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pdf4me-mcp

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Python
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    TDQS

    B3.1/5.0

    Scored across 92 tools

    Disambiguation2/5

    Many tools have overlapping purposes, especially the 14+ process_* AI extraction tools and the multiple split_pdf_by_* tools. For example, split_pdf_by_barcode and split_pdf_by_swiss_qr both split by barcode, and process_invoice vs process_receipt may be ambiguous for similar document types. Descriptions are detailed, but the sheer volume of similar tools makes misselection likely.

    Naming Consistency4/5

    Tool names generally follow a consistent verb_noun pattern (add_*, convert_*, extract_*, process_*). Minor inconsistencies exist, such as merge_multiple_pdfs vs merge_pdf_overlay, and generate_document_single vs generate_documents_multiple, but overall naming is readable and predictable.

    Tool Count1/5

    With 92 tools, the server is extremely overloaded. The typical well-scoped server has 3-15 tools; 92 is far beyond any reasonable threshold and will overwhelm agents. This extreme count likely leads to context bloat and decision paralysis.

    Completeness4/5

    The tool set covers a comprehensive range of PDF, image, conversion, and AI extraction operations, including create, read, update, delete, transform, and metadata operations. Minor gaps exist (e.g., no page reordering, no generic text editing beyond find-and-replace), but the coverage is broad and most common workflows are supported.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

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