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

Annotations already provide readOnlyHint, idempotentHint, and openWorldHint, so the description borrows on those. It adds significant transparency: cost ($0.08/call), grounding in an independent live search index (not training data), the precise return format ({query, answer, citations, citationCount}), and payment instructions. This goes well beyond annotations, covering both safety and operational details.

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 well-structured and front-loaded with cost and purpose. Every sentence adds value, though the payment section is a bit lengthy. It is not verbose overall but could be slightly tightened. It maintains a logical flow from what it does, to output, to usage, to payment.

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 that the output schema exists and annotations cover safety, the description is complete for an agent to decide and call correctly. It covers purpose, use case, cost, payment, grounding, and return format. An agent knows exactly what to expect and how to invoke it.

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 coverage is 100%, so the baseline is 3. The description adds meaning by giving practical guidance on max_tokens ('Lower for TL;DR; higher for research questions') and by explaining that output includes citations, which connects the q parameter to the result. It does not just repeat schema descriptions but provides usage nuance.

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 action 'AI-generated answer to a natural-language question' and specifies the resource (grounded in live web search results with source citations). It distinguishes itself from raw search tools like web.search and web.news by emphasizing synthesis plus citations, making its purpose unambiguous.

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?

It provides a clear usage context: 'Useful when an agent needs a synthesized answer plus the receipts to verify or follow up.' This implies when to use it, but it does not explicitly name alternatives or state when not to use it (e.g., when raw search results are sufficient). The context is clear but lacks explicit exclusions.

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