Google Analytics 4 MCP Server

mcp-name: io.github.surendranb/google-analytics-mcp

Connect Google Analytics 4 data directly to AI agents, analyst copilots, and MCP runtimes across Claude, ChatGPT, Gemini, Cursor, VS Code, and OpenClaw. Gives models analysis-ready GA4 access with live schema discovery, metric auto-aliasing, server-side aggregation, and autonomous self-healing defenses.

🌐 Website & Documentation: https://ga4mcp.com
🔗 Sister Project: Google Search Console MCP

⚡ Quickstart — 1-Line Installations

1. Universal 1-Line Installer (Recommended)

Auto-detects your system, configures Gemini CLI, Claude Desktop, Cursor, and VS Code automatically in 1 command:

curl -fsSL https://ga4.builditwithai.xyz | bash

2. Homebrew (macOS & Linux)

brew tap surendranb/tap
brew install google-analytics-mcp

3. One-Click for Cursor

4. NPX / Node.js (Claude Code, Cursor, VS Code, Windsurf)

Add to your MCP configuration file (claude_desktop_config.json or .cursor/mcp.json):

{
  "mcpServers": {
    "ga4-analytics": {
      "command": "npx",
      "args": ["-y", "google-analytics-mcp"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/absolute/path/to/service-account-key.json",
        "GA4_PROPERTY_ID": "123456789"
      }
    }
  }
}

5. Gemini CLI Extension

Install directly into Google Gemini CLI with a single command:

gemini extensions install github.com/surendranb/google-analytics-mcp

6. Python uvx & Explicit python -m ga4_mcp

{
  "mcpServers": {
    "ga4-analytics": {
      "command": "uvx",
      "args": ["--from", "google-analytics-mcp", "ga4-mcp-server"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/absolute/path/to/service-account-key.json",
        "GA4_PROPERTY_ID": "123456789"
      }
    }
  }
}

Or run directly via ga4-mcp-server / python -m ga4_mcp:

{
  "mcpServers": {
    "ga4-analytics": {
      "command": "python",
      "args": ["-m", "ga4_mcp"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/absolute/path/to/service-account-key.json",
        "GA4_PROPERTY_ID": "123456789"
      }
    }
  }
}

🧠 Why AI Agents & Marketers Prefer This Server

• Autonomous Self-Healing: System directives automatically intercept schema hallucinations (like guessing legacy metric names or incorrect filter nesting) and guide models to self-correct via get_troubleshooting_guide.
• Metric Auto-Aliasing: Automatically maps legacy or common LLM requests like 'conversions' → 'keyEvents', preventing unnecessary query failures.
• Server-Side Aggregation: Computes property totals dynamically for non-time-series queries, so LLMs spend time answering business questions rather than parsing raw rows.
• Data Volume Protection: Runs quick row-count estimates before executing large queries (>2,500 rows) to prevent crashing model context windows.
• Multi-Platform Support: Native packages an

... View full README on GitHub