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"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP servers:

  • mcp-rag-server

    shabib87
    A
    license
    -
    quality
    F
    maintenance
    A Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.
    1
    MIT

  • rag-mcp

    Swabhimanc
    F
    license
    -
    quality
    C
    maintenance
    Provides read-only MCP tools for hybrid semantic and keyword search over locally indexed PDF documentation, with citations and context retrieval for LLM agents.

  • LangGraph RAG MCP

    pedarias
    F
    license
    -
    quality
    D
    maintenance
    Retrieval-Augmented Generation system serving LangGraph documentation through the Model Context Protocol, enabling semantic search and context-aware responses.
    16

  • 1C RAG MCP

    royfincher25-source
    A
    license
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    quality
    D
    maintenance
    MCP server for RAG-based search over 1C Enterprise configuration documentation, enabling natural language queries to find objects like справочники, документы, and отчеты.
    MIT

  • porta-rag-mcp

    Ira-Korshunova
    F
    license
    -
    quality
    C
    maintenance
    MCP server exposing a RAG knowledge base as read-only tools (search_knowledge_base, ask_knowledge_base, kb_stats, kb_diagnostic) for AI clients like Claude Desktop and Cursor, enabling token-efficient document retrieval and Q&A.

  • Kedro RAG MCP

    SajidAlamQB
    F
    license
    -
    quality
    D
    maintenance
    An MCP server that enables users to query Kedro framework documentation using retrieval-augmented generation. It builds a local knowledge base from documentation files to help users navigate and apply Kedro's data science pipeline framework.

  • kb-rag-mcp

    n1khll
    F
    license
    -
    quality
    C
    maintenance
    Standalone MCP server exposing agentic-RAG tools (hybrid search, article retrieval, category listing) over a pgvector-backed knowledge base with bearer-token auth, designed for deployment on Render.

  • Gemini CLI RAG MCP

    pedarias
    A
    license
    -
    quality
    D
    maintenance
    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.
    8
    MIT

  • Knowledge Retrieval Server

    cskwork
    F
    license
    B
    quality
    D
    maintenance
    A BM25-based MCP server that enables document search and retrieval across structured domains of knowledge content, allowing Claude to search and reference documentation when answering questions.
    4
    1

  • Godot RAG MCP Server

    weekitmo
    A
    license
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    quality
    D
    maintenance
    Provides access to Godot engine documentation through a Retrieval-Augmented Generation (RAG) system using ChromaDB. It enables users to query processed Godot documentation and technical chunks for development assistance within MCP-compatible environments.
    28
    MIT

  • Markdown RAG Documentation

    andnp
    A
    license
    -
    quality
    B
    maintenance
    Enables semantic search over local Markdown documentation using hybrid retrieval combining embeddings, keyword search, and graph traversal with automatic file watching and zero-configuration setup.
    2
    MIT

  • Documentation Fetcher & RAG

    mikkelkrogsholm
    F
    license
    -
    quality
    D
    maintenance
    Enables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.

  • nist-rag-mcp-server

    nourawada02
    F
    license
    -
    quality
    B
    maintenance
    A local FastMCP STDIO server exposing two tools (ask_nist_rag and get_nist_visual) and one read-only resource (nist://visuals/catalog) that provides grounded question answering over NIST AI RMF documents using local RAG with selective OCR, hybrid retrieval, and verified visual figure retrieval.