Awesome GitHub RepositoriesModel Context Protocol

This project serves as a centralized directory and interoperability hub for the Model Context Protocol, providing a curated collection of standardized service connectors that bridge artificial intelligence models with external software, databases, and APIs. It facilitates the integration of AI agents with diverse ecosystems by offering a registry of machine-readable interface definitions that enable dynamic tool discovery and structured context injection. The directory distinguishes itself by focusing on the protocol-based interoperability required for autonomous AI agents to interact with he

The Model Context Protocol is a standardized communication framework designed to connect language models to external data sources, functional tools, and interactive user interfaces. It provides a vendor-neutral interface layer that enables AI hosts to discover and execute capabilities across heterogeneous service environments, using a JSON-RPC based messaging standard to facilitate bidirectional communication between clients and servers. The protocol distinguishes itself through a robust capability-based handshake that negotiates feature sets during session initialization, ensuring compatibil

Context7 is an AI-powered documentation retrieval engine designed to provide developers and AI agents with real-time, context-aware access to technical documentation and code snippets. By integrating external library documentation as callable tools, the platform equips AI coding assistants with project-specific knowledge, helping to improve generation accuracy and reduce hallucinations during inference. The platform distinguishes itself through a robust security and governance framework that manages documentation as a centralized knowledge base. It employs a multi-source ingestion pipeline to

This project is a Model Context Protocol server that provides a standardized interface for connecting host applications to external data sources and service APIs. It functions as a middleware component, exposing repository-related functionality as a set of discoverable tools that can be invoked dynamically by large language models to facilitate context-aware reasoning and task execution. By bridging host environments with external platforms, the server enables artificial intelligence models to access real-time repository information, supporting automated workflows and improved accuracy in gen

fastmcp is a Python library and framework for building servers and clients that implement the Model Context Protocol. It serves as a tool integration library designed to connect large language models to external tools and data sources. The framework features an interactive tool user interface renderer, which allows for the display of visual interfaces for tools directly within a conversational flow. It also provides a library for automatically generating schemas and validation for tools used by language models. The project covers server and client development, including tool and resource exp

The inspector is a diagnostic and validation tool for the Model Context Protocol. It provides an interactive interface and a transport proxy to discover, inspect, and execute the tools, prompts, and resources provided by an MCP server. The project serves as a debugger and compliance tester to verify that server implementations adhere to the protocol specification and JSON-RPC standards. It allows for real-time monitoring of message exchanges and logs between clients and servers across various transport layers, such as standard input/output and Server-Sent Events. The tool covers a broad rang

Model Context Protocol is a standardized framework for connecting large language models to external data sources and executable tools. It enables the creation of a universal interface where servers expose tools, resources, and prompts that can be discovered and utilized by various AI clients. The protocol utilizes a JSON-RPC message system that is transport-agnostic, supporting both standard input/output for local processes and HTTP with server-sent events for remote connections. It emphasizes security and control by delegating model sampling to the client to keep API keys secure from servers