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Module 12: Built-in Tools and Grounding

Theory​

Why Built-in Tools Matter​

Traditional AI models have a knowledge cutoff date—they don't know about recent events, current news, or real-time information. Built-in tools solve this by allowing models to ground their responses in current web data. This process of retrieving external information to enhance the LLM's generation is the foundation of Retrieval Augmented Generation (RAG) systems.

Key Advantages:

  • Current Information: Access to up-to-date web content.
  • No Local Execution: Tools run inside the model's environment, requiring no local code execution or infrastructure from you.
  • Automatic Integration: The LLM seamlessly incorporates the search results into its final response.
  • Production Ready: These tools are used by real-world, production applications.

Important: Built-in tools are a feature of Gemini 2.0 and newer models and will raise errors with older versions.

google_search: Web Grounding​

The google_search tool is a built-in capability that allows a Gemini 2.0+ model to search the web to find information. When you add this tool to your agent, the model can autonomously decide to use it when a user's query requires current information.

The search itself is executed within the model's environment. The results are then used by the model to formulate its answer, and information about the searches performed is available in the GroundingMetadata.

google_maps_grounding: Location-Based Queries​

The google_maps_grounding tool enables agents to answer location-based queries, such as finding nearby places, getting directions, or understanding geographic context. This tool is currently only available when using the Vertex AI API.

The GoogleSearchAgentTool Workaround​

A current limitation of the ADK is that built-in tools (like google_search) cannot be directly combined with custom function tools in the same agent's tools list.

To overcome this, the ADK provides the GoogleSearchAgentTool. This is a special wrapper that creates a sub-agent with the google_search tool and exposes that sub-agent as a regular FunctionTool. This allows you to build a primary agent that can use both your own custom tools and the powerful web search capabilities of the wrapped google_search agent.

Key Takeaways​

  • Built-in tools like google_search allow agents to access real-time information from the web, overcoming the knowledge cutoff of the LLM.
  • These tools run within the model's environment, requiring no local code execution.
  • To combine built-in tools with your own custom function tools, you must use a wrapper like the GoogleSearchAgentTool.