"Information or Uses for a Rag" matching MCP servers:

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

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 Model Context Protocol server for reading TiDB RAG knowledge base, enabling browsing, searching, and vector search of knowledge entries.

Keyless local RAG over your own documents (ChromaDB) with traceable citations, exposed as MCP tools and a FastAPI endpoint.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.

Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.

Provides RAG tools with local vector retrieval and web fallback using Firecrawl, enabling document ingestion and querying through MCP stdio transport.

A Model Context Protocol server for indexing and searching local document collections with hybrid BM25+vector search, optimized for Korean and English, supporting DOCX, PDF, audio, and more.

MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.

An MCP server for RAG using Qdrant that automatically indexes documents from directories and generates search tools for each collection.

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.

Exposes document ingestion, retrieval (vector, vectorless, hybrid), and multi-turn chat tools for a LangGraph-powered RAG pipeline with streaming answers.

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.