This project implements functions similar to Claude Code through the MCP protocol, providing tools for code understanding, modification, and command execution. It supports multiple models and Jupyter notebook operations, and has security control and development expansion capabilities.

MCPfinder is a local server tool that provides dynamic capability expansion for AI agents. By searching for and installing toolkits compatible with the MCP protocol, language models can obtain new functions on demand.

This is a development template for the Model Context Protocol (MCP) based on TypeScript, providing a complete foundation for building MCP clients and servers, including production-grade tools, sample code, and clear documentation, supporting rapid development and expansion.

An AI agent server based on the Model Context Protocol (MCP), providing system prompt management and tool expansion functions, supporting the creation, management, and version control of AI agents.

A model context protocol server implemented in C++, featuring a plugin-based architecture, supporting cross-platform operation, and providing modular expansion capabilities.

The Model Context Protocol (MCP) is an open - source protocol released by Anthropic, aiming to solve the standardization problem of connecting large language models (LLMs) with external data sources and systems. It provides a structured framework that enables models to integrate and utilize external context, thereby expanding their capabilities and improving response accuracy. MCP supports functions such as knowledge expansion, external tool calls, and pre - written prompts.

A document search and retrieval server based on the Model Context Protocol (MCP), supporting local storage and Confluence integration, providing semantic search functionality, and allowing for the expansion of multiple document services.

An intelligent conversational robot project based on large models, supporting multi - platform access and multiple AI models, with text, voice, image processing, and plugin expansion capabilities, and can customize enterprise AI applications.

This project showcases a lightweight multi-agent AI system that combines the Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) for business analysis. The system provides statistical analysis of business data and knowledge retrieval functions for natural language queries by coordinating multiple dedicated tool servers. It features a modular design that facilitates expansion and the switching of LLM backends.