Model Context Protocol
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Model Context Protocol Servers. This repository is a collection of reference implementations for the Model Context Protocol (MCP), demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.
A Model Context Protocol server that provides access to BigQuery. This server enables LLMs to inspect database schemas and execute queries.
A MCP server for Home Assistant
Go server implementing Model Context Protocol (MCP) for filesystem operations.
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A Model Context Protocol (MCP) server implementation that provides Elasticsearch interaction. This server enables searching documents, analyzing indices, and managing cluster through a set of tools.
This MCP server attempts to exercise all the features of the MCP protocol. It is not intended to be a useful server, but rather a test server for builders of MCP clients. It implements prompts, tools, resources, sampling, and more to showcase MCP capabilities.
MCP server to provide Figma layout information to AI coding agents like Cursor
Foxy contexts is a Golang library for building context servers supporting Model Context Protocol.
Model Context Protocol (MCP) server implementation providing Google News search capabilities via SerpAPI, with automatic news categorization and multi-language support.
A Google Tasks Model Context Protocol Server for Claude
A Model Context Protocol (MCP) server that provides tools for fetching information from Hacker News.
A TypeScript framework for building MCP (Model Context Protocol) servers elegantly
Understanding Model Context Protocol (MCP)
Model Context Protocol (MCP) is a groundbreaking open-source protocol developed by Anthropic that revolutionizes how AI systems interact with external data sources. As the foundation for next-generation AI interactions, MCP enables AI assistants like Claude to establish secure and standardized connections with various data sources and tools.
Key Features of MCP
- Universal Standard: Provides a unified framework for AI systems to access external data, tools, and prompts
- Client-Server Architecture: Implements a robust and scalable architecture for seamless AI interactions
- Security-First Design: Ensures secure data transmission and access control between AI systems and data sources
- Tool Integration: Enables AI assistants to interact with various external tools and services
- Standardized Communication: Establishes consistent protocols for data exchange and interaction patterns
Benefits for AI Development
MCP represents a significant advancement in AI infrastructure, offering developers and organizations a standardized way to build and deploy AI applications. By providing a common protocol, MCP reduces implementation complexity and ensures compatibility across different AI systems and data sources.
Whether you're developing AI applications, managing data infrastructure, or implementing AI solutions, MCP provides the foundation for secure, efficient, and standardized AI interactions. Explore our directory to find MCP servers that match your specific needs and requirements.