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ai-usage-metrics-mcp

by charlie-818
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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

    A3.7/5.0

    Scored across 5 tools

    Disambiguation4/5

    Each tool has a distinct primary purpose: write (log_model_call), search raw calls (search_model_calls), list session summaries (list_sessions), fetch session-scoped calls (get_session_calls), and aggregate stats (get_aggregate_metrics). However, get_session_calls overlaps with search_model_calls since a session filter could achieve the same result, and search/aggregate both operate on the same call set with filters.

    Naming Consistency5/5

    All names follow a consistent snake_case verb_noun pattern (log_, search_, list_, get_, get_). Verbs vary appropriately by operation while the structure stays predictable and readable.

    Tool Count5/5

    Five tools are well-scoped for a focused usage-metrics server: one write path, three read/query paths, and one aggregation path. Each tool earns its place without redundancy or gaps in count.

    Completeness4/5

    The core lifecycle is covered: logging calls, retrieving them, session grouping, and aggregation. Minor gaps exist around deletion/retention and explicit session creation, but agents can work around these for typical metrics workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources