# AI Protocol

MCP is a standardized protocol for interaction between AI models and applications, supporting tool invocation, resource management, and real-time data transfer.

A model context protocol (MCP) SDK implemented in Python for building standard servers and clients interacting with LLMs

This is a curated list of Awesome Model Context Protocol (MCP) Servers, which contains detailed classifications and descriptions of various MCP servers, frameworks, tools, and resources. MCP is an open protocol that enables AI models to securely interact with local and remote resources through standardized servers. The list covers MCP server implementations in multiple fields, including browser automation, cloud platforms, databases, developer tools, fintech, gaming, and security.

Awesome MCP Servers is a curated list of Model Context Protocol (MCP) servers that cover a variety of tools and integrations, including browser automation, art and culture, cloud platforms, command - line, communication, customer data platforms, databases, developer tools, file systems, fintech, knowledge and memory, location services, monitoring, search, security, travel and transportation, version control, etc. MCP is an open protocol that enables AI models to securely interact with local and remote resources through standardized servers.

MCP is an open protocol for standardizing the context of AI applications, similar to the USB - C interface in the AI field, enabling seamless connection between LLMs and various data sources/tools. It provides preset integrations, multi - model switching, secure data processing, and a standardized interface, supporting the construction of LLM - based intelligent agents and complex workflows.

This project implements a Model Context Protocol (MCP) server for standardizing the connection between AI applications and data sources, supporting querying document content and providing three core capabilities: resources, tools, and prompts.

A Docker operation protocol server based on Claude AI, providing functions such as container and Compose stack creation, deployment, and log viewing.

The TianGong AI Model Context Protocol (MCP) Server supports multiple communication protocols, including STDIO and Streamable Http, and provides convenient deployment and development support.

MCP is an open protocol that provides standardized context interaction for AI applications, similar to the unified interface of USB-C, supporting seamless connection between LLMs and multiple data sources and tools.

This project implements a Model Context Protocol (MCP) server, which provides a standardized data source access method for LLMs and supports three core capabilities: resources, tools, and prompts.

This project provides multiple examples of MCP server setup, supports local and cloud deployment, includes two transport modes: STDIO and SSE, and is accompanied by detailed tutorial videos.

This project is a collection of learning resources about Model Context Protocol (MCP), including guides, tools, and server implementations, aiming to help developers understand and apply the MCP standard. The project provides multiple example implementations of MCP servers, such as file system operations, Git management, GitHub integration, etc., as well as development guides and practical tools.

LOTUS-MCP is an open-source model coordination protocol that integrates the two major AI models, Mistral and Gemini, providing a unified interface, context sharing, and tool integration to achieve intelligent routing and cost optimization.