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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable behavioral detail: it probes each entity using ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. It also notes the first entity is treated as the subject, which is helpful for narrative interpretation.

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 three sentences with no wasted words. It front-loads the core purpose in the first sentence, then provides process details and an example. Every sentence contributes meaning.

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?

Although there is no output schema, the description explains the return format (ranked list with score, confidence, signal density). It also covers the process and use case. However, it could briefly mention that the models and _apiKey parameters are optional for advanced use, which would make it more complete for agents needing to decide on those inputs.

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%, so all parameters are documented in the schema. The description does not add additional parameter-level details beyond what is in the schema, so it meets the baseline but provides no extra value.

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 it compares AI visibility across multiple entities side-by-side, ranks by score, and identifies most/least recognized. It provides a specific use case example ('does Claude know about us as well as our competitors?'), effectively distinguishing it from the sibling tool ai_visibility_check which checks a single entity.

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 explicitly states it is useful for competitive AI-marketing audits, providing clear context. It does not explicitly exclude alternatives or state when not to use it, but the sibling list implicitly offers alternative single-entity checks. The given example suggests a clear 'when-to-use' scenario.

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