awesome-mcp-servers: A Curated List of Model Context Protocol Servers

awesome-mcp-servers: A Curated List of Model Context Protocol Servers

Summary

The `awesome-mcp-servers` repository is a comprehensive, curated list of Model Context Protocol (MCP) servers. It serves as a central hub for discovering various MCP implementations, ranging from official integrations to community-developed tools. This resource is invaluable for developers and AI enthusiasts looking to extend the capabilities of AI agents by connecting them to real-world services and data.

Repository Info

Updated on November 19, 2025
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Tags

Model Context ProtocolMCPAI AgentsCurated ListGitHubOpen SourceDeveloper ToolsIntegrations

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Introduction

The awesome-mcp-servers repository, maintained by wong2, is a meticulously curated collection of Model Context Protocol (MCP) servers. The Model Context Protocol is designed to enable AI agents and large language models (LLMs) to interact with external services, access real-time data, and perform actions in the real world. This repository acts as a vital directory, showcasing a diverse ecosystem of servers that expand the practical applications of AI.

From reference implementations demonstrating core MCP features to official integrations with major platforms and a growing list of community-contributed tools, awesome-mcp-servers highlights the breadth and potential of MCP. It's an essential resource for anyone looking to build or integrate AI agents with external systems.

Getting Started with MCP Servers

Since awesome-mcp-servers is a list, there isn't a direct "installation" for the repository itself. Instead, you can leverage it to discover and interact with various MCP servers:

  1. Explore the List: Browse the repository's README to find MCP servers relevant to your needs, categorized into Reference, Official, and Community servers.

  2. Use an MCP Client: To interact with any MCP server, you'll need an MCP client. A popular command-line client is mcp-cli:

    npm install -g mcp-cli
mcp-cli add <server-url> # Add a server by its URL
mcp-cli list             # List configured servers
mcp-cli call <server-name> <tool-name> <args> # Call a specific tool on a server

    Other clients like MBro, Continue, LibreChat, and Zed also offer robust MCP integration, allowing you to connect and manage servers directly within your development environment or AI interface.

  3. Integrate with AI Agents: Many AI platforms and IDEs (such as Cursor, Claude Desktop, and Windsurf) provide native support for MCP, enabling you to seamlessly integrate these servers into your AI-powered workflows for enhanced functionality.

Examples of MCP Servers

The awesome-mcp-servers list categorizes servers to help users find what they need. Here are a few examples from each category:

  • Reference Servers: These servers demonstrate core MCP features and SDK usage.

    • Everything: A comprehensive reference/test server featuring prompts, resources, and tools.
    • Filesystem: Provides secure file operations with configurable access controls.

  • Official Servers: Integrations maintained by companies building production-ready MCP servers for their platforms.

    • GitHub: GitHub's official MCP Server, enabling AI agents to interact with repositories, issues, and pull requests.
    • AWS Bedrock KB Retrieval: Allows querying Amazon Bedrock Knowledge Bases using natural language to retrieve information from your data sources.
    • Stripe: An official server to interact with the Stripe API for payment processing and financial management.

  • Community Servers: A growing set of servers developed and maintained by the community, showcasing diverse applications.

    • Airtable: Provides read and write access to Airtable databases.
    • Docker: Enables AI agents to run and manage Docker containers, Docker Compose, and view logs.
    • Weather: Offers accurate weather forecasts via the AccuWeather API.

Why Use Model Context Protocol?

The Model Context Protocol (MCP) offers significant advantages for AI development and integration:

  • Enhanced AI Capabilities: MCP allows AI agents to go beyond their training data, enabling them to perform real-world actions, access up-to-date information, and interact with dynamic environments.
  • Standardized Integration: It provides a unified and consistent way for AI models to connect with a wide array of external services, reducing complexity and promoting interoperability.
  • Rich Ecosystem: The awesome-mcp-servers list demonstrates a vast and rapidly expanding ecosystem of tools across various domains, including development, finance, smart homes, and data analytics.
  • Community-Driven Innovation: With contributions from both official platforms and the open-source community, MCP fosters innovation and ensures a broad spectrum of functionalities is available to AI developers.

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