MCP Lists Documentation
A comprehensive collection of Model Context Protocol (MCP) server implementations and curated lists for reference and exploration.
Repository Structure
This repository is organized into three main directories:
📁 official/
Contains the official MCP servers repository maintained by Anthropic:
- servers - Reference implementations demonstrating MCP features and the TypeScript/Python SDKs
- Everything - Reference/test server with prompts, resources, and tools - Fetch - Web content fetching and conversion - Filesystem - Secure file operations with configurable access controls - Git - Tools to read, search, and manipulate Git repositories - Memory - Knowledge graph-based persistent memory system - Sequential Thinking - Dynamic and reflective problem-solving - Time - Time and timezone conversion capabilities
📁 community/
Popular community-built MCP servers:
- **** - GitHub's official MCP Server for managing issues, PRs, and discussions
- playwright-mcp - Browser automation for testing and scraping
- aws-mcp - AWS documentation, billing, service metadata, and more
- AWS Bedrock KB Retrieval - AWS CDK - AWS Core - AWS Cost Analysis - AWS Documentation - AWS Nova Canvas
- mcp-mongo-server - MongoDB database interactions
- brave-search-mcp-server - Web search integration via Brave Search API
- postgresql-mcp - PostgreSQL database access (Neon serverless Postgres)
- gdrive-mcp-server - Google Drive integration for file operations
- google-maps-mcp - Google Maps API integration
- slack-mcp-server - Slack workspace integration
📁 curated-lists/
Comprehensive lists of MCP servers from the community:
- awesome-mcp-servers - Curated list of MCP servers (https://mcpservers.org)
- TensorBlock-awesome-mcp-servers - Extensive list covering 7,260+ servers
- appcypher-awesome-mcp-servers - Another curated collection of MCP servers
About Model Context Protocol (MCP)
The Model Context Protocol is an open standard that enables seamless integration between AI applications and external data sources. MCP servers provide a standardized way to expose tools, resources, and prompts to AI models.
Usage
Each cloned repository contains its own documentation, installation instructions, and usage examples. Refer to the individual repository READMEs for specific setup and configuration details.
Notes
- These repositories are cloned as Git submodules for documentation and reference purposes
- Each server may have different requirements (API keys, credentials, etc.)
- Check individual repository documentation for:
- Installation requirements - Configuration steps - Usage examples - API limitations
Resources
Contributing
This is a documentation repository. To contribute to individual MCP servers, please visit their respective repositories listed above.