Benchmark vs industry/geo

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

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds that results are 'Backed by aggregated audit data,' which provides data source context but does not disclose additional behavioral traits beyond what annotations convey.

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?

Three concise sentences with front-loaded main action: first sentence states purpose, second provides usage examples, third gives data source. No unnecessary words.

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?

Covers purpose, usage, and data source. Missing details on return format (e.g., percentile, visual) which could aid agent understanding, but given no output schema, the description is fairly complete for a read-only benchmarking tool.

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%; the description does not add new meaning beyond the schema's parameter descriptions. It mentions industry and location but those are already described in the schema.

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?

Description clearly states the tool benchmarks a website's AI visibility against industry and geo. It distinguishes from absolute number tools and from sibling compare_competitors by specifying percentile/peer context rather than direct competitor comparison.

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?

Provides explicit usage scenarios (e.g., 'how do I compare to others,' 'is this a good score for my industry') and notes it's for percentile/peer context. Lacks explicit when-not-to-use guidance, but positive cues are strong.

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