"MCP with RAG capabilities" matching MCP servers:

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

Enables any MCP host to search and answer over your own documents with hybrid BM25+dense retrieval, cross-encoder reranking, and grounded, cited responses that refuse when no evidence is found.

A fully modular RAG system with MCP protocol integration, enabling hybrid retrieval, multimodal support, and dual-mode transport for connecting to AI assistants like GitHub Copilot and Claude Desktop.

Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.

MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

Enables local knowledge base management with retrieval-augmented generation (RAG), providing semantic search, document reading, listing, and Q&A via MCP tools and REST endpoints, all running locally without cloud dependencies.

A local MCP server that integrates with Claude Desktop, enabling RAG capabilities to provide Claude with up-to-date private information from custom LlamaCloud indices.

An MCP server that ingests Spring Boot REST microservices from GitHub, builds a request-flow ontology, and enables natural-language queries about code with flow-aware answers and citations.

Enables advanced RAG with knowledge graphs, supporting document ingestion, multimodal extraction, and multiple query modes (naive, local, global, hybrid) via the Model Context Protocol.

A powerful AI service platform that provides complete MCP tool calling capabilities and RAG knowledge base functionality, enabling users to connect to multiple MCP servers and perform intelligent document search.

Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.

Turns any folder of PDFs, markdown, and text files into a local, queryable knowledge base exposed as an MCP server. Enables MCP-compatible agents to semantically search indexed documents, retrieve relevant passages with source and relevance scores, list or reindex documents, and inspect cache and token usage — instead of reading whole files into context.

A modular RAG server with hybrid search and LLM rerank, exposing 3 MCP tools for AI clients like Claude Desktop and Cursor.

A memory-enhanced MCP server with RAG database and expiring memory capabilities, enabling AI systems to store and retrieve memories and documents with semantic search.

A powerful MCP server for Excel operations with seamless Supabase Storage integration.

A pluggable, observable modular RAG framework that exposes query knowledge hub, list collections, and get document summary tools via MCP, enabling AI assistants to perform hybrid search and document retrieval with reranking.

Transforms static gemini-cli documentation into a queryable RAG service, enabling developers to ask questions about Gemini CLI in natural language and receive instant, accurate answers based on the official documentation directly within their workflow.

A pluggable, observable modular RAG framework exposing tools via MCP protocol for AI assistants like Copilot/Claude to query knowledge bases, list collections, and retrieve document summaries.

Provides AI agents with complete web search, crawling, and RAG capabilities through a Docker-based solution combining Model Context Protocol, Crawl4AI, SearXNG, and Supabase.