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isp-analytics-mcp

isp-analytics-mcp

by KaiqueSavi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

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    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

    B3.4/5.0

    Scored across 13 tools

    Disambiguation4/5

    The tools mostly target clearly distinct analytical angles: the churn family splits cleanly into trend (by_month), reasons, segment breakdown, and risk scoring, and the delinquency family into aging buckets, due-month flow, and customer list. Minor overlap exists between ticket_stats and tickets_by_month, since the latter covers a time dimension that arguably belongs in the former, and overview partially duplicates churn/delinquency KPIs at a single snapshot.

    Naming Consistency4/5

    Nearly all tools follow a consistent snake_case `<domain>_<qualifier>` pattern (churn_by_month, churn_reasons, delinquency_aging, ticket_stats), which makes the set predictable. The outliers are the bare `overview` and the verb-first `run_sql`, but they are few and their roles are self-evident.

    Tool Count5/5

    13 tools sits comfortably in the well-scoped range, and each one maps to a concrete analytical question (churn, delinquency, retention, sales, tickets). No padding is visible, and run_sql acts as a deliberate escape hatch rather than bloat.

    Completeness4/5

    The surface covers the core ISP analytics lifecycle: KPIs, churn in four dimensions, delinquency in three, cohort retention, sales acquisition, and support tickets, plus a read-only SQL fallback. The main gap is revenue evolution over time — sales_by_month reports only new MRR, with no total MRR/ARPU trend or expansion/contraction view, though run_sql can backfill this.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

    Resources