Why Your AI Agent Needs Subscription Analytics

Building a RevenueCat Charts API MCP Server that gives AI agents direct access to your subscription metrics

The Problem: Agents Can't See Your Dashboard

You've built an AI agent that helps manage your subscription app. It can write code, review PRs, and analyze logs. But when you ask "What's our churn rate this month?", it goes blank.

The problem is simple: your agent can't see your RevenueCat dashboard. It doesn't have access to the subscription analytics that drive your business decisions. Every time you need a metric, you context-switch — open the dashboard, find the chart, interpret the data, then manually relay it back to your agent conversation.

This is the "last mile" problem of agentic AI: agents are incredibly capable at reasoning, but they're blind to proprietary data behind authentication walls.

The Solution: MCP Server for Charts API

With RevenueCat's new Charts API v2 (released February 2026), we can now give agents direct, programmatic access to subscription analytics. And with the Model Context Protocol (MCP), we can package this as a tool that any AI agent can use.

MCP is an open standard that lets AI models discover and use external tools. Think of it as USB for AI — a universal connector that works across Claude, GPT, and any MCP-compatible agent.

Architecture

Real Conversation Example

Here's what a real interaction looks like once the MCP server is connected:

Why This Matters for Agent Developers

The agent ecosystem is moving fast. We've solved code generation, web search, and file manipulation. But the real value unlock is connecting agents to proprietary business data.

5-Minute Quick Start

// Claude Code: ~/.claude/mcp_servers.json
{
  "revenuecat-charts": {
    "command": "npx",
    "args": ["revenuecat-charts-mcp"],
    "env": {
      "REVENUECAT_API_KEY": "sk_your_key",
      "REVENUECAT_PROJECT_ID": "proj1ab2c3d4"
    }
  }
}