Open-Source MCP servers

Production-ready MCP servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

4,921 servers. Last updated -

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    A server providing tools to read, write, and edit Microsoft Word (docx) files through the Model Context Protocol, allowing operations like complete document reading, content creation, targeted paragraph editing, and text insertion.
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    A powerful Word document processing service based on FastMCP, enabling AI assistants to create, edit, and manage docx files with full formatting support. Preserves original styles when editing content.
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    Provides comprehensive document processing, including reading, converting, and manipulating various document formats with advanced text and HTML processing capabilities.
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    Integrates Cline with Azure DevOps services, enabling access to work items, repositories, and pull requests through configurable MCP tools.
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    Helps AI read GitHub repository structure and important files. Want to quickly understand what a repo is about? Prompt it with "read https://github.com/adhikasp/mcp-git-ingest and determine how the code technically works".
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    A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, allowing users to query work items with plans to support creating/updating items, managing pipelines, handling pull requests, and administering sprints and branch policies.
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    A Model Context Protocol server that enables AI assistants to interact with Azure DevOps resources including projects, work items, repositories, pull requests, branches, and pipelines through a standardized protocol.
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    A Model Context Protocol server for interacting with the Figma API that handles large Figma files efficiently through memory-aware chunking and pagination capabilities.
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    Converts REST API code (like NestJS controllers or FastAPI endpoints) to Postman collections and environments, helping developers automatically sync their API endpoints with Postman.
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    MCP server for seamless document format conversion using Pandoc, supporting Markdown, HTML, PDF, DOCX (.docx), csv and more.
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    A document conversion server that transforms various file formats (PDFs, documents, images, audio, web content) to Markdown with improved multilingual and UTF-8 support.
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    Enables AI assistants to maintain persistent project context across sessions by storing and retrieving structured information in markdown files organized in a memory bank directory.
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    Apache 2.0
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    A TypeScript tool that ranks files in your codebase by importance, tracks dependencies, and provides file summaries to help understand code structure through Cursor's Model Context Protocol.
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    A Model Context Protocol server that integrates with the Icypeas API to help users find work emails based on name and company information.
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    A Model Context Protocol server that enables AI assistants to interact with Feishu project management systems, allowing retrieval of project views and work items.
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    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.
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    This server provides a convenient API for interacting with Azure DevOps services, enabling AI assistants and other tools to manage work items, code repositories, boards, sprints, and more. Built with the Model Context Protocol, it provides a standardized interface for communicating with Azure DevOps
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    An MCP server that executes tox commands to run Python tests within a project using pytest, allowing users to run all tests or specific test groups, files, cases, or directories.
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    Enables Claude to interact with FTP servers through natural language commands, allowing users to list directories, download/upload files, create directories, and delete files/directories on FTP servers.
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    Converts Figma designs into React components with TypeScript and Tailwind CSS by extracting components from Figma files and transforming them into ready-to-use code.
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    A server that helps discover and analyze websites implementing the llms.txt standard, allowing users to check if websites have llms.txt files and list known compliant websites.
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    Jinni is a tool to efficiently provide Large Language Models the context of your projects. It gives a consolidated view of relevant project files complete with metadata, overcoming the limitations and inefficiencies of reading files one by one. The philosophy behind this tool is that LLM context wi
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    A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.
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    A Model Context Protocol server that converts Markdown content to HTML format.
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    An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.
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    A Python server that enables interaction with Box files and folders through the Box API, allowing operations like file search, text extraction, and AI-based querying and data extraction.
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    An MCP server that connects Gemini 2.5 Pro to Claude Code, enabling users to generate detailed implementation plans based on their codebase and receive feedback on code changes.
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    An MCP server that converts Excel and Apple Numbers files to PDF format, enabling AI assistants like Claude to perform file conversion directly through conversation.
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    The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
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    Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.
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    Chat with your codebase through intelligent code searching without embeddings by breaking files into logical chunks, giving the LLM tools to search these chunks, and letting it find specific code needed to answer your questions.
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    Provides ripgrep search capabilities to MCP clients like Claude, allowing high-performance text searches across files on your system.
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