The Google Analytics MCP Server enables LLMs to interact with Google Analytics APIs through the Model Context Protocol. Key capabilities include:
Account and property management: Retrieve account summaries, get detailed property information, and list Google Ads links
Core reporting: Execute standard Google Analytics reports with custom date ranges, dimensions, metrics, filters, and sorting options
Real-time analytics: Access live data through real-time reports with specific dimensions and metrics
Custom configurations: Retrieve and utilize custom dimensions and metrics defined for tailored reporting
The server provides comprehensive access to Google Analytics data for analysis and reporting through structured API interactions.
Allows retrieval of Google Ads account links associated with Google Analytics properties.
Provides tools for interacting with Google Analytics Admin and Data APIs, enabling retrieval of account and property information, running core and realtime reports, and managing custom dimensions and metrics.
Google Analytics MCP Server (Experimental)
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 Google Analytics accounts and properties.get_property_details
: Returns details about a property.list_google_ads_links
: Returns a list of links to Google Ads accounts for a property.
Run core reports 📙
run_report
: Runs a Google Analytics report using the Data API.get_custom_dimensions_and_metrics
: Retrieves the custom dimensions and metrics for a specific property.
Run realtime reports ⏳
run_realtime_report
: Runs a Google Analytics realtime report using the Data API.
Setup instructions 🔧
✨ Watch the Google Analytics MCP Setup Tutorial on YouTube for a step-by-step walkthrough of these instructions.
Setup involves the following steps:
- Configure Python.
- Configure credentials for Google Analytics.
- 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:
Check out Manage OAuth Clients for how to create an OAuth client.
Here are some sample gcloud
commands you might find useful:
- Set up ADC using user credentials and an OAuth desktop or web client after
downloading the client JSON to
YOUR_CLIENT_JSON_FILE
. - Set up ADC using service account impersonation.
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!
Configure Gemini
- Install Gemini CLI or Gemini Code Assist.
- Create or edit the file at
~/.gemini/settings.json
, adding your server to themcpServers
list.ReplacePATH_TO_CREDENTIALS_JSON
with the path you copied in the previous step.We also recommend that you add aGOOGLE_CLOUD_PROJECT
attribute to theenv
object. ReplaceYOUR_PROJECT_ID
in the following example with the project ID of your Google Cloud project.
Try it out 🥼
Launch Gemini Code Assist or Gemini CLI and type /mcp
. You should see
analytics-mcp
listed in the results.
Here are some sample prompts to get you started:
- Ask what the server can do:
- Ask about a Google Analytics property
- Prompt for analysis:
- Ask about signed-in users:
- Ask about property configuration:
Contributing ✨
Contributions welcome! See the Contributing Guide.
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Tools
Official Google Analytics MCP Server
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