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

Annotations already declare readOnly, idempotent, and openWorld hints. The description adds meaningful behavioral context by explaining that it probes each entity via ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. This goes beyond the safety profile and clarifies the operational mechanism.

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 long, front-loaded with the core action and includes a concrete use case, example query, and output specification. Every sentence adds value with no 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?

The tool has no output schema, but the description explicitly states the return values (ranked list with score, confidence, signal density). The schema covers all parameters and the annotations cover safety. The description sufficiently explains the method and use case, though it omits potential rate limits or cost implications of probing multiple entities.

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?

The input schema has 100% description coverage for all four parameters, including detailed descriptions for models, _apiKey, context, and entities. The description does not add further parameter semantics beyond what the schema already states (e.g., first entity as 'subject'), so the baseline of 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 states a specific action ('Compare AI visibility across multiple entities side-by-side') with clear scope (your brand + N competitors) and outcome (ranks by score, surfaces most/least recognized). It distinguishes itself from sibling ai_visibility_check by focusing on multi-entity comparison and explicitly referencing it as the internal probing mechanism.

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 clear use case ('competitive AI-marketing audits') and an example query, providing strong contextual guidance. It implicitly suggests ai_visibility_check as the single-entity alternative but does not explicitly name it as an alternative or state when not to use this tool, so it falls short of full explicit guidance.

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