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

The description adds meaningful behavioral detail beyond the annotations: it probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, and signal density. This goes beyond the read-only/idempotent hints. It does not disclose rate limits or cost implications, but the annotations cover the safety profile adequately.

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, each earning its place: first states the core function, second explains the mechanism and output, third gives a use case. No redundant wording, and the formatting is clear with a quoted example.

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 tool's moderate complexity (4 params, no output schema), the description is fairly complete: it explains what it does, how it works, what it returns, and when to use it. It lacks explicit mention of model options or entity ordering (first is subject), but those are covered in the schema or are minor gaps. Overall, it provides enough context for reliable invocation.

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 provides 100% coverage for all parameters, including descriptions for models, _apiKey, context, and entities. The tool description adds no additional parameter semantics beyond what the schema already offers, so the baseline score 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 uses a specific verb ('Compare') and clearly identifies the resource ('AI visibility across multiple entities'). It distinguishes itself from the sibling tool ai_visibility_check by emphasizing side-by-side comparison and ranking, and from compare_entities by focusing specifically on AI visibility rather than generic entity comparison.

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?

Provides clear context: 'Useful for competitive AI-marketing audits' with a concrete example question. It implies when to use this tool over ai_visibility_check (single entity) by describing multi-entity probing, but does not explicitly state exclusions or alternatives. Still, the guidance is sufficient for an agent to select it appropriately.

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