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

Annotations already declare readOnly, idempotent, and openWorld behavior. The description adds valuable behavioral context: it probes each entity using `ai_visibility_check`, ranks by score, and returns per-entity fields (score, confidence, signal density). This goes beyond the annotations by explaining mechanism and output format, so a 4 is appropriate.

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 compact and front-loaded with the core action. Each sentence earns its place: the first states the primary function, the second explains the mechanism and output, and the third provides a concrete use case. The example quote is illustrative without being wordy. No filler or redundant phrasing.

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 fully explains return values ('ranked list with score, confidence, signal density per entity'), usage ('competitive AI-marketing audits'), and behavior (probes with ai_visibility_check, ranks). With rich annotations and a 100% schema-coverage, this description gives an agent everything needed to select and invoke 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 description coverage is 100%, so the baseline is 3. The description adds no extra parameter detail beyond what the schema already states—it mentions 'your brand + N competitors', which mirrors the `entities` parameter's existing description. Since the schema fully documents parameters and the description doesn't need to compensate, the baseline score of 3 stands.

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 further explains ranking and output ('ranks by score, surfaces which is most/least recognized'), which clearly distinguishes it from the single-entity sibling `ai_visibility_check`. The example use case('does Claude know about us as well as our competitors?')makes the purpose immediately accessible.

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 usage context: 'Useful for competitive AI-marketing audits' and the example scenario. It implies the tool is for comparing multiple entities rather than checking a single one, but it does not explicitly state when not to use it or name alternative tools. Since it gives clear situational guidance without explicit exclusions, it earns a 4.

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