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"Using RAG to Provide Documentation to LLMs" matching MCP servers:

  • MCP PDF to PGVector

    sanskritiparmendranaval
    F
    license
    Not graded
    quality
    C
    maintenance
    MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

  • llms-txt-mcp

    tenequm
    A
    license
    Not graded
    quality
    C
    maintenance
    Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.
    3
    MIT

  • rag

    FrameMuse
    F
    license
    Not graded
    quality
    B
    maintenance
    A CLI tool and MCP server that turns markdown documentation into a searchable, queryable knowledge base.
    280

  • knowledge-rag

    lyonzin
    A
    license
    A
    quality
    A
    maintenance
    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.
    13
    257
    MIT

  • Local RAG

    shinpr
    A
    license
    A
    quality
    A
    maintenance
    Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.
    9
    3,968
    371
    MIT

  • rag-mcp

    NextLevelManagementAdvisors
    A
    license
    A
    quality
    B
    maintenance
    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.
    4
    AGPL 3.0

  • MCP-Markdown-RAG

    Zackriya-Solutions
    A
    license
    A
    quality
    D
    maintenance
    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.
    3
    56
    Apache 2.0

  • rag-pipeline

    jansc4
    F
    license
    A
    quality
    C
    maintenance
    Enables Claude to search a local hybrid retrieval index of research papers and ingest new PDFs, providing research-paper memory queryable directly through natural language.
    2

  • MCP RAG

    kalicyh
    F
    license
    A
    quality
    C
    maintenance
    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.
    10
    6

  • MCP RAG Server

    LuizDoPc
    A
    license
    A
    quality
    D
    maintenance
    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.
    4
    245
    MIT

  • MCP RAG

    santis84
    A
    license
    B
    quality
    D
    maintenance
    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.
    11
    9
    1
    MIT

  • MCP RAG Server

    0xrdan
    A
    license
    Not graded
    quality
    C
    maintenance
    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.
    245
    MIT

  • Markdown RAG

    ashrobertsdragon
    A
    license
    Not graded
    quality
    D
    maintenance
    A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.
    1
    MIT

  • rag-mcp-server

    weitingyen168
    A
    license
    Not graded
    quality
    C
    maintenance
    RAG document search MCP server that allows AI assistants to search a local document set and retrieve grounded passages via keyword (SQLite FTS5) or semantic (Chroma) backends.
    MIT

  • mcp-rag-bridge

    zyay
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI agents to query and manage a document knowledge base via MCP, with RAG-powered search and grounded answers with citations.
    MIT

  • mcp-rag-server

    FORIFOR
    A
    license
    Not graded
    quality
    D
    maintenance
    MCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.
    MIT