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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the safety profile is covered. The description adds genuinely useful scope detail: it names the sampled models (Google Gemini, OpenAI GPT-OSS), which tells the agent the data is a limited sample. It stops short of explaining return format or freshness.

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?

Two sentences, zero filler, with the core capability front-loaded and the model-coverage caveat placed second. Every clause earns its place.

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 does carry the burden of describing returns, and it does so reasonably: citation rate overall and by platform, example queries, and cited URLs. Sampling limitations are disclosed. Minor gaps remain around freshness and pagination, but the essential picture is complete.

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% and the single 'domains' parameter is fully documented in the schema, including the 50-item cap and the omit-for-all-brands behavior. The description adds nothing beyond that, so the baseline 3 applies.

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?

States a specific measurement (AI citation rate when answering shopping questions) and enumerates the breakdown dimensions: overall, by AI platform, with example queries and cited URLs. This is distinguishable from the sibling get_how_ai_models_describe_brand, which concerns descriptions rather than citations.

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 by clarifying what the tool returns, but never states when to reach for it versus alternatives such as get_how_ai_models_describe_brand. No exclusions or prerequisites are given, so the agent must infer the trigger.

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