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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes the automatic decision process between web search and training data, and specifies return type as 'AI-generated answer, potentially grounded in web search results'. Annotations already declare readOnlyHint=true, and description aligns without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise with no fluff. Front-loaded with purpose, then comparison, then structured Args and Returns sections. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only one parameter and an output schema (implied), the description covers the tool's behavior, input, and output adequately. Mentions sibling tool for differentiation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage for the parameter, but the description provides an explicit Args section explaining 'prompt: Any question or instruction. Google AI Mode will search the web if needed and synthesize an answer.' This adds meaningful semantics beyond the schema type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Ask Google AI Mode any question and get an AI-generated answer' - specific verb+resource. Distinguishes from sibling web_search by noting it's for general questions that may not require web search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly compares to web_search and explains that Google AI Mode decides whether to search the web or answer from training data, providing good context on when to use. Lacks explicit 'when not to use' but covers key distinction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.