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GroupDocs.Search MCP Server

GroupDocs.Search MCP Server

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    • 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
    • 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
      A
      quality
      B
      maintenance
      Indexes local documents (PDF, Word, Markdown, text) into a SQLite database for AI agents to search and retrieve bounded, source-located passages. Runs fully locally with optional OCR, preserving privacy.
      5
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI assistants to privately search, read, and cite the user's own files on their computer through hybrid semantic and exact-word search across indexed folders.
      AGPL 3.0
    • A
      license
      A
      quality
      B
      maintenance
      Enables privacy-first, offline indexing and full-text search across local document workspaces while automatically redacting PII/secrets and enforcing path access controls before snippets reach LLMs.
      3
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to run full-text search inside Word, PowerPoint, Excel, PDF, and plain-text files by extracting their contents into a single local SQLite FTS5 index. Exposes tools to search documents with ranked highlighted snippets and human-readable locators, list indexed files, and incrementally build or refresh the index.
      MIT