Awesome MCP Servers
The definitive curated directory of Model Context Protocol servers for extending AI capabilities across every domain.
Author: punkpeye | GitHub Repo | 58542 Stars|4508 Forks|MIT License|Updated Jun 29, 2025
Overview
Awesome MCP Servers is the most comprehensive collection of Model Context Protocol (MCP) servers available, featuring hundreds of curated implementations organized by category that enable AI models to securely interact with local and remote resources. This repository serves as the central hub for discovering MCP servers across databases, developer tools, cloud platforms, finance, and specialized domains.
Features
- Comprehensive Directory - Extensive collection of MCP servers organized by domain and functionality
- Multi-Language Support - Servers implemented in Python, TypeScript, Go, Rust, and more
- Web Directory Integration - Connected to glama.ai/mcp/servers for easy browsing and discovery
- Domain Coverage - Databases (50+), developer tools, cloud services, finance, AI/ML platforms
- Regular Updates - Active curation with new servers added weekly
- Community Contributions - Open contribution process for server maintainers
How to Use This Directory
What This Is
- Comprehensive curated directory of MCP servers (not installable software)
- Browse via GitHub repository or glama.ai web interface
- Each listed server has its own separate installation procedures
Finding MCP Servers
- Browse on GitHub: Navigate categories in the repository
- Use Web Interface: Visit glama.ai/mcp/servers for searchable browsing
- Filter by Domain: Categories include databases, developer tools, cloud services, finance
- Check Server Details: Each listing provides repository links and descriptions
Directory Organization
- Emoji-based categorization for easy navigation
- Direct repository links for each MCP server
- Regular updates with new server discoveries
- Community contribution guidelines for server maintainers
- Quality curation focusing on stable implementations
Using Individual Servers
Installation Process
- Select a Server: Choose from the curated directory
- Visit Repository: Follow the GitHub link for the specific server
- Follow Instructions: Each server has its own installation guide
- Configure Client: Add to Claude Desktop, VS Code, or your preferred MCP client
The directory provides standardized documentation for each MCP server, making it easy to evaluate and integrate the right tools for your AI workflows. Individual servers are categorized by domain expertise, with links to their detailed setup instructions and usage examples.
Awesome MCP Servers is maintained by punkpeye and the open-source community. For server submissions and updates, please refer to the official GitHub repository.