Grounding Agents with Looker and MCP
Connecting Large Language Models (LLMs) to enterprise data requires more than just a generic integration; it requires well-defined capabilities. By using the Agent Development Kit (ADK) and the Model Context Protocol (MCP), developers can create agents that interact directly with Looker’s data modeling, governance, and visualization layers. This approach allows the agent to translate natural language prompts into specific, actionable data requests.
Configuration and Implementation
The process of building an agent involves three primary technical components:
- Credential Management: Secure your Looker API client ID and client secret as environment variables. These are essential for the MCP server to authenticate and act on your behalf within the Looker instance.
- Tool Definition (
tools.yaml): This file acts as the configuration layer for your agent's capabilities. You define the Looker source (base URL and credentials) and specify which tools the agent can access. For example, defining aget_connectionstool allows the agent to fetch database metadata. The LLM uses the provided descriptions in this file to determine which tool to invoke based on user prompts. - Agent Initialization: Using the ADK, you define a root agent in Python that imports the necessary MCP toolsets and server parameters. The agent is then launched via
adk web, which provides a local interface to test and interact with the agent.
Iterative Development
A key advantage of this architecture is the ability to easily expand agent capabilities. By modifying the tools.yaml file to include additional Looker-specific tools—such as those for retrieving dashboards, looks, or explores—and restarting the local application, the agent immediately gains new functionality. This modular approach allows developers to scale from a single-tool agent to a complex system capable of performing tasks like health analysis or data exploration without rewriting the core agent logic.