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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context about the probing mechanism (calls ai_visibility_check per entity), ranking logic, and that it returns a ranked list with score, confidence, and signal density. This goes beyond the annotations and gives agents a clear expectation of the tool's behavior.

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 two sentences, front-loaded with the core purpose, and includes a concrete use-case example and return summary. Every sentence contributes meaningfully, with no filler or redundancy.

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 complexity (4 params, 1 required) and lack of output schema, the description adequately covers what the tool does, how it works (probes each entity, ranks), and what it returns (ranked list with score, confidence, signal density). It omits explicit constraints like the 2-8 entity limit, but that is in the schema and not essential for selection. The use-case example adds practical context. Minor gap: it doesn't mention that most/least recognized is determined by score, but that is implied.

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 does not add additional meaning beyond the schema; it mentions 'your brand + N competitors' which loosely aligns with the 'entities' parameter, but all semantic details (e.g., first entity is the subject, models supported, API key requirement) are already in the schema. No extra value is contributed here.

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 compares AI visibility across multiple entities side-by-side, uses ai_visibility_check to probe each entity, ranks by score, and identifies most/least recognized. This specific verb+resource combination distinguishes it from single-entity tools like ai_visibility_check and generic compare_entities.

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 explicitly states a use case: competitive AI-marketing audits, with an example question. It implies when to use this tool over alternatives (when comparing multiple entities) but does not explicitly mention when not to use it or name alternative tools beyond referencing ai_visibility_check. This is clear but not exhaustive.

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