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Study Errorbook

Study Errorbook

by SAKURAfan1023
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Python
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    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

    A3.6/5.0

    Scored across 32 tools

    Disambiguation3/5

    The toolset spans distinct stages, but several read/query tool families overlap significantly (read_document vs read_element vs read_evidence vs read_element_evidence; read_practice_feedback vs read_practice_history). Their descriptions are detailed and help, yet the sheer number of superficially similar 'read_*' and 'list_*' tools creates real misselection risk.

    Naming Consistency4/5

    Almost all tools use snake_case with a verb_noun pattern (import_document, read_study_record, update_review_plan), which is predictable. Minor deviations like workspace_status (noun phrase) and compound names such as resume_job_observation keep it from perfect consistency.

    Tool Count2/5

    32 tools far exceed the typical well-scoped range of 3–15 and land in the 'too many' bucket per the rubric. Although each tool maps to a real sub-operation in a complex study-errorbook workflow, the volume imposes substantial cognitive load and coordination burden.

    Completeness4/5

    The surface covers import, OCR parsing, evidence extraction, study-record CRUD, practice recording/history, review planning, and export—a broad lifecycle. Only minor gaps remain, such as explicit parse-job cancellation or bulk study-record management, which agents can likely work around.

    Maintenance

    ActivitySlowing
    ResponsivenessNo recent community issues

    Resources