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name: mixpanel-mcp-connector description: "MCP connector for Mixpanel product analytics – enables AI-powered event analysis, funnel building, and user behavior insights through Claude" license: Proprietary/API tags: ["data","analytics","mcp","mixpanel","product"] difficulty: intermediate time_to_master: "4-8 weeks" version: "1.0.0"

Mixpanel MCP Connector

Overview

This skill enables Claude to interact with Mixpanel through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Mixpanel's REST API, allowing natural language control of Mixpanel operations, intelligent automation, and AI-powered assistance for Mixpanel workflows.

When to Use This Skill

• Event analysis and trend identification via AI
• Funnel creation and conversion analysis
• Cohort definition and retention analysis
• Custom report generation
• User segmentation and behavioral analysis

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Mixpanel         │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (REST API      )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Mixpanel operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.

Key Endpoints/Interfaces

/api/2.0/events, /api/2.0/funnels, /api/2.0/retention, /api/2.0/segmentation, /api/2.0/jql

Implementation

// Mixpanel MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "mixpanel-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Mixpanel resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Mixpanel REST API
    const response = await fetch(`${BASE_URL}/api/2.0/events`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Mixpanel",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}/api/2.0/events`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Mixpanel data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}/api/2.0/events`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "mixpanel-mcp-connector": {
      "command": "node",
      "args": ["path/to/mixpanel-mcp-connector/index.js"],
      "env": {
        "MIXPANEL_API_KEY": "your-api-key",
        "MIXPANEL_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Mixpanel API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Mixpanel API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Build a funnel from sign-up to first purchase and identify the biggest drop-off point with user segments"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Mixpanel Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

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