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.

The server uses the Google Analytics Admin API and Google Analytics Data API to provide several Tools for use with LLMs.

• 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_report: Runs a Google Analytics report using the Data API.
• run_funnel_report: Runs a Google Analytics funnel report using the Data API.
• get_custom_dimensions_and_metrics: Retrieves the custom dimensions and metrics for a specific property.

• run_realtime_report: Runs a Google Analytics realtime report using the Data API.

✨ Watch the Google Analytics MCP Setup Tutorial on YouTube for a step-by-step walkthrough of these instructions.

Setup involves the following steps:

1. Configure Python.
2. Configure credentials for Google Analytics.
3. Configure Gemini.

Install pipx.

Follow the instructions to enable the following APIs in your Google Cloud project:

• Google Analytics Admin API
• Google Analytics Data API

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:

• Set up ADC using user credentials and an OAuth desktop or web client after downloading the client JSON to 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

• Set up ADC using service account impersonation.

  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]

1. Install Gemini CLI or Gemini Code Assist.

2. Create or edit the file at ~/.gemini/settings.json, adding your server to the mcpServers list.

   Replace PATH_TO_CREDENTIALS_JSON with the path you copied in the previous step.

   We also recommend that you add a GOOGLE_CLOUD_PROJECT attribute to the env object. Replace YOUR_PROJECT_ID in the following example with the project ID of your Google Cloud project.

   {
     "mcpServers": {
       "analytics-mcp": {
         "command": "pipx",
         "args": ["run", "analytics-mcp"],
         "env": {
           "GOOGLE_APPLICATION_CREDENTIALS": "PATH_TO_CREDENTIALS_JSON",
           "GOOGLE_PROJECT_ID": "YOUR_PROJECT_ID"
         }
       }
     }
   }

1. Add the MCP server with the following command:

   Replace PATH_TO_CREDENTIALS_JSON with the path you copied in the previous step, and replace YOUR_PROJECT_ID with the project ID of your Google Cloud project.

   claude mcp add analytics-mcp \
     --scope user \
     -e "GOOGLE_APPLICATION_CREDENTIALS=PATH_TO_CREDENTIALS_JSON" \
     -e "GOOGLE_PROJECT_ID=YOUR_PROJECT_ID" \
     -- pipx run analytics-mcp

Launch Gemini Code Assist or Gemini CLI and type /mcp. You should see analytics-mcp listed in the results.

This fork includes a Railway entry point that exposes the same tools over Streamable HTTP at /mcp. The upstream stdio entry point remains unchanged.

1. Base64-encode a Google service-account JSON key and add the result to the Railway service as GOOGLE_APPLICATION_CREDENTIALS_BASE64.

2. Choose one remote authentication mode:

   • For ChatGPT and other OAuth MCP clients, create an Auth0 API whose identifier is the public Railway origin, add an analytics:read scope, and set these Railway variables:

     AUTH0_DOMAIN=YOUR-TENANT.us.auth0.com
     AUTH0_AUDIENCE=https://YOUR-SERVICE.up.railway.app
     AUTH0_SCOPE=analytics:read
     

     The server publishes OAuth protected-resource metadata and verifies each Auth0 access token's signature, issuer, audience, expiry, and scope.

   • For MCP clients that support custom request headers, add a long random value as MCP_AUTH_TOKEN.

3. Deploy the repository. Railway detects the root Dockerfile and uses /health as the deployment health check.

4. Generate a public Railway domain. The Streamable HTTP MCP URL is:

   https://YOUR-SERVICE.up.railway.app/mcp
   

5. Static-token clients must send this header:

   Authorization: Bearer YOUR_MCP_AUTH_TOKEN
   

When Auth0 variables and MCP_AUTH_TOKEN are both present, Auth0 takes precedence. For a short-lived local connectivity test without authentication, explicitly set ALLOW_UNAUTHENTICATED_MCP=true. Do not leave an Analytics endpoint publicly accessible without authentication.

To test locally with an existing credentials file:

GOOGLE_APPLICATION_CREDENTIALS=/path/to/key.json \
MCP_AUTH_TOKEN=replace-me \
PORT=8080 \
python -m analytics_mcp.remote_server

Here are some sample prompts to get you started:

• Ask what the server can do:

  what can the analytics-mcp server do?
  

• Ask about a Google Analytics property

  Give me details about my Google Analytics property with 'xyz' in the name
  

• Prompt for analysis:

  what are the most popular events in my Google Analytics property in the last 180 days?
  

• Ask about signed-in users:

  were most of my users in the last 6 months logged in?
  

• Ask about property configuration:

  what are the custom dimensions and custom metrics in my property?
  

Contributions welcome! See the Contributing Guide.