Google Analytics MCP Server

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

Connect Google Analytics 4 data to Claude, Cursor and other MCP clients. Query your website traffic, user behavior, and analytics data in natural language with access to 200+ GA4 dimensions and metrics.

Compatible with: Claude, Cursor and other MCP clients.

I also built a Google Search Console MCP that enables you to mix & match the data from both the sources

Prerequisites

Check your Python setup:

Required:

• Python 3.10 or higher

• Google Analytics 4 property with data

• Service account with Analytics Reporting API access

Step 1: Setup Google Analytics Credentials

Create Service Account in Google Cloud Console

1. Go to Google Cloud Console

2. Create or select a project:

   • New project: Click "New Project" → Enter project name → Create

   • Existing project: Select from dropdown

3. Enable the Analytics APIs:

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

   • Search for "Google Analytics Data API" → Click "Enable"

4. Create Service Account:

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

   • Click "Create Credentials" → "Service Account"

   • Enter name (e.g., "ga4-mcp-server")

   • Click "Create and Continue"

   • Skip role assignment → Click "Done"

5. Download JSON Key:

   • Click your service account

   • Go to "Keys" tab → "Add Key" → "Create New Key"

   • Select "JSON" → Click "Create"

   • Save the JSON file - you'll need its path

Add Service Account to GA4

1. Get service account email:

   • Open the JSON file

   • Find the client_email field

   • Copy the email (format: ga4-mcp-server@your-project.iam.gserviceaccount.com)

2. Add to GA4 property:

   • Go to Google Analytics

   • Select your GA4 property

   • Click "Admin" (gear icon at bottom left)

   • Under "Property" → Click "Property access management"

   • Click "+" → "Add users"

   • Paste the service account email

   • Select "Viewer" role

   • Uncheck "Notify new users by email"

   • Click "Add"

Find Your GA4 Property ID

1. In Google Analytics, select your property

2. Click "Admin" (gear icon)

3. Under "Property" → Click "Property details"

4. Copy the Property ID (numeric, e.g., 123456789)

   • Note: This is different from the "Measurement ID" (starts with G-)

Test Your Setup (Optional)

Verify your credentials:

Create a test script (test_ga4.py):

Run the test:

If you see "✅ GA4 credentials working!" you're ready to proceed.

Step 2: Install the MCP Server

Choose one method:

Method A: pip install (Recommended)

MCP Configuration:

First, check your Python command:

Then use the appropriate configuration:

If python3 --version worked:

If python --version worked:

Method B: GitHub download

MCP Configuration:

Step 3: Update Configuration

Replace these placeholders in your MCP configuration:

• /path/to/your/service-account-key.json with your JSON file path

• 123456789 with your GA4 Property ID

• /full/path/to/ga4-mcp-server/ with your download path (Method B only)

Usage

Once configured, ask your MCP client questions like:

Discovery & Exploration

• What GA4 dimension categories are available?

• Show me all ecommerce metrics

• What dimensions can I use for geographic analysis?

Traffic Analysis

• What's my website traffic for the past week?

• Show me user metrics by city for last month

• Compare bounce rates between different date ranges

Multi-Dimensional Analysis

• Show me revenue by country and device category for last 30 days

• Analyze sessions and conversions by campaign and source/medium

• Compare user engagement across different page paths and traffic sources

E-commerce Analysis

• What are my top-performing products by revenue?

• Show me conversion rates by traffic source and device type

• Analyze purchase behavior by user demographics

Quick Start Examples

Try these example queries to see the MCP's analytical capabilities:

1. Geographic Distribution

This demonstrates:

• Geographic analysis

• User segmentation

• Time-based filtering

• Data visualization

2. User Behavior Analysis

This demonstrates:

• Multi-dimensional analysis

• Time series comparison

• User engagement metrics

• Technology segmentation

3. Traffic Source Performance

This demonstrates:

• Marketing performance analysis

• Period-over-period comparison

• Conversion tracking

• Revenue attribution

4. Content Performance

This demonstrates:

• Content analysis

• Trend analysis

• Engagement metrics

• Ranking and sorting

🚀 Performance Optimizations

This MCP server includes built-in optimizations to prevent context window crashes and ensure smooth operation:

Smart Data Volume Management

• Automatic row estimation - Checks data volume before fetching

• Interactive warnings - Alerts when queries would return >2,500 rows

• Optimization suggestions - Provides specific recommendations to reduce data volume

Server-Side Processing

• Intelligent aggregation - Automatically aggregates data when beneficial (e.g., totals across time periods)

• Smart sorting - Returns most relevant data first (recent dates, highest values)

• Efficient filtering - Leverages GA4's server-side filtering capabilities

User Control Parameters

• limit - Set maximum number of rows to return

• proceed_with_large_dataset=True - Override warnings for large datasets

• enable_aggregation=False - Disable automatic aggregation

• estimate_only=True - Get row count estimates without fetching data

Example: Handling Large Datasets

Available Tools

The server provides a suite of tools for data reporting and schema discovery.

1. search_schema - Searches for a keyword across all available dimensions and metrics. This is the most efficient way to discover fields for a query.

2. get_ga4_data - Retrieve GA4 data with built-in intelligence for better and safer results (includes data volume protection, smart aggregation, and intelligent sorting).

3. list_dimension_categories - Lists all available dimension categories.

4. list_metric_categories - Lists all available metric categories.

5. get_dimensions_by_category - Gets all dimensions for a specific category.

6. get_metrics_by_category - Gets all metrics for a specific category.

7. get_property_schema - Returns the complete schema for the property (Warning: this can be a very large object).

Dimensions & Metrics

Access to 200+ GA4 dimensions and metrics organized by category:

Dimension Categories

• Time: date, hour, month, year, etc.

• Geography: country, city, region

• Technology: browser, device, operating system

• Traffic Source: campaign, source, medium, channel groups

• Content: page paths, titles, content groups

• E-commerce: item details, transaction info

• User Demographics: age, gender, language

• Google Ads: campaign, ad group, keyword data

• And 10+ more categories

Metric Categories

• User Metrics: totalUsers, newUsers, activeUsers

• Session Metrics: sessions, bounceRate, engagementRate

• E-commerce: totalRevenue, transactions, conversions

• Events: eventCount, conversions, event values

• Advertising: adRevenue, returnOnAdSpend

• And more specialized metrics

Troubleshooting

If you get "No module named ga4_mcp_server" (Method A):

If you get "executable file not found":

• Try the other Python command (python vs python3)

• Use pip3 instead of pip if needed

Permission errors:

Credentials not working:

1. Verify the JSON file path is correct and accessible

2. Check service account permissions:

   • Go to Google Cloud Console → IAM & Admin → IAM

   • Find your service account → Check permissions

3. Verify GA4 access:

   • GA4 → Admin → Property access management

   • Check for your service account email

4. Verify ID type:

   • Property ID: numeric (e.g., 123456789) ✅

   • Measurement ID: starts with G- (e.g., G-XXXXXXXXXX) ❌

API quota/rate limit errors:

• GA4 has daily quotas and rate limits

• Try reducing the date range in your queries

• Wait a few minutes between large requests

Project Structure

License

MIT License