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Metricairn

by rnepal2
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Python
Remote

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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

    B3.1/5.0

    Scored across 20 tools

    Disambiguation3/5

    Several tools overlap heavily: run_query subsumes query_metrics and breakdown (total/timeseries/breakdown modes), while compare and investigate_change both perform equal-duration period comparisons, and ask overlaps with the structured query tools. Funnel/goal/retention reports and the note/investigation lifecycle are clearly distinct, but the querying layer has fuzzy boundaries that invite misselection.

    Naming Consistency3/5

    There is a recognizable verb_noun pattern for many tools (list_metrics, query_metrics, run_query, list_goals, get_realtime, detect_anomalies, investigate_change, get_investigation), but it is broken by bare nouns and ambiguous names (breakdown, compare, ask, funnel_report, revenue_attribution, mcp_usage). The mix is readable but not a predictable convention.

    Tool Count3/5

    At 20 tools this is on the heavy side for an analytics server, and the count is inflated by overlapping query paths that could be consolidated (e.g. query_metrics/breakdown folded into run_query). It is not egregious—each tool maps to a plausible analytics task—but it sits at the borderline of too many.

    Completeness4/5

    The surface covers the analytics lifecycle well: discovery (list_metrics, list_dimension_values), querying, funnel/goal/retention/attribution reporting, anomaly detection, realtime, data-health checks, and investigation save/review. Minor gaps exist (e.g. no explicit breakdown-by-metric filter builder output, no scheduling), but core workflows are covered without dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources