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"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

  • Agentic RAG MCP

    enached134-ctrl
    A
    license
    A
    quality
    A
    maintenance
    A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
    3
    1
    MIT

  • agentic-rag-knowledge

    jayanthlocam
    A
    license
    -
    quality
    B
    maintenance
    Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.
    MIT

  • 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
    252
    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 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
    115
    MIT

  • mcp-rag-local

    renl
    A
    license
    B
    quality
    C
    maintenance
    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.
    5
    18
    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
    13
    1
    MIT

  • Markdown RAG

    ashrobertsdragon
    A
    license
    -
    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
    -
    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

    AnuragB7
    A
    license
    -
    quality
    C
    maintenance
    An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.
    10
    MIT

  • RAG MCP Server

    sglwsjxh
    A
    license
    -
    quality
    A
    maintenance
    A RAG knowledge base MCP server that adds vector search and reranking capabilities to opencode, supporting multimodal embeddings, multiple knowledge bases, and local storage.
    1
    MIT

  • MCP-RAG

    seanshin0214
    A
    license
    -
    quality
    -
    maintenance
    Turns Claude Desktop into a personal document question-answering system using local vector search. Index PDF, TXT, and Markdown documents into collections and get answers based strictly on your documents with zero hallucination.
    13