This repository curates a comprehensive list of Model Context Protocol (MCP) servers, tools, and frameworks, addressing the need for AI models to securely interact with local and remote resources. It targets developers, researchers, and power users looking to extend AI capabilities beyond text generation by enabling structured access to data, APIs, and system functionalities. The primary benefit is a standardized, extensible protocol for AI-resource integration.

How It Works

MCP is an open protocol that defines standardized server implementations allowing AI models to securely access and interact with various resources. These servers act as intermediaries, translating AI requests into actions on local filesystems, databases, cloud services, APIs, and more. This approach enables AI agents to perform complex tasks by leveraging external data and tools in a controlled manner.

Quick Start & Requirements

Installation and management can be facilitated by tools like mcp-get (for Claude Desktop) and mxcp. Official documentation and tutorials are available via links within the README. Users should be aware of the significant security implications of running MCP servers, as they can execute arbitrary code.

Highlighted Details

• Extensive Categorization: Servers are meticulously categorized across dozens of domains, including File Systems, Databases, Cloud Storage, AI Services, Development Tools, and more, showcasing the protocol's broad applicability.
• Official Implementations: Key vendors like Cloudflare, Stripe, PayPal, Box, and others provide official MCP server implementations (marked with ⭐), indicating production readiness and strong ecosystem support.
• Security Warnings: A prominent security warning highlights risks such as full system access, code execution, prompt injection, and data exposure, recommending best practices like running servers in isolated environments (VMs, containers) and preferring official implementations.
• Rich Ecosystem: The list includes reference servers, official servers, a vast array of community servers, clients, and frameworks for building MCP services, demonstrating a rapidly growing and active ecosystem.

Maintenance & Community

The project fosters community engagement through a dedicated Reddit community (r/mcp) and a Discord Server. While specific maintainers for the curated list are not detailed, the numerous linked GitHub repositories indicate a broad base of contributors.

Licensing & Compatibility

The README does not explicitly state a license for the curated list itself. Individual linked GitHub repositories will have their own licenses, which users must consult for compatibility, especially for commercial use or closed-source linking.

Limitations & Caveats

The primary limitation is the inherent security risk associated with running MCP servers, particularly untrusted community implementations, which can lead to arbitrary code execution and system compromise. Users must exercise caution and adhere to security best practices, such as running servers in isolated environments. The ecosystem's rapid evolution means users need to stay updated on new developments and potential changes.