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MCP Analytics Server

by Jojeda96
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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

    A4.1/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool serves a distinct purpose: dataset overview, column discovery, column statistics, overall churn summary, grouped churn analysis, and custom SQL fallback. The overlap between churn summary and churn by dimension is clearly differentiated by the latter's grouping parameter, and SQL is explicitly a last resort. No ambiguity remains for the agent.

    Naming Consistency5/5

    All tools follow a consistent verb_noun snake_case pattern: get_dataset_info, list_columns, describe_column, get_churn_summary, get_churn_by_dimension, run_readonly_sql. The verbs (get, list, describe, run) and noun phrases (dataset_info, columns, column, churn_summary, etc.) are uniform, creating a predictable and readable API surface.

    Tool Count5/5

    With 6 tools, the server is well-scoped for its purpose of analyzing a single churn dataset. Each tool addresses a distinct analytical need, and none feel redundant or extraneous. The count sits comfortably within the ideal 3-15 range.

    Completeness5/5

    The tool set covers the full analytics lifecycle: discovery (dataset info, columns), exploration (describe column), summary statistics (churn summary), dimension breakdowns (churn by dimension), and arbitrary ad-hoc queries (SQL fallback). The inclusion of read-only SQL ensures no analytical question remains unanswered, making the surface effectively complete for a read-only analytics server.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources