We can all agree that the quality of AI-driven answers relies on the consistency of the underlying data. But AI models, while powerful, lack business context out of the box. As more organizations ask questions of their data using natural language, it is increasingly important to unify business measures and dimensions, ensure consistency company-wide. If you want trustworthy AI, what you need is a semantic layer that acts as the single source of truth for business metrics.
But how do you make that data accessible and actionable for your end users? Building off the recent introduction of Looker’s Model Context Protocol (MCP) server, in this blog we take you through the process of creating an Agent Development Kit (ADK) agent that is connected to Looker via the MCP Toolbox for Databases and exposing it within Gemini Enterprise. Let’s get started.
Step 1 – Set up Looker Integration in MCP Toolbox
MCP Toolbox for Databases is a central open-source server that hosts and manages toolsets, enabling agentic applications to leverage Looker’s capabilities without working directly with the platform. Instead of managing tool logic and authentication themselves, agents act as MCP clients and request tools from the Toolbox. The MCP Toolbox handles all the underlying complexities, including secure connections to Looker, authentication and query execution.
The MCP Toolbox for Databases natively supports Looker’s pre-built toolset. To access these tools, follow the below steps:
Connect to Cloud Shell. Check that you’re already authenticated, and that the project is set to your project ID using the following command:
Source Credit: https://cloud.google.com/blog/products/business-intelligence/connecting-looker-to-gemini-enterprise-with-mcp-toolbox-and-adk/
