MCP Server for Google Analytics 4

A Model Context Protocol (MCP) server that allows Large Language Models (LLMs) to interact with Google Analytics 4 (GA4) data through the Google Analytics Data API.

Features

• Run standard GA4 reports with customizable dimensions, metrics, and date ranges

• Get real-time data for the past 30 minutes

• Retrieve metadata about available metrics and dimensions

• Secure authentication using Google Cloud's Application Default Credentials

• Configurable for easy deployment via Smithery

Related MCP server: Google Analytics MCP Server

Installation

Using pip

pip install mcp-server-ga4

From source

git clone https://github.com/yourusername/mcp-server-ga4.git
cd mcp-server-ga4
pip install -e .

Authentication

This server uses Google Cloud's Application Default Credentials (ADC) for authentication. Before using the server, you'll need to:

1. Create a Google Cloud project

2. Enable the Google Analytics Data API

3. Set up authentication:

gcloud auth application-default login

For more details, see the Google Cloud Authentication documentation.

Usage

Command Line

Start the server with your GA4 property ID:

mcp-server-ga4 --property-id YOUR_GA4_PROPERTY_ID

The server will use the standard MCP stdio transport by default.

Environment Variables

You can also set configuration via environment variables:

• GA4_PROPERTY_ID: Your Google Analytics 4 property ID

Using with Claude Desktop

1. Install the server globally: pip install mcp-server-ga4

2. Set up your Google Cloud authentication: gcloud auth application-default login

3. Edit your Claude Desktop configuration:

{
    "mcpServers": {
        "ga4": {
            "command": "mcp-server-ga4",
            "args": ["--property-id", "YOUR_GA4_PROPERTY_ID"]
        }
    }
}

Available Tools

run-report

Runs a standard GA4 report with configurable metrics, dimensions, and date ranges.

Parameters:

• property_id (optional): GA4 property ID (overrides the default)

• metrics: List of metric names (e.g., ["activeUsers", "sessions"])

• dimensions (optional): List of dimension names (e.g., ["date", "country"])

• date_range: Date range in one of these formats:

  • {"start_date": "YYYY-MM-DD", "end_date": "YYYY-MM-DD"}

  • "last7days", "last30days", "today", "yesterday"

• limit (optional): Number of rows to return (default: 10)

run-realtime-report

Gets real-time data for the past 30 minutes.

Parameters:

• property_id (optional): GA4 property ID (overrides the default)

• metrics: List of metric names (e.g., ["activeUsers", "screenPageViews"])

• dimensions (optional): List of dimension names (e.g., ["country", "city"])

• limit (optional): Number of rows to return (default: 10)

get-metadata

Retrieves available metrics and dimensions for a GA4 property.

Parameters:

• property_id (optional): GA4 property ID (overrides the default)

• type (optional): Type of metadata to retrieve ("metrics", "dimensions", or "all", default: "all")

Examples

Running a standard report

What were the top 5 countries by active users in the last 30 days?

Checking real-time data

How many users are currently active on the site?

Getting metadata information

What metrics are available for me to query in GA4?

Development

1. Clone the repository

2. Install development dependencies: pip install -e ".[dev]"

3. Run tests: pytest

License

MIT