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Evidenceline

by autopilotyourworkflow
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Python
Hybrid

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    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

    A4.4/5.0

    Scored across 9 tools

    Disambiguation5/5

    Each tool owns a distinct task: data tidy/QA, review-item drill-down, per-well retrieval, limit lookup, rule comparison, paragraph checking, number filling, document search, and redaction reporting. The closest pairs (get_results vs tidy_lab_files and lookup_limit vs search_guidelines) are clearly separated by workflow step and output type.

    Naming Consistency5/5

    All nine tool names follow the same lowercase snake_case verb_noun pattern, from tidy_lab_files to show_redactions. Minor plural/singular variation such as get_results vs get_review_item is natural and does not break the convention.

    Tool Count5/5

    Nine tools is well within the ideal range and each one maps to a distinct step in the evidence-to-report workflow. The count feels deliberately scoped rather than padded or thin.

    Completeness4/5

    The core workflow is well covered: data ingestion/QA, review item drill-down, results retrieval, guideline comparison, report number filling, paragraph verification, document search, and redaction reporting. The main gap is that soil guideline values such as HIL A are only searchable as document passages rather than available through a structured lookup like the drinking-water values, so some soil reporting tasks require manual reading of cited pages.

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