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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds value by explaining the internal behavior: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. No contradictions with 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 very concise: three sentences that immediately convey purpose, mechanism, and use case. It is front-loaded with the most critical information, with no wasted words.

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 specifies the return format ('ranked list with score, confidence, signal density per entity'). It adequately covers the tool's functionality and context for an AI agent, though it assumes familiarity with the sibling ai_visibility_check.

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%, providing baseline 3. The description mentions 'entities' and 'models' in context but does not add semantic meaning beyond the schema descriptions. No additional parameter-level details are provided, so a 3 is appropriate.

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 verb 'Compare AI visibility' and the resource 'multiple entities', distinguishing it from the sibling tool ai_visibility_check which likely handles single entities. It explains the aggregation and ranking process, making the purpose unmistakable.

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 a realistic question, guiding when to use this tool. It implicitly suggests that for a single entity, one would use ai_visibility_check, but it does not explicitly state when not to use or list alternatives, preventing a 5.

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