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.

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

Use the `get_metadata` tool 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.

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

Yes! Use the `run_realtime_report` tool 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.

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

Use the `run_funnel_report` tool 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.

Google Analytics MCP Server for Claude Code 12 tools — connect in under 2 minutes

Name: Google Analytics
Availability: InStock
Author: Vinkius

docs.anthropic.com/en/docs/claude-code

Built by Vinkius GDPR 12 Tools CLI

Claude Code is Anthropic's agentic CLI for terminal-first development. Add Google Analytics as an MCP server in one command and Claude Code will discover every tool at runtime — ideal for automation pipelines, CI/CD integration, and headless workflows via the Vinkius.

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The modern way to manage MCP Servers — no config files, no terminal commands. Install Google Analytics and 2,500+ MCP Servers from a single visual interface.

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Classic Setup·bash

# Your Vinkius token — get it at cloud.vinkius.com
claude mcp add google-analytics --transport http "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Google Analytics MCP Server

Connect your Google Analytics 4 (GA4) account to any AI agent and take full control of web and app analytics through natural conversation.

Claude Code registers Google Analytics as an MCP server in a single terminal command. Once connected, Claude Code discovers all 12 tools at runtime and can call them headlessly — ideal for CI/CD pipelines, cron jobs, and automated workflows where Google Analytics data drives decisions without human intervention.

What you can do

Custom Reports — Run reports with any combination of metrics (activeUsers, screenPageViews, sessions, eventCount) and dimensions (city, country, deviceCategory, channel grouping)
Realtime Data — Monitor what's happening on your site right now with live user counts, events, and traffic sources from the last 30-60 minutes
Batch Reports — Execute multiple report configurations in a single API call for efficient dashboard loading
Metadata Discovery — List all available metrics and dimensions for your property, including custom definitions
Compatibility Checks — Validate metric/dimension combinations before running reports to avoid errors
Audience Exports — List and monitor audience export jobs for user segmentation and activation
User Activity — Retrieve event history for specific users for journey analysis and support investigations
Funnel Analysis — Visualize user progression through conversion steps and identify drop-off points

The Google Analytics MCP Server exposes 12 tools through the Vinkius. Connect it to Claude Code in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Google Analytics to Claude Code via MCP

Follow these steps to integrate the Google Analytics MCP Server with Claude Code.

Install Claude Code

Run npm install -g @anthropic-ai/claude-code if not already installed

Add the MCP Server

Run the command above in your terminal

Verify the connection

Run claude mcp to list connected servers, or type /mcp inside a session

Start using Google Analytics

Ask Claude: "Using Google Analytics, show me..." — 12 tools are ready

Why Use Claude Code with the Google Analytics MCP Server

Claude Code provides unique advantages when paired with Google Analytics through the Model Context Protocol.

Single-command setup: `claude mcp add` registers the server instantly — no config files to edit or applications to restart

Terminal-native workflow means MCP tools integrate seamlessly into shell scripts, CI/CD pipelines, and automated DevOps tasks

Claude Code runs headlessly, enabling unattended batch processing using Google Analytics tools in cron jobs or deployment scripts

Built by the same team that created the MCP protocol, ensuring first-class compatibility and the fastest adoption of new protocol features

Google Analytics + Claude Code Use Cases

Practical scenarios where Claude Code combined with the Google Analytics MCP Server delivers measurable value.

CI/CD integration: embed Google Analytics tool calls in your deployment pipeline to validate configurations or fetch secrets before shipping

Headless batch processing: schedule Claude Code to query Google Analytics nightly and generate reports without human intervention

Shell scripting: pipe Google Analytics outputs into other CLI tools for data transformation, filtering, and aggregation

Infrastructure monitoring: run Claude Code in a cron job to query Google Analytics status endpoints and alert on anomalies

Google Analytics MCP Tools for Claude Code (12)

These 12 tools become available when you connect Google Analytics to Claude Code via MCP:

batch_run_reports

Provide property_id and an array of report configurations. Each report can have different metrics, dimensions, and date ranges. This is efficient for dashboard loading or comparative analysis. The reports parameter should be a JSON array of report objects with metrics, dimensions, and dateRanges. Run multiple reports in a single API call

check_compatibility

Before running complex reports, use this to ensure compatibility between your chosen metrics and dimensions. This prevents errors and wasted API calls. Provide property_id and the metrics/dimensions you plan to use. Returns compatibility status and any conflicts that would prevent the report from running successfully. Check if metrics and dimensions can be combined in a report

get_audience_export

Audience exports allow you to extract user lists matching specific audience criteria. Use this to monitor the progress of audience extraction jobs. Provide property_id and the audience_export_id from list_audience_exports. Get status of a specific audience export

get_metadata

This includes both standard and custom metrics/dimensions with their descriptions, types, and compatibility information. Use this to discover what data is available before building reports. The propertyId is required and can be found in your GA4 admin settings. Get available metrics and dimensions for a GA4 property

get_property

Use the property_id obtained from list_properties to inspect property configuration. Get detailed information about a specific GA4 property

get_user_activity

This shows all interactions a user has had with your property, including pageviews, events, and conversions. Use this for user-level analysis, journey mapping, or support investigations. The userId must match the one sent with your tracking events. Get activity history for a specific user

list_accounts

This is the top-level container for properties. Each account can contain multiple properties. Use this to discover what accounts are available before drilling down into properties. List all Google Analytics accounts accessible to the user

list_audience_exports

Audience exports are used to extract user lists matching specific audience criteria for activation in other platforms. Shows status (CREATING, ACTIVE, FAILED) and configuration of each export job. List all audience export jobs for a property

list_properties

Properties represent individual websites, apps, or measurement streams. Each property has a unique ID needed for running reports. Use this to find the correct property_id for report queries. List all GA4 properties in an account

run_funnel_report

This helps identify where users drop off in conversion paths like checkout flows or signup processes. Provide property_id and a funnelSpec object defining the steps and breakdown settings. The funnelSpec should be a JSON object with steps array containing stepName, filterExpression, and optional breakdown settings. Run a funnel analysis report

run_realtime_report

Unlike standard reports, this shows what's happening on your site/app right now. Provide property_id and the metrics/dimensions you want to monitor in realtime. Common realtime metrics: activeUsers, eventCount, screenPageViews. Common realtime dimensions: city, country, deviceCategory, streamId. Get realtime analytics data (last 30-60 minutes)

run_report

You must provide the property_id, metrics (e.g., 'activeUsers', 'screenPageViews', 'eventCount'), and dimensions (e.g., 'city', 'pageTitle', 'sessionDefaultChannelGrouping'). Date ranges use YYYY-MM-DD format. Optional filter expression can narrow results. Common metrics: activeUsers, screenPageViews, sessions, eventCount, engagementRate, averageSessionDuration. Common dimensions: city, country, deviceCategory, sessionDefaultChannelGrouping, pageTitle, pagePath. Run a custom Google Analytics report

Example Prompts for Google Analytics in Claude Code

Ready-to-use prompts you can give your Claude Code agent to start working with Google Analytics immediately.

"Show me the number of active users and pageviews by country for the last 7 days for property 123456789."

"What's happening on the site right now? Show me realtime users by traffic source."

"Run a funnel analysis for our checkout flow: step 1 = viewed product, step 2 = added to cart, step 3 = started checkout, step 4 = completed purchase. Show me where users drop off."