Connect Google Analytics to ChatGPT, Claude and Gemini

• 01 / 03How to analyze funnels
• 02 / 03How to monitor traffic
• 03 / 03How to troubleshoot user journeys

Fastest day

762ms

Slowest day

1180ms

14-day trend

Slowing+7%

Google Analytics 4 Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_v8thcnqdwIs5ecz18LHuEc3p1uB6nf8I9CCTGT2t/mcpCopy

Claude Desktop

Follow the steps below to connect in seconds.

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — Google Analytics 4 capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "google-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_v8thcnqdwIs5ecz18LHuEc3p1uB6nf8I9CCTGT2t/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Google Analytics 4 in Claude and other AIs

See all the AI clients this connector works with ↑

• 01

  Marketing Teams

  Pull custom reports by channel or geography through chat.

• 02

  Product Managers

  Monitor conversion rates and user drop off in specific flows.

• 03

  Data Analysts

  Validate metric compatibility and export data for BI capabilities.

• 04

  Support Teams

  Investigate specific customer journeys using user activity logs.

• 01

  How do I connect my Google Analytics data?

  You subscribe to the MCP on Vinkius and provide your Google Analytics API key. Once connected, your AI client can immediately start querying your properties.

• 02

  Which AI clients can I use with this MCP?

  This MCP works with any compatible client, including Claude, Cursor, Windsurf, and VS Code.

• 03

  Can I use custom dimensions in my reports?

  Yes. You can use the get_metadata capability to find your custom definitions and then include them in your run_report requests.

• 04

  How recent is the realtime data?

  The realtime reports provide data from the last 30 to 60 minutes of activity on your site or app.

• 05

  What happens if I request incompatible metrics?

  You can use the check_compatibility capability to verify your metrics and dimensions before running a report to avoid errors.

• 06

  How do I get a Google Analytics API key and what type of credentials do I need?

  You need an API Key from Google Cloud Console. Go to console.cloud.google.com → Select or create a project → Enable the Google Analytics Data API v1 → Navigate to APIs & Services > Credentials → Click Create Credentials > API Key. Copy the key (starts with AIzaSy...). Then, in Google Analytics Admin, add the service account email with Viewer or Analyst role to your GA4 property. Paste the API key below.

• 07

  What metrics and dimensions are available in GA4 and how do I find them?

  Use the get_metadata capability with your property_id to list all available metrics and dimensions. Common metrics include: activeUsers, screenPageViews, sessions, eventCount, engagementRate, averageSessionDuration, conversions. Common dimensions include: city, country, deviceCategory, sessionDefaultChannelGrouping, pageTitle, pagePath, streamName. The metadata response shows descriptions, types, and whether each field is a metric or dimension.

• 08

  Can I get realtime data and how far back does it go?

  Yes! Use the run_realtime_report capability to get data from the last 30-60 minutes. Realtime reports show active users, events, and traffic sources as they happen on your site. This is useful for monitoring campaign launches, tracking live events, or checking if tracking is working correctly. Unlike standard reports which can take 24-48 hours to process, realtime data is available within minutes.

• 09

  How do I analyze funnel conversion rates and identify drop-off points?

  Use the run_funnel_report capability with a funnel_spec JSON object defining your conversion steps. Each step includes a stepName, filterExpression (e.g., eventName equals 'page_view'), and optional breakdown settings. The response shows how many users entered each step, how many completed it, and where the biggest drop-offs occurred. This helps identify friction points in checkout flows, signup processes, or any multi-step user journey.

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