On July 23, 2025, Google announced the release of an open-source Model Context Protocol (MCP) server designed to facilitate the analysis of Google Analytics data using large language models like Gemini.

This new tool connects Google's Gemini AI to Google Analytics, enabling users to interact with their data through natural language queries and generate detailed reports.

The MCP server supports both standard and custom dimensions for real-time data retrieval, making it easier for marketing teams and analysts to access visitor engagement metrics instantly.

It also allows for advanced analysis, such as identifying top-selling products or assessing the impact of marketing budgets on revenue, through AI-generated insights.

Users can ask simple questions like 'How many users did I have yesterday?' and receive instant, contextual answers, replacing traditional manual reporting.

The open-source nature of the tool ensures customization, compliance with privacy standards like GDPR, and broad compatibility with MCP clients, especially Gemini CLI.

The MCP server offers eight core tools accessible via Google Analytics APIs, enabling tasks such as account summaries, comprehensive reporting, and data access based on user queries.

However, challenges remain, including setup complexity and reliance on AI model quality, which can affect the accuracy of insights.

During a demo, Google’s Matt Landers showcased how users can query data in plain English, such as asking for top traffic sources or user counts, highlighting the tool's user-friendly interface.

Google is actively developing the MCP project, seeking feedback through GitHub and Discord, with ongoing enhancements aimed at future enterprise deployment.

This initiative aligns with industry trends, as competitors like Microsoft and AppsFlyer have launched similar natural language analytics tools, emphasizing AI's growing role in marketing.

Experts expect widespread adoption of this technology by 2026, with potential expansion into other Google Cloud services, despite concerns about over-reliance on AI.