stripe-intelligence
Provides 20 curated, tested API tools for Stripe, focusing on revenue summaries, subscription stats, MRR breakdowns, churn analysis, fraud detection, and payouts. Free tools cover charges, failed payments, products, disputes, refunds, and payment methods; paid tools include top customers, coupons, subscription lifecycle, AI revenue forecast, and AI anomaly detection. SaaS founders, revenue analysts, and developers use it to query critical business metrics without navigating 100+ raw Stripe APIs.
Overview
The stripe-intelligence MCP server delivers 20 curated and live-tested tools for Stripe API access, prioritizing essential metrics like revenue, MRR, churn, fraud, and payouts. Unlike the official Stripe MCP's 100+ unfiltered tools, this server focuses on what matters, fixing crashes (e.g., get_subscription_stats) found in community versions. Free tier offers 10 basic tools; paid tier ($29/mo) adds 10 advanced analytics tools via VibeDNA MCP Studio.
Key Capabilities
Free Tools (10):
- revenue summary: Aggregates total revenue across time periods.
- charges: Lists all payment charges with details.
- subscription_stats: Retrieves active, canceled, and trial subscription counts.
- failed_payments: Identifies and details payment failures.
- products: Queries product catalog and pricing.
- disputes: Manages and lists customer disputes.
- payment_methods: Views saved payment methods per customer.
- payouts: Tracks bank payouts and schedules.
- refunds: Handles refund records and amounts.
Paid Tools (10):
- mrr_breakdown: Breaks down monthly recurring revenue by cohorts.
- churn_analysis: Calculates churn rates and reasons.
- top_customers: Ranks customers by lifetime value or spend.
- coupons: Analyzes coupon usage and impact.
- fraud: Detects fraudulent transactions.
- subscription_lifecycle: Tracks subscription states over time.
- ai_revenue_forecast: Predicts future revenue using AI.
- ai_anomaly_detection: Flags unusual patterns in data.
Use Cases
- A SaaS founder runs mrr_breakdown and churn_analysis weekly to assess growth and retention, then uses ai_revenue_forecast for planning.
- Finance teams query disputes, failed_payments, and payouts to reconcile accounts and resolve issues.
- Developers build dashboards with subscription_stats, top_customers, and revenue summary for real-time monitoring.
- Security ops apply fraud and ai_anomaly_detection to scan charges for risks.
Who This Is For
SaaS operators monitoring MRR and churn, finance analysts handling payouts and disputes, developers integrating Stripe metrics into apps, and revenue teams needing AI-driven forecasts without raw API complexity.