"Information or resources related to 'rag'" matching MCP servers:

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

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.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.

Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.

Enables local-first hybrid knowledge retrieval from authorized Markdown and plain-text files, combining full-text and vector search with reranking and traceable source references via a single search tool.

Enables semantic search and question answering over a knowledge base using hybrid retrieval and grounded answers, all running offline with no API keys.

Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.

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.

Searches local markdown files using TF-IDF retrieval and serves full documents as resources.

Offline RAG MCP server that indexes documents into SQLite with hybrid vector and lexical search, and enables natural language queries via Claude.

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

Enables users to build and manage a complete retrieval-augmented generation pipeline through conversation, including file ingestion, collection management, hybrid search, reranking, citations, and a guided setup wizard.

"primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨

Exposes Obsidian notes as a semantic search and RAG knowledge base over MCP, enabling AI assistants to index, retrieve, and analyze personal notes via natural language.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

A RAG-based knowledge base system supporting document processing, semantic search, and intelligent Q\&A with multiple AI model integrations.

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

Fetches a URL and returns the main content as clean Markdown, using plain HTTP when possible and headless Chromium for JavaScript-rendered or bot-protected pages.