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"Exploring RAG through defined workspaces" matching MCP servers:

  • dryad-rag-mcp by DryadAI

    dryad-rag-mcpofficial

    DryadAI
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    quality
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    maintenance
    Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.
    3
    MIT

  • local-Rag

    zongzi-zongzhi
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    quality
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    maintenance
    A local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.
    7
    MIT

  • Local RAG

    shinpr
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    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
    3,271
    365
    MIT

  • mcp-local-rag

    damoqiongqiu
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    A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.
    16
    153
    13
    MIT

  • MCP RAG Server

    LuizDoPc
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    quality
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    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

  • source-rag-mcp

    hanhy06
    F
    license
    B
    quality
    B
    maintenance
    MCP server for searching, browsing, and analyzing decompiled Minecraft source code locally. Supports symbol lookup, text search, reference lookup, and lightweight RAG.
    11

  • MCP RAG

    santis84
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    license
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    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.
    11
    13
    1
    MIT

  • mcp-rag-server

    kwanLeeFrmVi
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    license
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    quality
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    maintenance
    An MCP server that indexes documents and serves relevant context to LLMs via Retrieval Augmented Generation (RAG).
    115
    36
    MIT

  • claude-rag-mcp

    VXConsulting
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    quality
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    maintenance
    MCP server providing RAG context and failure capture for Claude Code, enabling semantic search across project knowledge and storing/analyzing failures.
    1
    MIT

  • concept-rag

    m2ux
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    license
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    quality
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    maintenance
    Enables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.
    3
    MIT

  • mcp-rag

    shawndrake2
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    quality
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    maintenance
    Enables retrieval-augmented generation over a local markdown corpus, allowing grounded, cited answers via an MCP tool or CLI.
    13
    MIT

  • mcp-rag-server

    shabib87
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    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

  • 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

  • RAG Memory MCP

    ttommyth
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    quality
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    maintenance
    An advanced MCP server providing RAG-enabled memory through a knowledge graph with vector search capabilities, enabling intelligent information storage, semantic retrieval, and document processing.
    123
    47
    MIT

  • mcp-local-rag

    xiuxiansk
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    license
    A
    quality
    C
    maintenance
    MCP server that enables local hybrid semantic and keyword search over private PDF, DOCX, Markdown, and text documents without sending data to embedding APIs.
    9
    3,271
    MIT