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knowledge-rag-mcp

by TTANF1

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    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI agents to search, deep-read, and build knowledge bases from Markdown, PDF, DOCX, and PPTX documents via MCP tools for retrieval, document navigation, and ingestion.
      12 npm
      638
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Enables AI agents to discover, read, search, and install Markdown-based knowledge (rules, skills, workflows) from a local directory via MCP tools.
      13
      270 npm
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      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to retrieve relevant context from local documents via MCP tools, supporting ingestion, semantic search, metadata filtering, and evidence inspection entirely on-device.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables coding agents to search local repositories through hybrid keyword and embedding retrieval, read matched chunks, and list indexed repos via MCP, without API keys or cloud services.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Provides AI agents with persistent knowledge storage, enabling them to store, search, and retrieve text, documents, and files using semantic and keyword search via MCP tools.
      32
      Apache 2.0

    TDQS

    A4/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool targets a distinct action in the RAG lifecycle: list, status check, preview, connect/index, search, and read. The preview/connect pair shares a resource but their descriptions clearly separate a read-only preview from a confirmed configuration step, so misselection is unlikely.

    Naming Consistency5/5

    All tools follow a consistent snake_case verb_noun pattern (list_knowledge_bases, get_setup_status, preview_knowledge_base, connect_knowledge_base, search_knowledge, read_document). No mixed conventions or vague verbs.

    Tool Count5/5

    Six tools map cleanly onto the setup-to-retrieval workflow with no filler. Each tool earns its place and the count is well-scoped for the domain.

    Completeness4/5

    The surface covers setup status, listing, preview, indexing/connect, search, and document read, which handles the core retrieval lifecycle. However, there is no way to disconnect/delete or reindex a knowledge base, leaving lifecycle management gaps an agent may hit.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues