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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context beyond annotations: it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density per entity. It does not mention external API calls or rate limits, but for a read-only, non-destructive tool with these annotations, the added return-format and ranking insight justifies a 4.

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, front-loaded with the main purpose, followed by procedural details and a use-case example. Every sentence earns its place: purpose, mechanism with ai_visibility_check, and return format. No fluff, no redundancy with schema or annotations.

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 there is no output schema, the description adequately covers return values ('ranked list with score, confidence, signal density per entity') and the use case. It also clarifies the entity-role nuance ('your brand + N competitors'). However, it does not explain edge cases such as entity count limits (though schema does), tie-breaking, or error handling. For a moderate-complexity comparison tool with rich annotations, this is fairly complete but has minor gaps.

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 schema fully documents all parameters (entities, models, _apiKey, context). The description does not add significant meaning beyond the schema; it mentions 'your brand + N competitors' which aligns with the entities parameter but does not explain syntax or formats further. Baseline 3 is appropriate when the schema carries the parameter documentation burden.

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: 'Compare AI visibility across multiple entities side-by-side' and specifies that it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. This distinguishes it from the sibling tool ai_visibility_check, which likely handles single-entity checks. The verb 'Compare' and resource 'AI visibility' are specific and actionable.

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 clear context for when to use the tool: 'Useful for competitive AI-marketing audits' with a concrete example ('does Claude know about us as well as our competitors?'). It implies use when comparing multiple entities side-by-side, but does not explicitly state when NOT to use it or name alternatives beyond the underlying ai_visibility_check. This is clear context without exclusions, scoring a 4.

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