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

Annotations are present (readOnlyHint: false, destructiveHint: false) but they don't convey much safety. The description adds crucial behavioral context: it costs credits, which is not in annotations, and it indicates that cached results are free, implying a cost model. It doesn't fully describe what data is returned, but given annotations are minimal, this adds significant value.

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 concise (three sentences) and front-loaded with the core purpose. It maximizes information density: what it does, when to use, and cost implications—all without fluff. Every sentence 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?

Given the complexity (one parameter, no output schema, annotations present but minimal), the description covers all essential aspects: purpose, usage trigger, cost behavior, and returned data type. It is complete for the agent to decide and invoke correctly.

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?

The schema has 100% coverage with a single parameter 'brand' described as 'Brand or domain', which is already quite clear. The description reinforces it by mentioning 'brand' in the context. It doesn't add extra meaning beyond the schema, so 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 clearly states the tool's function: it checks how AI assistants cite a brand, what platforms are involved, mention counts, and associated entities. It also distinguishes itself from siblings by specifically addressing AI assistant citations, which is unique among the listed siblings (e.g., research_backlinks for links, research_domain_overview for domain metrics).

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 provides explicit guidance on when to use this tool: when the user asks 'does AI recommend us/them?'. It also mentions cost implications ('Costs credits; cached results are free'), which helps the agent decide when to invoke it and informs about potential retries.

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