Google Analytics MCP Server

Overview

The Google Analytics MCP Server bridges the gap between AI assistants and real‑world web analytics. By exposing GA4 data through the Model Context Protocol, it lets Claude, Cursor and any MCP‑compatible client ask natural‑language questions about website traffic, user journeys, conversion funnels, and more—without leaving the AI environment. This removes the need for manual data exports or spreadsheet wrangling, enabling instant insights that can drive decisions in real time.

At its core, the server authenticates with a Google Cloud service account that has Viewer access to a GA4 property. Once authenticated, it exposes over 200 dimensions and metrics—including page views, sessions, user acquisition channels, engagement scores, and event counts—through a unified query interface. The AI client can request summaries, trend analyses, or drill‑downs by simply phrasing a question like “How did mobile traffic change last quarter?” The server translates this into the appropriate GA4 Data API call, retrieves the data, and returns it in a structured JSON format that the assistant can embed directly into responses or visualizations.

Key capabilities include:

• Natural‑language querying: Convert conversational prompts into GA4 API requests.
• Rich data coverage: Access a comprehensive set of dimensions and metrics, enabling detailed audience segmentation and funnel analysis.
• Secure authentication: Use a service account with minimal permissions (Viewer) to keep data access tightly controlled.
• Cross‑source integration: Pair with the companion Google Search Console MCP to combine search performance data with on‑site behavior for a holistic view of digital marketing effectiveness.

Typical use cases span from marketing teams crafting data‑driven briefs to product managers monitoring feature adoption, and even developers building conversational dashboards. For example, a data analyst can ask the assistant to “Show me the top landing pages for new users in the last 30 days” and receive an instant, ready‑to‑share report. In a product context, a sprint review could include AI‑generated insights on how a recent UI change affected user engagement metrics.

By embedding GA4 data directly into AI workflows, the MCP Server eliminates latency between data collection and analysis. It empowers developers to build smarter applications—chatbots, recommendation engines, or internal knowledge bases—that respond with up‑to‑date analytics without manual intervention. The result is a seamless, secure, and highly productive integration that turns raw GA4 metrics into actionable intelligence at the click of a button.