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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds meaningful context by revealing that the tool probes each entity via ai_visibility_check, ranks results, and returns per-entity metrics (score, confidence, signal density), giving agents a clear behavioral model beyond the annotations.

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 tight sentences that front-load the main action, then add behavioral detail, a use case, and output format. Every sentence earns its place with no redundancy.

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 present, the description compensates by explicitly listing return fields (ranked list with score, confidence, signal density). It also explains the probe-and-rank workflow and the number of entities involved, making the tool's behavior fully understandable for decision-making.

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 the baseline is 3. The description adds minimal parameter-specific meaning beyond the schema, mostly restating the 'your brand + competitors' relationship already explained in the entities parameter description.

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 opens with a specific verb+resource: 'Compare AI visibility across multiple entities side-by-side.' It explicitly differentiates from sibling ai_visibility_check by emphasizing multi-entity comparison, ranking, and 'most/least recognized' output.

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 concrete use case: 'competitive AI-marketing audits' with an illustrative question. It clearly implies this is for comparing multiple entities rather than a single check, but does not explicitly state when not to use it or name alternatives beyond referencing ai_visibility_check.

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