Google Analytics MCP Server

The Google Analytics MCP Server bridges the gap between conversational AI assistants and real‑time web analytics data. By exposing Google Analytics metrics, dimensions, and reports as MCP resources and tools, it allows assistants to answer data‑driven questions without leaving the chat interface. This eliminates the need for developers to write custom API wrappers or manually query the Analytics Data API, streamlining the workflow from data retrieval to insight generation.

At its core, the server offers a single, highly flexible tool—. Developers can specify exactly which metrics and dimensions they need, define date ranges, apply granular filters on both metrics and dimensions, and control ordering and pagination. The tool returns structured JSON that can be immediately parsed by an assistant or passed to downstream services, enabling dynamic dashboards, anomaly detection, or automated reporting within the same conversational context.

Key capabilities include:

• Comprehensive metric and dimension catalogues exposed as and , so assistants can discover available fields on the fly.
• Report‑level access via , allowing retrieval of pre‑defined or custom reports.
• Strong typing in the TypeScript implementation ensures that client applications receive clear contract definitions, reducing runtime errors.
• Flexible filtering with logical operations (e.g., GREATER_THAN, EXACT) lets users craft complex queries without writing SQL or raw API calls.

Real‑world scenarios that benefit from this server are abundant. Marketing teams can ask an assistant, “Show me the top countries by new users for last month,” and receive a concise table instantly. Product managers might request “List devices with the highest bounce rate in Q2,” triggering a targeted analysis that feeds directly into roadmap discussions. Analysts can embed the server within automated alerting systems, where a conversational interface surfaces the latest traffic spikes or drops as soon as they occur.

Integration into existing AI workflows is straightforward. Once the MCP server is registered in an assistant’s configuration, any prompt that references will be routed to the server. The assistant can then format the response, add natural language explanations, or trigger follow‑up actions—all within a single conversation thread. This tight coupling between data retrieval and natural language generation empowers developers to deliver richer, context‑aware experiences without the overhead of managing separate analytics pipelines.