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.
• 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"
         }
       }
     }
   }

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:

  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?
  

このフォークは Vercel 上で HTTP サーバーとして動作します。ローカルへの Python 環境構築なしに、Claude Desktop や Cursor などの MCP クライアントから直接接続できます。

Vercel プロジェクトの Environment Variables に以下を設定します。

GOOGLE_CREDENTIALS の値は以下で取得できます。

cat ga4-service-account.json | tr -d '\n'

初回のみシークレット鍵とソルトを生成し、.env.local と Vercel 環境変数に保存します。

# 鍵の生成（初回のみ）
openssl rand -base64 32  # → JWT_SECRET
openssl rand -base64 16  # → JWT_SALT

2週間ごとにトークンを再生成します（.env.local も自動更新されます）。

JWT_SECRET="..." JWT_SALT="..." python scripts/generate_token.py

生成されたトークンを Vercel の MCP_AUTH_TOKEN に設定してから再デプロイしてください。

~/.claude/claude_desktop_config.json または Cursor の MCP 設定に追記します。

{
  "mcpServers": {
    "google-analytics": {
      "url": "https://<your-vercel-domain>/mcp",
      "headers": {
        "Authorization": "Bearer <MCP_AUTH_TOKEN の値>"
      }
    }
  }
}

{
  "mcpServers": {
    "google-analytics": {
      "command": "mcp-remote",
      "args": [
        "https://<your-vercel-domain>/mcp",
        "--header",
        "Authorization: Bearer <MCP_AUTH_TOKEN の値>"
      ]
    }
  }
}

ヘルスチェックエンドポイントで動作を確認できます。

curl https://<your-vercel-domain>/health

正常時のレスポンス例:

{
  "status": "ok",
  "mcp_auth_configured": true
}

Contributions welcome! See the Contributing Guide.