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Ads Analytics MCP

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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

    A3.9/5.0

    Scored across 16 tools

    Disambiguation5/5

    Each tool is scoped by a platform prefix (google_ads/meta/tiktok) plus a specific entity or metric, so even parallel tools like get_meta_hourly_performance and get_tiktok_hourly_performance or the two anomaly signals are clearly distinguishable by platform and purpose. There is no meaningful overlap where an agent could reasonably misselect.

    Naming Consistency5/5

    All data tools follow the consistent get_{platform}_{metric_or_entity} pattern (e.g. get_google_ads_keywords, get_meta_auction_rankings, get_tiktok_anomaly_signal). The single discovery tool list_clients deviates slightly but is a natural, predictable exception with no competing convention.

    Tool Count4/5

    16 tools sits at the upper edge of the ideal range, but the count is justified: three ad platforms each get parallel coverage of performance, hourly, and diagnostics. It is slightly heavy but every tool maps to a concrete analytical need rather than being filler.

    Completeness4/5

    The surface covers a strong analytics lifecycle across all three platforms: campaign performance, dayparting/hourly, auction rankings, anomaly detection, plus discovery. Minor asymmetries exist (Google Ads has no auction-rankings or anomaly tool, and Meta lacks ad-level performance), but these are workaroundable gaps rather than dead ends.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources