answer_query_websearch

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: uses a specific Vertex AI model (gemini-2.5-pro-exp-03-25), incorporates web search for up-to-date info, and requires a query string. However, it lacks details on rate limits, authentication needs, output format, or potential errors. For a tool with no annotations, this is a moderate disclosure but misses important operational context.

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?

The description is appropriately sized and front-loaded, with the core purpose stated in the first sentence. It efficiently conveys the tool's function, model, and enhancement in two concise sentences. There's no wasted text, though it could be slightly more structured by separating usage notes. Overall, it's clear and to the point.

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?

Given the tool's moderate complexity (AI + web search integration), no annotations, and no output schema, the description is somewhat complete but has gaps. It covers the basic operation and model specifics but omits details on response format, error handling, or limitations. For a tool with rich functionality and no structured output, more context would improve completeness.

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 description coverage is 100%, with the single parameter 'query' fully documented in the schema as 'The natural language question to answer using web search.' The description adds minimal value beyond this, only restating that it requires a 'query' string. Given high schema coverage, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

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?

The description clearly states the tool's purpose: 'Answers a natural language query using the configured Vertex AI model enhanced with Google Search results for up-to-date information.' It specifies the verb ('answers'), resource ('natural language query'), and key mechanism (AI model + web search). However, it doesn't explicitly distinguish from its sibling 'answer_query_direct', which likely answers queries without web search, so it misses full sibling differentiation.

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?

The description implies usage context by mentioning 'enhanced with Google Search results for up-to-date information,' suggesting this tool is for queries needing current data. However, it doesn't explicitly state when to use this vs. alternatives like 'answer_query_direct' or other query tools, nor does it provide exclusions or prerequisites beyond the required query parameter. This leaves usage somewhat implied rather than clearly guided.

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