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coldchain-mcp

by MatuZale
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Python

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    TDQS

    A3.9/5.0

    Scored across 4 tools

    Disambiguation4/5

    Each tool targets a distinct operation: summary_stats computes aggregate measures, detect_threshold_breaches finds compliance violations, detect_anomalies finds statistical outliers, and segment_journey splits logs into trips. The purposes are clearly differentiated by domain function, though summary_stats and detect_anomalies both operate on raw series and could theoretically be confused—but their outputs are clearly different.

    Naming Consistency4/5

    All tools use snake_case and follow a consistent verb_first pattern: summary_stats (slightly verb-less), detect_threshold_breaches, detect_anomalies, segment_journey. The naming is coherent and predictable, with only 'summary_stats' deviating slightly from the verb-object convention used by the others.

    Tool Count5/5

    Four tools is a reasonable, focused scope for a cold-chain analytics server. Each tool addresses a distinct, valuable analysis task and none feels like filler. The count is on the lean side but entirely appropriate for the narrow domain.

    Completeness4/5

    The set covers core cold-chain analysis workflows: summary stats, threshold breach detection (key for compliance), anomaly detection, and journey segmentation. However, there are notable gaps—no tool for loading/filtering raw data, no visualization/serialization of results, and no export or alerting tools. For a cold-chain MCP server, CRUD-style operations aren't expected, but a data-cleaning or filtering tool would be a natural addition.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources