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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, covering safety. The description adds behavioral context beyond these: it reveals the tool internally calls ai_visibility_check for each entity, ranks results by score, and treats the first entity as the 'subject'. This explains aggregation behavior and the ordering of inputs, which is valuable for an agent. It stops short of a 5 by not addressing potential rate limits or result volume, but that is acceptable given 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 two sentences and every word earns its place. It starts with a clear high-level purpose, then explains mechanism, gives an example use case, and lists output fields. No filler or irrelevant details. This is exemplary conciseness.

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?

Despite lacking an output schema, the description explicitly states the return format ('ranked list with score, confidence, signal density per entity'), which fully compensates. It also provides a use case, default model behavior, and input semantics. Combined with strong annotations and fully described parameters, this is a complete description for an agent to invoke and interpret results 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?

Schema description coverage is 100%, so the baseline is 3. The description adds extra meaning by explaining the 'entities' array semantics: the first entry is treated as the 'subject' and the rest are competitors, affecting the narrative output. This is not in the schema and directly helps an agent construct correct input. Hence, it earns a 4.

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 clearly distinguishes from the sibling ai_visibility_check by explicitly stating it probes each entity with that tool and aggregates results. It also states the output (ranked list, score, confidence, signal density), leaving no ambiguity about what the tool does.

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') and a concrete example question. It implies when to use this tool versus the single-entity sibling (ai_visibility_check) by contrasting 'multiple entities side-by-side' with probing each entity individually. However, it does not explicitly name alternatives or state when not to use it, 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.