Skip to main content
Glama
Sign In
mcp-docparser

mcp-docparser

by Jasonzld
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

Related Servers

Alternatives to mcp-docparser

No user-submitted related servers found.

    Related Servers

    • MinerU MCP

      GermaniU
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents and MCP clients to convert PDFs, DOCX, PPTX, XLSX and images into structured Markdown with HTML tables and LaTeX formulas, either synchronously or as asynchronous tasks with page ranges. Runs entirely against a self-hosted parsing API over HTTP/SSE, so clients install no heavy dependencies and documents never leave the local network.
      1
      MIT

    • vectorise-mcp

      jameslovespancakes
      A
      license
      Not graded
      quality
      D
      maintenance
      Local MCP server that indexes folders of documents into a hybrid vector + keyword search index for Claude Desktop, with support for PDFs, Office files, and images via OCR.
      MIT

    • Techtenstein PDF MCP

      sathvic-kollu
      A
      license
      Not graded
      quality
      D
      maintenance
      MCP server that gives your Claude, Cline, or Cursor session the ability to extract text, tables, and metadata from any PDF URL — including scanned PDFs via OCR.
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.1/5.0

    Scored across 7 tools

    Disambiguation2/5

    Multiple tools have overlapping purposes: parse_document, extract_text, and ocr_image all extract text from images/documents, with descriptions that are nearly indistinguishable. analyze_image also overlaps with get_document_info for image metadata, making it hard for an agent to choose the right tool.

    Naming Consistency4/5

    All tool names use snake_case and most follow a verb_noun pattern (e.g., analyze_image, chunk_document, get_document_info). batch_ocr and ocr_image deviate slightly by starting with a noun or acronym, but the overall convention is clear and readable.

    Tool Count5/5

    Seven tools is well-scoped for a document parsing server, providing distinct capabilities for parsing, OCR, text extraction, metadata, image analysis, and chunking without being overwhelming or thin.

    Completeness4/5

    The surface covers core document parsing tasks: parsing, text extraction, OCR (including batch and language support), metadata retrieval, image analysis, and chunking. Minor gaps exist, such as tools for output saving or format-specific handling, but agents can work around them.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

    Resources