SaaS Metrics
Delivers programmatic access to core SaaS KPIs such as MRR, ARR, churn rate, LTV, and user acquisition metrics via the MCP protocol. Data analysts, product managers, and finance teams query this data to build custom reports, monitor growth, and feed into BI tools. Applications include automated dashboard updates and cohort analysis for SaaS operations.
Overview
The SaaS Metrics MCP server provides standardized API access to key performance indicators (KPIs) for Software as a Service products. It aggregates and exposes metrics like monthly recurring revenue (MRR), annual recurring revenue (ARR), churn rates, lifetime value (LTV), and customer acquisition cost (CAC) through the Model Context Protocol, enabling integration with AI models, scripts, and analytics pipelines.
Key Capabilities
- get_mrr: Fetches current and historical MRR data, broken down by segments like new, expansion, and reactivation revenue.
- get_arr: Retrieves annualized revenue projections with growth trends over time.
- calculate_churn: Computes cohort-based churn rates, including revenue and customer churn.
- user_growth_metrics: Returns active users, new signups, upgrades, and downgrades with time-series data.
- ltv_cac_ratio: Analyzes customer lifetime value against acquisition costs for profitability insights.
These functions support filtering by date ranges, customer segments, and plans.
Use Cases
- A SaaS product manager runs get_mrr and calculate_churn weekly to track revenue retention and identify at-risk cohorts for targeted interventions.
- Finance teams use get_arr and ltv_cac_ratio to generate investor reports with scripted queries into spreadsheet tools.
- Growth analysts query user_growth_metrics to correlate signups with marketing campaigns in BI dashboards like Looker or Tableau.
- AI developers integrate all metrics into models for forecasting churn via MCP calls in real-time applications.
Who This Is For
Target users include SaaS data analysts monitoring KPIs, product managers optimizing retention, finance professionals preparing board reports, and developers building automated analytics pipelines. It suits teams at scaling SaaS companies needing reliable, queryable metric data without manual exports.