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

Annotations already cover safety (readOnly, idempotent, non-destructive). The description adds valuable behavioral context: it probes each entity, ranks by score, surfaces most/least recognized, and specifies output fields (score, confidence, signal density). This goes beyond the annotations to clarify exactly what the tool does.

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 two sentences, with the main purpose front-loaded. It includes a use case, a method reference, and output details, with zero wasted words. 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?

With no output schema, the description still specifies the return structure (ranked list with score, confidence, signal density). It omits details like entity count limits and model/API key requirements, but those are fully covered in the parameter schema, so the description does its job for a tool of this simplicity.

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 input schema has 100% description coverage for all parameters, so the baseline is 3. The description adds minimal parameter insight beyond the schema, such as 'your brand + N competitors' for entities, but does not introduce syntax or format details not already present. No compensation needed given full schema coverage.

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 purpose with a specific verb ('Compare') and resource ('AI visibility across multiple entities side-by-side'). It explains the process (probes with ai_visibility_check, ranks by score) and differentiates from the single-entity sibling ai_visibility_check by explicitly targeting multiple entities.

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 a clear use case: competitive AI-marketing audits, with an example question. It implicitly contrasts with ai_visibility_check by focusing on multiple entities. However, it does not explicitly state when not to use it or name direct alternatives, so it falls just short of a 5.

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