A local server based on the MCP protocol that implements semantic search and RAG functions for Apple Notes, which can be called by AI assistants such as Claude.

The Crawl4AI RAG MCP Server is an AI agent service integrating web crawler and RAG functions, supporting smart URL detection, recursive crawling, parallel processing, and vector search. It aims to provide powerful knowledge acquisition and retrieval capabilities for AI coding assistants.

An MCP server implemented for the RAG Web Browser Actor, supporting AI agents to perform web searches and content extraction.

A document semantic search service based on the Qdrant vector database, supporting URL and local file imports and providing natural language query functions.

Quick-start Auto MCP is a tool that helps users quickly register the Anthropic Model Context Protocol (MCP) in Claude Desktop and Cursor. It provides functions such as RAG document search, Dify workflow, real-time web search, and supports one-click generation of JSON configuration files.

RagRabbit is a self - hosted AI site search and LLM.txt generation tool that supports one - click deployment on Vercel.

The SMMS project builds an MCP server based on semantic maps, providing 3D instance object management functions, including database operations, RAG retrieval, and cognitive topology map generation.

The project aims to connect RAG applications to open-webui through the MCP protocol to achieve model context interaction, including basic PoC verification, knowledge base integration, and client docking.

A RAG system based on Python code analysis. It parses the code structure through AST and stores it in the Weaviate vector database, providing code query, natural language Q&A, and visualization functions, and supporting multi-codebase management and dependency analysis.

A code knowledge management tool based on vector embedding, used to maintain and query code library knowledge, supporting local storage and RAG enhancement.

The Biel.ai MCP server connects the IDE to product documentation and enables AI tools to access and search the company's knowledge base through RAG technology, providing intelligent code completion and technical Q&A.

rag - mcp is an over - designed retrieval - augmented generation system that provides multiple text search modes (semantic search, question - answer search, style search) through a Python server. It uses PostgreSQL and pgvector to store text embedding vectors, supports interaction with AI agents, and has a complex but scalable architecture.

This project implements an MCP server and client for building intelligent agent applications based on Retrieval Augmented Generation (RAG). The server provides tools such as entity extraction, query optimization, and relevance checking, and the client demonstrates how to connect to the server and use these tools to enhance the performance of the RAG system.

The Storm MCP server is an open protocol that implements the Anthropic Model Context Protocol, supports the integration of LLM and RAG data sources, and provides functions such as context sharing, tool system, file management, and API integration.

This project implements an MCP server based on the FAISS vector database, supporting the Retrieval Augmented Generation (RAG) function, including a complete workflow such as GitHub file download, document indexing, local query, and LLM integration.

ClaudeHopper is an AI-based construction document assistance tool that enables intelligent interaction with construction drawings and specification documents through RAG and hybrid search technologies.

Code Graph RAG MCP is an advanced model context protocol server that provides 13 professional tools for code understanding, relationship mapping, and semantic search through intelligent graph representation and multi - language code analysis capabilities.

RAG project based on MCP server

This project builds an HR chatbot based on RAG. The MCP server serves as the functional call center to implement PDF document upload, parsing, retrieval, and natural language question answering functions.

A high-performance local RAG system based on Rust, integrated with Claude Desktop via the MCP protocol to achieve local processing, semantic search, and privacy protection of PDF documents.

A diary system based on Retrieval Augmented Generation (RAG), supporting diary organization by date and topic, and providing semantic search functionality, which can be connected to an AI agent to enhance interaction.

The MCP application is a service that combines RAG and web search tools, using OpenAI embedding vector storage, PostgreSQL as the database, and PGVector as the vector storage, supporting knowledge retrieval and document addition functions.

The Shared Knowledge MCP Server is a middleware that provides unified knowledge base services for multiple AI assistants, enabling efficient information retrieval and sharing through RAG technology.

A RAG service based on the Qdrant vector database and Ollama/OpenAI embedding, providing document semantic search and management functions.

The Root Signals MCP Server is a bridging project that exposes the Root Signals evaluation tools to AI assistants and agents through the Model Context Protocol (MCP), supporting standard evaluation and RAG evaluation with context.

This project builds a RAG-based HR chatbot. Through the MCP server as the function call center, it realizes the functions of PDF document uploading, parsing, retrieval, and natural language Q&A.

This project builds a Retrieval Augmented Generation (RAG) server based on IBM Watsonx.ai, uses ChromaDB for vector indexing, and exposes interfaces through the Model Context Protocol (MCP). The system can process PDF documents and answer questions based on the document content, realizing an intelligent question answering function that combines large language models with specific domain knowledge.

Archive Agent is an intelligent file indexing tool that supports searching and querying file content through natural language. It combines AI search (RAG engine), automatic OCR, and the MCP interface, and can handle various file types, including text, documents, PDFs, and images.

A Python-based document processing and Retrieval Augmented Generation (RAG) server that uses the DuckDB database to store embedding vectors, supports multiple file format processing, and provides a Web interface and API.

An MCP server based on the OpenAI API knowledge base, built with TypeScript, supporting RAG technology, capable of answering technical questions and compatible with multiple client tools.

A locally run RAG - style Model Context Protocol (MCP) server for web search. It can provide the latest web information retrieval function for models such as Claude without using an API.

The Inkeep MCP Server is a Python-based project that provides product documentation retrieval services for Claude Desktop by integrating Inkeep's RAG technology.

The RAG Document MCP Server is a vector search-based document processing tool that provides document retrieval and context enhancement functions for AI assistants.

RAGFlow MCP is a temporary implementation of an MCP server, providing basic functionality before the official MCP server is released. Two installation methods are provided, and the uv tool is recommended for fast installation and dependency management.

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.

An MCP server based on TypeScript that implements a Retrieval Augmented Generation (RAG) system for local documents, supporting querying and indexing of Git repositories and text files.

mcp-rag-server is a service based on the Model Context Protocol (MCP) that supports Retrieval Augmented Generation (RAG) and can index documents and provide relevant context for large language models.

An MCP server based on LanceDB that enables LLMs to directly interact with local documents through proxy RAG and hybrid search.

A Godot documentation query assistant based on Retrieval Augmented Generation (RAG), achieving intelligent Q&A through vectorization technology and semantic search.

This is an MCP server project that provides documentation support for the Godot RAG model, including functions such as document download, format conversion, chunk processing, and vector database construction