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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context by stating that it probes each entity via ai_visibility_check, ranks by score, and returns a specific structure (score, confidence, signal density). This goes beyond the schema and annotations, enhancing transparency.

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?

Four concise sentences, front-loaded with the primary action, followed by mechanism, use case, and output format. Every sentence adds distinct value without redundancy or fluff, making it efficiently 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 that the tool has no output schema, the description's inclusion of the return structure is essential and covered. The schema fully documents parameters, and annotations cover the safety profile. The description is missing only minor details like default model behavior (which is in the schema) and potential rate limits, but overall it is complete enough for a read-only comparison tool.

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 only marginal semantic context for parameters (e.g., "your brand + N competitors" clarifies the entities param) but does not enhance understanding of models, _apiKey, or context beyond what the schema already states. The schema carries the burden, and the description's contribution is minimal.

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," and clearly differentiates itself from sibling ai_visibility_check by emphasizing multi-entity comparison and ranking. It also states the output type (ranked list with score, confidence, signal density), leaving no ambiguity about what the tool accomplishes.

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 gives a concrete use case ("competitive AI-marketing audits") and an illustrative question, which clearly tells when to use it. It does not explicitly state when not to use it, but the "side-by-side" scope vs. single-entity probe is implicit, so the guidance is clear but lacks explicit exclusions.

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