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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral details: it probes each entity internally using ai_visibility_check, ranks by score, and returns a structured result with score, confidence, and signal density per entity. No contradictions.

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, each serving a distinct purpose: main action, method and output, and use case. No unnecessary words; information is front-loaded and well-structured.

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?

Given the lack of output schema, the description adequately describes the output format (ranked list with score, confidence, signal density per entity). All parameters are covered by schema descriptions, and the description adds the competitive audit context. The tool's behavior is fully explained.

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 meaning beyond the schema by noting that the first entity is treated as the 'subject' for narrative. It also explains the purpose of the 'context' parameter implicitly through the example and use case.

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: comparing AI visibility across multiple entities, probing each with ai_visibility_check, ranking, and surfacing which is most/least recognized. It distinguishes from sibling tools like 'ai_visibility_check' (single entity) and 'compare_entities' (generic comparison).

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, implying when to use this tool over alternatives like the single-entity 'ai_visibility_check'. It lacks explicit exclusions or when-not-to-use guidance, but the context is sufficiently clear.

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