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 framework for building Model Context Protocol (MCP) servers elegantly in TypeScript.
A Model Context Protocol (MCP) server for interacting with Twitter.
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A simple, composable framework to build agents using Model Context Protocol and simple workflow patterns
A Model Context Protocol (MCP) server that provides secure, read-only access to BigQuery datasets. Enables Large Language Models (LLMs) to safely query and analyze data through a standardized interface.
Store and load JSON documents from LLM tool use
A framework for building Model Context Protocol (MCP) servers elegantly in TypeScript.
A Model Context Protocol (MCP) server that helps read GitHub repository structure and important files.
A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools.
Model Context Protocol with Neo4j. This server provides natural language interaction with Neo4j and Aura accounts.
An MCP server that provides LLMs with the latest stable package versions when coding
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
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.