Google Analytics launches MCP server for AI-powered data conversations

Google Analytics announced the release of an experimental open-source Model Context Protocol (MCP) server on July 22, 2025, marking a significant advancement in how marketing professionals access analytics data. The development enables Large Language Models like Gemini to connect directly with Google Analytics through natural language conversations.

Matt Landers, head of Developer Relations for Google Analytics, demonstrated the server's capabilities during a video presentation published on July 22, 2025. The tool operates as "a Model Context Protocol server, which allows us to connect an LLM, like Gemini, to a system, like analytics, and essentially allows us to chat with our data," according to Landers in the demonstration.

The server provides eight core tools through the Google Analytics Admin API and Data API. Users can retrieve account summaries, property details, Google Ads links, and run comprehensive reports through natural language commands. Landers demonstrated asking simple questions like "How many users did I have yesterday?" which returned specific data: "You had 3,082 users yesterday."

The demonstration showcased real-time query processing where Gemini automatically determined the appropriate analytics property and constructed the necessary API calls. For e-commerce queries, Landers asked "What were my top selling products over the last month?" The system returned detailed product revenue data, identifying the "Super G Brick Puzzle Set" as a top performer. When prompted to show results by units sold instead of revenue, the AI modified the query parameters automatically.

Advanced functionality includes comprehensive marketing analysis capabilities. During the demonstration, Landers provided a $5,000 monthly marketing budget scenario, asking the AI to "come up with a plan that will drive more revenue" and "run any reports that you need to to justify this plan." The system generated a complete marketing strategy with budget allocations across Google Ads search and shopping campaigns, paid social media, and email marketing initiatives.

The AI justified its recommendations by analyzing existing traffic patterns, noting that "direct and organic search are our powerhouse, driving over $419,000 in revenue." The system provided detailed campaign structures and budget distributions based on actual analytics data rather than generic recommendations.

Technical implementation requires Python configuration with Google Analytics API access. Users must enable the Google Analytics Admin API and Google Analytics Data API within their Google Cloud project. Authentication occurs through Application Default Credentials with the Google Analytics read-only scope. The system integrates with Gemini CLI or Gemini Code Assist through configuration files stored in ~/.gemini/settings.json.

Configuration involves adding the analytics-mcp server to the mcpServers list with specific command parameters. Users can optionally configure the GOOGLE_APPLICATION_CREDENTIALS environment variable for consistent credential usage across sessions. After configuration, users type "/mcp" in Gemini to access the analytics server.

GitHub activity shows sustained development with 11 active pull requests and 124 stars since the repository went public. The experimental designation indicates ongoing development with potential feature changes. Recent commits include order_bys argument support, documentation improvements, and pipx compatibility enhancements for broader installation options.

The server's tools encompass both standard and custom dimension retrieval, realtime reporting capabilities, and comprehensive metric access. Standard dimensions and metrics provide structured data access while custom dimensions enable organization-specific tracking requirements. Realtime reporting tools deliver current visitor activity and engagement metrics for immediate decision-making.

The release follows broader industry adoption of Model Context Protocol technology. Microsoft launched its Clarity MCP server on June 4, 2025, enabling similar natural language analytics queries through AI assistants. AppsFlyer introduced its MCP tool on July 17, 2025, focusing on mobile marketing measurement and attribution.

The Google Analytics implementation differentiates itself through comprehensive web analytics coverage and integration with Google's broader advertising ecosystem. Unlike mobile-focused attribution platforms, the server handles complete website visitor journeys from acquisition through conversion. This positioning aligns with Google's exploration of MCP integration for its Ads API, potentially creating unified AI-powered campaign management workflows.

Sample prompts suggested by Google include fundamental queries about server capabilities, property-specific analysis, and sophisticated strategic planning. "What can the analytics-mcp server do?" provides initial orientation. "What are the most popular events in my Google Analytics property in the last 180 days?" enables event-driven analysis. "Were most of my users in the last 6 months logged in?" addresses user authentication patterns critical for personalization strategies.

Property configuration queries help users understand their tracking setup through questions like "What are the custom dimensions and custom metrics in my property?" This information proves essential for organizations with complex measurement requirements or custom tracking implementations.

The experimental status suggests Google is evaluating user feedback and usage patterns before potential enterprise deployment. Developer community engagement occurs through GitHub issues and Discord server participation. The open-source approach enables community contributions and custom modifications for specific organizational requirements.

Implementation considerations include API rate limiting, authentication management, and data access permissions. The server operates within standard Google Analytics API constraints, requiring appropriate user permissions for accessed properties. Organizations with multiple properties or complex account structures may need additional configuration for optimal functionality.

Error handling and response formatting occur automatically through the MCP framework. The system provides structured data responses suitable for AI processing while maintaining human-readable output formatting. Query refinement happens conversationally, allowing users to modify parameters and explore different analytical perspectives without technical intervention.

The development represents a significant shift toward conversational analytics interfaces. Traditional dashboard navigation and manual report construction become supplemented by natural language interaction. Marketing professionals can access complex data analysis without technical expertise or extensive platform training.

Security protocols follow Google Analytics standard access patterns through OAuth authentication and role-based permissions. The MCP server maintains existing security boundaries while enabling enhanced accessibility through AI interfaces. Data processing occurs through established Google Analytics APIs without introducing additional exposure risks.

Future development may include enhanced integration with Google's advertising platforms, expanded metric coverage, and improved AI understanding of marketing terminology. The experimental designation allows for feature iteration based on community feedback and usage analytics.

Performance implications vary depending on query complexity and data volume. Simple user counts return quickly while comprehensive marketing analysis requires multiple API calls and processing time. The system's efficiency depends on underlying Google Analytics API performance and current platform load.

Timeline

• July 22, 2025: Google Analytics releases experimental MCP server enabling AI-powered natural language data queries
• July 17, 2025: AppsFlyer launches AI-powered MCP tool for mobile marketing measurement
• June 30, 2025: Google extends conversion environment field deadline for API improvements
• June 8, 2025: Microsoft launches Clarity MCP server for web analytics AI integration
• June 4, 2025: Microsoft introduces Clarity Model Context Protocol serverenabling natural language analytics
• April 16, 2025: Google releases Ads API v19.1 with enhanced capabilities
• March 2024: IAB Tech Lab launches Trusted Server initiative at Signal Shift event

Key Terms Explained

Model Context Protocol (MCP): A standardization framework that enables AI systems to connect with external services and data sources. MCP operates as a bridge between Large Language Models and various tools, databases, or APIs, allowing seamless integration without custom development work. The protocol ensures compatibility across different AI platforms while maintaining consistent data access patterns and security protocols.

Google Analytics API: The programming interface that enables developers to access Google Analytics data programmatically. The API encompasses both the Admin API for account and property management and the Data API for retrieving reporting data. This technical foundation allows third-party applications to integrate analytics functionality, automate reporting tasks, and build custom dashboards or tools.

Natural Language Queries: The capability for users to interact with data systems using everyday conversational language instead of technical commands or structured query formats. This approach eliminates the need for users to learn specific syntax or database query languages, making analytics accessible to marketing professionals without technical expertise while maintaining the sophistication of underlying data operations.

Gemini CLI: Google's command-line interface tool that supports MCP server connections for AI-powered interactions. The CLI serves as the technical foundation for connecting AI models to various data sources and services. Users can configure multiple MCP servers through JSON configuration files, enabling comprehensive AI-assisted workflows across different platforms and data repositories.

AI-Powered Analytics: The integration of artificial intelligence capabilities into data analysis platforms to enhance accessibility, automate insights generation, and enable conversational data exploration. This technology transforms traditional dashboard-based analytics into interactive experiences where users can ask questions, receive explanations, and generate strategic recommendations through natural language interactions with their data.

Application Default Credentials (ADC): Google Cloud's authentication mechanism that automatically manages credentials for applications accessing Google services. ADC simplifies the authentication process by using environment-based credential discovery, eliminating the need for manual credential management in most development scenarios. This system supports both user credentials and service account impersonation for secure API access.

GitHub Repository: The open-source code hosting platform where Google Analytics maintains the MCP server project. The repository serves as the central distribution point for the experimental software, enabling community contributions, issue tracking, and collaborative development. Developers can access the latest code, documentation, and configuration examples through this public repository.

Marketing Strategy Generation: The AI system's capability to analyze existing analytics data and generate comprehensive marketing recommendations with budget allocations and campaign structures. This functionality represents a significant advancement beyond traditional reporting, enabling AI to synthesize performance data into actionable strategic plans with specific tactical recommendations and financial considerations.

Real-time Reporting: The ability to access current website visitor activity and engagement metrics through immediate data processing. Real-time reporting capabilities enable marketing professionals to monitor campaign performance, track visitor behavior, and make immediate optimization decisions based on current activity patterns rather than historical data alone.

Configuration Setup: The technical process required to connect the MCP server with Google Analytics and AI systems. Configuration involves enabling APIs in Google Cloud projects, setting up authentication credentials, and modifying JSON configuration files to establish proper connections. This setup process determines which analytics properties the AI can access and how authentication occurs during data queries.

Summary

Who: Google Analytics development team, led by Matt Landers, head of Developer Relations, announced the release targeting marketing professionals and developers.

What: Experimental open-source Model Context Protocol server enabling natural language queries to Google Analytics data through AI assistants like Gemini, featuring eight core tools for account management, reporting, and strategic analysis.

When: Announced and demonstrated on July 22, 2025, with immediate availability through GitHub repository and configuration setup.

Where: Open-source release available globally through GitHub repository github.com/googleanalytics/google-analytics-mcp, requiring Google Cloud project API enablement and authentication setup.

Why: Addresses growing need for accessible analytics data interaction, eliminating technical barriers for marketing professionals while enabling AI-powered strategic planning and campaign optimization through conversational interfaces.