Overview

This repository provides a comprehensive collection of reference implementations for the Model Context Protocol (MCP). MCP enables Large Language Models (LLMs) to securely and controllably access external tools and data sources, unlocking advanced capabilities and real-world integrations. These reference servers serve as educational examples, demonstrating MCP features and SDK usage, empowering developers to build their own custom MCP servers.

Key Features

• Diverse Reference Implementations: Explore a variety of servers showcasing different functionalities, including web fetching, secure filesystem access, Git repository manipulation, persistent knowledge graphs, sequential reasoning, and time conversions.
• Official MCP SDKs: Built using a range of official MCP SDKs across multiple programming languages (C#, Go, Java, Kotlin, PHP, Python, Ruby, Rust, Swift, TypeScript), providing flexibility for developers.
• Easy Integration: Simplified "Getting Started" guides and clear examples for using servers directly with npx or package managers like uvx and pip, as well as configuring them within MCP clients like Claude Desktop.
• Extensibility: Designed to be a foundation for developers to learn from and extend, encouraging the creation of new and innovative MCP servers.
• Archived Examples: Access a historical archive of previously maintained servers, offering insights into the evolution of MCP integrations.

Typical Use Cases

• Enhanced LLM Capabilities: Empower LLMs with real-time data retrieval, file system operations, code repository analysis, and time-sensitive information.
• Secure Data Access: Implement controlled and secure access to sensitive data and tools, crucial for enterprise applications.
• Tool Integration for AI Agents: Build sophisticated AI agents that can interact with the external world through a standardized protocol.
• Developer Education and Prototyping: Rapidly prototype and learn how to develop custom MCP servers and integrate them into AI workflows.
• Knowledge Augmentation: Connect LLMs to persistent knowledge bases and graph databases for richer context and recall.