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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false), so the bar is lower, yet the description adds real behavioral context: it discloses what is included (rate by model and platform, recent examples) and, importantly, what is excluded (own prompt-generation queries are not citation tests). That inclusion/exclusion disclosure is genuinely useful and not derivable from the annotations.

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 purpose and the primary distinction are front-loaded, and every clause carries meaning. It is dense with nested parentheticals, which slightly impedes parsing, but no sentence is filler.

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 no output schema, the description carries the burden of describing return content, and it does so by naming the rate, the model/platform breakdown, and recent examples. It is largely complete for a read-only metric tool; only the domains parameter behavior is left unaddressed.

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?

There is one optional parameter (domains) with 100% schema description coverage, so the schema already fully documents it. The description never mentions the domains filter or how omitting it affects results, so it adds nothing beyond the schema; baseline 3 is appropriate.

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 states a specific verb and resource: whether AI models mention each brand in answers, with a clear breakdown by question kind (brand_named vs unbranded). It clearly conveys what the tool returns, though it never names or distinguishes itself from the closely related sibling get_how_ai_models_describe_brand, leaving that differentiation to inference.

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 explains the semantics of the two question categories, which implicitly tells the agent what each slice is useful for (brand awareness vs recommendation test). However, it provides no explicit when-to-use guidance and does not route the agent toward or away from alternatives like get_how_ai_models_describe_brand.

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