Building an MCP server so Claude can query my SaaS analytics directly

Last week I shipped a Model Context Protocol (MCP) server for my analytics SaaS. Now Claude Desktop, Cursor, and any MCP compatible client can query traffic, revenue, and funnel data directly.

This is a walkthrough of how I built it, what worked, and a couple of patterns that surprised me.

What MCP is, briefly

MCP is a protocol that lets AI clients invoke tools and read resources from external servers. Think of it as REST for LLM tool calling, with a stable schema and discovery.

Server skeleton

import { Server } from '@modelcontextprotocol/sdk/server/index.js'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'

const server = new Server({
  name: 'zenovay',
  version: '1.0.0',
}, {
  capabilities: {
    tools: {},
    resources: {}
  }
})

Defining tools

server.setRequestHandler('tools/list', async () => ({
  tools: [
    {
      name: 'get_traffic',
      description: 'Get pageview and visitor counts for a site over a date range',
      inputSchema: {
        type: 'object',
        properties: {
          site: { type: 'string' },
          from: { type: 'string' },
          to: { type: 'string' }
        },
        required: ['site', 'from', 'to']
      }
    }
  ]
}))

The pattern that surprised me: structured returns

Way better than raw JSON: return a short natural language summary plus the data. Claude uses the summary for its response and the JSON for follow up questions.