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Google Trends MCP Server

by mamrrez
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Python
Hybrid

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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

    A3.6/5.0

    Scored across 32 tools

    Disambiguation4/5

    Most tools target clearly distinct operations, and descriptions go to lengths to distinguish them (e.g. trending_now vs trending_feed vs trend_details). However there is a dense cluster of time-series analysis tools (interest_over_time, daily_history, compare_periods, compare_many, trend_momentum, find_spikes, seasonality) whose boundaries, while documented, still invite misselection.

    Naming Consistency4/5

    Nearly all names are snake_case and readable, with some verb_noun forms (find_location, compare_locations, clear_cache) and some noun phrases (seasonality, daily_history, content_calendar). Conventions are mixed in verb/noun ordering but no camelCase or chaos, so it stays predictable.

    Tool Count3/5

    At 32 tools this sits at the heavy end for a single-domain server. The breadth of Google Trends analysis justifies many of them, but several analysis tools (seasonality vs content_calendar, related_queries vs keyword_ideas) overlap enough that consolidation would help.

    Completeness5/5

    The surface covers trending now, historical trending via snapshots, over-time and by-region interest, related queries/topics, multi-term and multi-period comparisons, seasonality, momentum, spikes, share of search, watchlists and raw SQL. Core Trends workflows and edge cases (anonymous-session limitations, endpoint health) are all addressed with no obvious dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources