AmanMCP is a privacy-first, zero-configuration local code library retrieval-augmented generation (RAG) tool that integrates with Claude AI through the MCP protocol, providing hybrid search functionality. It runs entirely locally without the need for an internet connection.

The MCP RAG Server is a retrieval-augmented generation service based on the Model Context Protocol. It automatically indexes project documents through local tools (ChromaDB and Ollama) and provides context enhancement capabilities for connected LLMs.

ctx-sys is a locally prioritized hybrid retrieval-augmented generation (RAG) tool designed for AI programming assistants. It analyzes the codebase through a tree parser, combines semantic vector search, keyword retrieval, and graph relationship traversal to provide in-depth project understanding for AI assistants. All operations are performed locally to protect code privacy.

A server-side project based on the Model Context Protocol (MCP) that provides retrieval-augmented generation (RAG) capabilities. It can serve as a bridge between clients such as Cursor IDE and Claude Desktop and Contextual AI agents, enabling intelligent Q&A based on the knowledge base and context-aware responses.

Apple RAG MCP is a retrieval-augmented generation system that provides Apple development expertise for AI agents. It integrates official Swift documentation, design guides, and Apple Developer YouTube content, and provides accurate technical answers through AI-driven hybrid search technology.

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.