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SAKURAfan1023

Scholar Library

by SAKURAfan1023
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Python
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    TDQS

    B3.4/5.0

    Scored across 38 tools

    Disambiguation4/5

    Tools are mostly well separated by resource and action (e.g., search_literature vs search_library, parse_local_pdf vs parse_document). Some adjacent read/extract/parse-job tools (read_extraction vs read_parse_result, get_parse_job vs retry_parse vs resume_parse_observation) require careful reading, but descriptions clarify boundaries.

    Naming Consistency4/5

    All names use snake_case and nearly all follow a verb_noun or action_object pattern. workspace_status is a minor noun-only exception, but the overall naming convention is predictable and readable.

    Tool Count2/5

    38 tools is above the typical 3-15 band and exceeds the 25+ heavy threshold. The parsing, extraction, and job-management pipeline contains many granular tools that could be consolidated for easier agent selection.

    Completeness4/5

    The surface covers project setup, import, parsing, search, evidence building, export, deletion, backup/restore, and more. Minor lifecycle gaps exist (e.g., no explicit project-level delete/list beyond workspace_status), but major research workflows are well covered.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources