About
Google Analytics MCP Server is an official MCP integration that connects AI assistants to Google Analytics, enabling retrieval of account data, property details, and comprehensive reports directly through the Google Analytics Data API and Admin API. Key features of Google Analytics MCP Server: - Retrieve account summaries and property details from Google Analytics - Access Google Ads links associated with Analytics properties - Run standard reports using Google Analytics 4 Data API with custom dimensions and metrics - Execute real-time reports showing current website activity and user behavior - Query custom dimensions and metrics defined for specific properties
README
Google Analytics MCP Server (Experimental)
[](https://pypi.org/project/analytics-mcp/) [](https://www.python.org/downloads/) [](https://github.com/googleanalytics/google-analytics-mcp/actions?query=branch%3Amain++) [](https://pypi.org/project/analytics-mcp/) [](https://github.com/googleanalytics/google-analytics-mcp/stargazers) [](https://github.com/googleanalytics/google-analytics-mcp/network/members) [](https://www.youtube.com/watch?v=PT4wGPxWiRQ)
This repo contains the source code for running a local MCP server that interacts with APIs for Google Analytics.
Join the discussion and ask questions in the 🤖-analytics-mcp channel on Discord.
Tools 🛠️
The server uses the Google Analytics Admin API and Google Analytics Data API to provide several Tools for use with LLMs.
Retrieve account and property information 🟠
get_account_summaries: Retrieves information about the user's Googleget_property_details: Returns details about a property.list_google_ads_links: Returns a list of links to Google Ads accounts forRun core reports 📙
run_report: Runs a Google Analytics report using the Data API.get_custom_dimensions_and_metrics: Retrieves the custom dimensions andRun realtime reports ⏳
run_realtime_report: Runs a Google Analytics realtime report using theSetup instructions 🔧
✨ Watch the Google Analytics MCP Setup Tutorial on YouTube for a step-by-step walkthrough of these instructions.
[](https://www.youtube.com/watch?v=nS8HLdwmVlY)
Setup involves the following steps:
1. Configure Python. 1. Configure credentials for Google Analytics. 1. Configure Gemini.
Configure Python 🐍
Enable APIs in your project ✅
Follow the instructions to enable the following APIs in your Google Cloud project:
Configure credentials 🔑
Configure your Application Default Credentials (ADC). Make sure the credentials are for a user with access to your Google Analytics accounts or properties.
Credentials must include the Google Analytics read-only scope:
https://www.googleapis.com/auth/analytics.readonly
Check out Manage OAuth Clients for how to create an OAuth client.
Here are some sample gcloud commands you might find useful:
YOUR_CLIENT_JSON_FILE.
gcloud auth application-default login \
--scopes https://www.googleapis.com/auth/analytics.readonly,https://www.googleapis.com/auth/cloud-platform \
--client-id-file=YOUR_CLIENT_JSON_FILE
gcloud auth application-default login \
--impersonate-service-account=SERVICE_ACCOUNT_EMAIL \
--scopes=https://www.googleapis.com/auth/analytics.readonly,https://www.googleapis.com/auth/cloud-platform
When the gcloud auth application-default command completes, copy the
PATH_TO_CREDENTIALS_JSON file location printed to the console in the
following message. You'll need this for the next step!
Credentials saved to file: [PATH_TO_CREDENTIALS_JSON]
Configure Gemini
1. Install Gemini CLI or Gemini Code Assist.
1. Create or edit the file at ~/.gemini/settings.json, adding your
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