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

Annotations already provide readOnlyHint and idempotentHint, so the safety profile is known. The description adds valuable behavioral context beyond annotations: it reveals that the tool makes multiple probes (one per entity), ranks results by score, and returns a ranked list with score, confidence, and signal density. No contradictions found.

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 exactly two sentences, efficient and front-loaded. The first sentence states the action and mechanism; the second provides the use case and return value. Every sentence contributes meaningfully without 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?

Despite having no output schema, the description clearly states what the tool returns ('ranked list with score, confidence, signal density per entity'). It also covers the use case, the role of the first entity, and the underlying probe mechanism. With rich annotations and full schema coverage, the description fully equips an agent to use the tool 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 coverage is 100%, so the baseline is 3. The description adds extra semantic value by clarifying that 'First entry treated as the "subject" for narrative; rest are competitors', which is not in the schema description. This gives the agent important context for how to structure the entities array.

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 function: 'Compare AI visibility across multiple entities side-by-side' and explicitly mentions the underlying mechanism ('Probes each entity... with ai_visibility_check, ranks by score'). It distinguishes from sibling tools by focusing on multi-entity comparison for AI-marketing audits, which is unique among the listed tools.

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') and explains that it probes each entity with ai_visibility_check, implying it's for multi-entity scenarios. However, it does not explicitly state when not to use it or directly contrast with alternatives like compare_entities, despite being clear enough for an agent to infer the right context.

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