Google Analytics MCP Server 📊

A FastMCP-powered Model Context Protocol server for Google Analytics 4 API integration with automatic OAuth 2.0 authentication

Connect Google Analytics 4 data directly to Claude Desktop and other MCP clients with seamless OAuth 2.0 authentication, automatic token refresh, comprehensive reporting, and analytics capabilities.

🌟 Open Source & Community

GoMarble AI Open Source Projects

Check out our other open source contributions at GoMarble AI GitHub:

• Analytics Tools - Advanced analytics and reporting solutions

• AI Integration - Tools for integrating AI with marketing platforms

• MCP Servers - Additional Model Context Protocol implementations

• Marketing Automation - Open source marketing automation tools

Join Our Community

Connect with other developers and marketers using AI in advertising:

Join our Slack Community - AI in Ads

• 💬 Discuss AI applications in advertising

• 🤝 Share your projects and get feedback

• 📚 Learn from industry experts

• 🚀 Collaborate on open source projects

• 🔧 Get help with technical implementation

🚀 Try Our One-Click Integration

Skip the manual setup and get started instantly:

One-Click MCP Integration - Connect Google Analytics and other tools to Claude Desktop in seconds

• ⚡ Instant Setup - No manual configuration required

• 🔐 Secure Authentication - Built-in OAuth handling

• 📊 Multiple Integrations - Google Analytics, Google Ads, Meta Ads, and more

• 📖 Documentation - Complete integration guide at gomarble.ai/docs

Related MCP server: Choose MCP Server

✨ Features

• 🔐 Automatic OAuth 2.0 - One-time browser authentication with auto-refresh

• 🔄 Smart Token Management - Handles expired tokens automatically

• 📊 Comprehensive Reporting - Access all GA4 metrics and dimensions

• 🏢 Property Management - List and manage Google Analytics properties

• 📈 Advanced Analytics - Page views, users, events, traffic sources, and more

• 🚀 FastMCP Framework - Built on the modern MCP standard

• 🖥️ Claude Desktop Ready - Direct integration with Claude Desktop

• 🛡️ Secure Local Storage - Tokens stored locally, never exposed

📋 Available Tools

Note: All tools automatically handle authentication - no token parameters required!

🚀 Quick Start

Prerequisites

Before setting up the MCP server, you'll need:

• Python 3.10+ installed

• A Google Cloud Platform account

• A Google Analytics 4 property with data access

🔧 Step 1: Google Cloud Platform Setup

1.1 Create Google Cloud Project

1. Go to Google Cloud Console

2. Create a new project:

   • Click "Select a project" → "New Project"

   • Enter project name (e.g., "Google Analytics MCP")

   • Click "Create"

1.2 Enable Google Analytics APIs

1. In your Google Cloud Console:

   • Go to "APIs & Services" → "Library"

   • Search for "Google Analytics Data API" and enable it

1.3 Create OAuth 2.0 Credentials

1. Go to "APIs & Services" → "Credentials"

2. Click "+ CREATE CREDENTIALS" → "OAuth 2.0 Client ID"

3. Configure consent screen (if first time):

   • Click "Configure Consent Screen"

   • Choose "External" (unless you have Google Workspace)

   • Fill required fields:

     • App name: "Google Analytics MCP"

     • User support email: Your email

     • Developer contact: Your email

   • Add scopes:

     • https://www.googleapis.com/auth/analytics

     • https://www.googleapis.com/auth/analytics.readonly

   • Click "Save and Continue" through all steps

4. Create OAuth Client:

   • Application type: "Desktop application"

   • Name: "Google Analytics MCP Client"

   • Click "Create"

5. Download credentials:

   • Click "Download JSON" button

   • Save file as client_secret_[long-string].json in your project directory

🔧 Step 2: Google Analytics Access

2.1 Ensure Analytics Access

1. Sign in to Google Analytics

2. Verify you have access to GA4 properties

3. Note your property IDs (found in GA4 Admin → Property Settings)

4. Ensure your Google account has at least Viewer access to the properties you want to query

🔧 Step 3: Installation & Setup

3.1 Clone and Install

# Clone the repository
git clone https://github.com/yourusername/google-analytics-mcp-server.git
cd google-analytics-mcp-server

# Create virtual environment (recommended)
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3.2 Environment Configuration

Create a .env file in your project directory:

# Copy the example file
cp .env.example .env

Edit .env with your credentials:

# Required: Path to OAuth credentials JSON file (downloaded from Google Cloud)
GOOGLE_ANALYTICS_OAUTH_CONFIG_PATH=/full/path/to/your/client_secret_file.json

Example .env file:

GOOGLE_ANALYTICS_OAUTH_CONFIG_PATH=/Users/john/google-analytics-mcp/client_secret_138737274875-abc123.apps.googleusercontent.com.json

🖥️ Step 4: Claude Desktop Integration

4.1 Locate Claude Configuration

Find your Claude Desktop configuration file:

macOS:

~/Library/Application Support/Claude/claude_desktop_config.json

Windows:

%APPDATA%\Claude\claude_desktop_config.json

4.2 Add MCP Server Configuration

Edit the configuration file and add your Google Analytics MCP server:

{
  "mcpServers": {
    "google-analytics": {
      "command": "/full/path/to/your/project/.venv/bin/python",
      "args": [
        "/full/path/to/your/project/server.py"
      ]
    }
  }
}

Real Example:

{
  "mcpServers": {
    "google-analytics": {
      "command": "/Users/marble-dev-01/workspace/google_analytics_mcp/.venv/bin/python",
      "args": [
        "/Users/marble-dev-01/workspace/google_analytics_mcp/server.py"
      ]
    }
  }
}

Important:

• Use absolute paths for all file locations

• On Windows, use forward slashes / or double backslashes \\ in paths

4.3 Restart Claude Desktop

Close and restart Claude Desktop to load the new configuration.

🔐 Step 5: First-Time Authentication

5.1 Trigger OAuth Flow

1. Open Claude Desktop

2. Try any Google Analytics command, for example:

   "List all my Google Analytics properties"

5.2 Complete Authentication

1. Browser opens automatically to Google OAuth page

2. Sign in with your Google account (the one with Analytics access)

3. Grant permissions by clicking "Allow"

4. Browser shows success page

5. Return to Claude - your command will complete automatically!

5.3 Verify Setup

After authentication, you should see:

• A google_analytics_token.json file created in your project directory

• Your Google Analytics properties listed in Claude's response

📖 Usage Examples

Property Management

"List all my Google Analytics properties"

"Show me properties for account 123456789"

"What GA4 properties do I have access to?"

Page View Analysis

"Get page views for property 421301275 from 2025-01-01 to 2025-01-31"

"Show me top pages by page views for last month for property 421301275"

"Analyze page performance by country for property 421301275"

User Analytics

"Get active users for property 421301275 in the last 7 days"

"Show me user metrics by device category for property 421301275"

"Compare new vs returning users for last month"

Traffic Source Analysis

"Analyze traffic sources for property 421301275 from 2025-01-01 to 2025-01-31"

"Show me which channels drive the most users to my site"

"Compare organic vs paid traffic performance"

Event Tracking

"Get events data for property 421301275 in the last 30 days"

"Show me conversion events by source/medium"

"Which events are most popular on my site?"

Custom Reports

"Create a report for property 421301275 with sessions, users, and page views by country from 2025-01-01 to 2025-01-31"

"Run a custom report showing bounce rate and engagement rate by device category"

"Generate a comprehensive traffic report with sessions, conversions, and revenue by source/medium"

🔍 Advanced GA4 Examples

Sessions and Users by Country

run_report(
    property_id="421301275",
    start_date="2025-01-01",
    end_date="2025-01-31",
    metrics=["sessions", "totalUsers", "screenPageViews"],
    dimensions=["country"],
    limit=20
)

Device Performance Analysis

run_report(
    property_id="421301275",
    start_date="2025-01-01",
    end_date="2025-01-31",
    metrics=["sessions", "bounceRate", "engagementRate"],
    dimensions=["deviceCategory", "operatingSystem"],
    limit=50
)

Traffic Sources with Conversions

run_report(
    property_id="421301275",
    start_date="2025-01-01",
    end_date="2025-01-31",
    metrics=["sessions", "conversions", "totalRevenue"],
    dimensions=["source", "medium", "campaignName"],
    limit=100
)

Daily Trend Analysis

run_report(
    property_id="421301275",
    start_date="2025-01-01",
    end_date="2025-01-31",
    metrics=["sessions", "activeUsers", "screenPageViews"],
    dimensions=["date"],
    limit=31
)

📁 Project Structure

google-analytics-mcp-server/
├── server.py                              # Main MCP server
├── oauth/
│   ├── __init__.py                        # Package initialization
│   └── google_auth.py                     # OAuth authentication logic
├── google_analytics_token.json            # Auto-generated token storage (gitignored)
├── client_secret_[long-string].json       # Your OAuth credentials (gitignored)
├── .env                                   # Environment variables (gitignored)
├── .env.example                           # Environment template
├── .gitignore                             # Git ignore file
├── requirements.txt                       # Python dependencies
├── LICENSE                                # MIT License
└── README.md                              # This file

🔒 Security & Best Practices

File Security

• ✅ Credential files are gitignored - Never committed to version control

• ✅ Local token storage - Tokens stored in google_analytics_token.json locally

• ✅ Environment variables - Sensitive data in .env file

• ✅ Automatic refresh - Minimal token exposure time

Recommended File Permissions

# Set secure permissions for sensitive files
chmod 600 .env
chmod 600 google_analytics_token.json
chmod 600 client_secret_*.json

Production Considerations

1. Use environment variables instead of .env files in production

2. Implement rate limiting to respect API quotas

3. Monitor API usage in Google Cloud Console

4. Secure token storage with proper access controls

5. Regular token rotation for enhanced security

🛠️ Troubleshooting

Authentication Issues

Configuration Issues

Claude Desktop Issues

API Issues

Debug Mode

Enable detailed logging for troubleshooting:

# Add to server.py for debugging
import logging
logging.basicConfig(level=logging.DEBUG)

🚀 Advanced Configuration

HTTP Transport Mode

For web deployment or remote access:

# Start server in HTTP mode
python3 server.py --http

Claude Desktop config for HTTP:

{
  "mcpServers": {
    "google-analytics": {
      "url": "http://127.0.0.1:8000/mcp"
    }
  }
}

Custom Token Storage

Modify token storage location in oauth/google_auth.py:

# Custom token file location
def get_token_path():
    return "/custom/secure/path/google_analytics_token.json"

🤝 Contributing

We welcome contributions! Here's how to get started:

Development Setup

# Fork and clone the repository
git clone https://github.com/yourusername/google-analytics-mcp-server.git
cd google-analytics-mcp-server

# Create development environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Set up development environment
cp .env.example .env
# Add your development credentials to .env

Making Changes

1. Create a feature branch: git checkout -b feature/amazing-feature

2. Make your changes with appropriate tests

3. Test thoroughly with different property configurations

4. Update documentation as needed

5. Commit changes: git commit -m 'Add amazing feature'

6. Push to branch: git push origin feature/amazing-feature

7. Open a Pull Request with detailed description

📊 API Limits and Quotas

Google Analytics API Quotas

• Core Reporting API: 100,000 requests per day per project

• Realtime API: 10,000 requests per day per project

• Request rate: 10 queries per second per project

Best Practices for API Usage

1. Cache results when possible to reduce API calls

2. Use appropriate date ranges to limit data volume

3. Batch requests when supported

4. Monitor usage in Google Cloud Console

5. Implement retry logic for rate limit errors

Quota Management

# Monitor usage in Google Cloud Console
# Go to APIs & Services → Quotas
# Search for "Google Analytics" to see current usage

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2025 Google Analytics MCP Server Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

📈 Roadmap

Upcoming Features

• 🔄 Enhanced real-time analytics with streaming data

• 📊 Built-in data visualization with charts and graphs

• 🤖 AI-powered insights and anomaly detection

• 📝 Custom dashboard creation tools

• 🔍 Advanced segmentation capabilities

• 🌐 Multi-property reporting

Made with ❤️ for the MCP community

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