers topics such as ai, anthropic, anthropic-ai.
You can find installation instructions and usage details in the markdownify-mcp GitHub repository at github.com/zcaceres/markdownify-mcp. The project has 2.7k stars and 222 forks, indicating an active community.
markdownify-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to markdownify-mcp on Agent Skills Hub include awesome-mcp-servers, unity-mcp, mcp-context-forge. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.