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systemlink-mcp

by rhit-folayaod
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Python
Hybrid

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    TDQS

    A4.1/5.0

    Scored across 12 tools

    Disambiguation5/5

    Each tool targets a distinct resource and action: yield aggregation, result queries, step-level failure analysis, measurement statistics, calibration status, asset/system/product/spec queries, file listing, result property updates, and notebook execution. There is no overlap; even query_results and summarize_yield differ clearly (raw counts vs pass/fail ratio). Descriptions explicitly disambiguate boundaries.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: summarize_yield, query_results, get_failing_steps, list_calibration_due, execute_notebook, etc. Retrieval verbs vary (query vs list vs get) but are semantically appropriate and predictable, with no mixing of conventions.

    Tool Count5/5

    Twelve tools is well within the ideal 3–15 range and appropriately scoped for a SystemLink MCP server covering test data, asset management, and notebook execution. Each tool adds distinct value; none feel redundant or superfluous.

    Completeness4/5

    The surface covers the core read workflows (yield, results, failing steps, measurements, assets, systems, products, specs, files) plus limited write actions (update_result_properties, execute_notebook). Notable gaps include lack of update/delete for assets (acknowledged as unsupported by the underlying client), no single-result full detail view beyond failing steps, and no creation of test results. However, these are minor for the apparent purpose of monitoring and analysis.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources