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"RAG MCP servers with quality validation" matching MCP servers:

  • MCP RAG with ChromaDB

    CyprianFusi
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    Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
    Last updated
    1
    MIT

  • esg-mcp-servers

    freminder
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    quality
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    Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.
    Last updated
    MIT

  • rag-mcp

    NextLevelManagementAdvisors
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    quality
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    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.
    Last updated
    4
    AGPL 3.0

  • MCP RAG

    kalicyh
    F
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    quality
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    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.
    Last updated
    10
    5

  • MCP RAG Server

    LuizDoPc
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    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.
    Last updated
    4
    37
    1
    MIT

  • MCP RAG

    santis84
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    license
    B
    quality
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    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.
    Last updated
    11
    7
    1
    MIT

  • MCP-RAG Server

    apatoliya
    F
    license
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    quality
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    maintenance
    A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.
    Last updated
    3

  • Markdown RAG

    ashrobertsdragon
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    quality
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    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.
    Last updated
    1
    MIT

  • mcp-rag-mini

    Pinger1456
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    quality
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    maintenance
    A minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.
    Last updated
    MIT

  • RAG MCP Server

    tungetti
    A
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    quality
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    A Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) operations. It provides tools for building and querying vector-based knowledge bases from document collections, enabling semantic search and document retrieval capabilities.
    Last updated
    3
    MIT

  • mcp-rag-server

    FORIFOR
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    quality
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    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.
    Last updated
    MIT

  • MCP-Markdown-RAG

    Zackriya-Solutions
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    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.
    Last updated
    55
    Apache 2.0

  • MCP-RAG

    AnuragB7
    A
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    quality
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    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.
    Last updated
    10
    MIT