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MCP Knowledge Librarian

MCP Knowledge Librarian

by Schimmilab

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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables assistants to search a personal library of local documents using hybrid BM25 and embeddings, retrieve exact passages with file and page citations, and manage collections entirely on the user's own machine.
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables local AI agents to perform hybrid search (BM25 + vector) over privately indexed local files, including notes, PDFs, DOCX, source code, and OCR'd images. All indexing and retrieval stay on the machine with read-only, whitelist-scoped access.
      3
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.
      -
    • A
      license
      B
      quality
      C
      maintenance
      Enables AI agents to search any local folder through a private SQLite FTS5 index that extracts text from PDFs, Office documents, plain text, code and zip listings, returning ranked matches instead of slow ls/grep scans. It also supports metadata and glob listing, paged retrieval of already-extracted text, duplicate detection, and incremental background re-indexing with secrets and caches excluded by default.
      8
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.
      6
      MIT
    • F
      license
      A
      quality
      D
      maintenance
      Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.
      4
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