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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavior beyond that: it is a composite tool that probes each entity, ranks by score, and returns a ranked list with score, confidence, and signal density. This conveys the multi-step, aggregative behavior not present in annotations.

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 focused sentences: what it does, how it does it, and what it returns. Every sentence adds value, no fluff, and the purpose is front-loaded. The use-case question is a nice touch without adding length.

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?

With no output schema, the description adequately describes the return format (ranked list with score, confidence, signal density). It explains the composite behavior and the expected inputs conceptually. It could go deeper on interpreting confidence or signal density, but overall is complete enough for a tool of this moderate complexity.

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 all parameters have detailed descriptions (entities, models, _apiKey, context). The tool description adds no new parameter-level meaning beyond what the schema already provides, such as explaining the 'subject/competitors' distinction already covered in the schema. Baseline 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 clearly states the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. This is a specific verb+resource outcome and differentiates from sibling tools like the singular ai_visibility_check or 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?

Explicitly names the use case ('competitive AI-marketing audits') and provides a concrete example question. It implies the tool is for multi-entity comparison and references ai_visibility_check as the underlying probe, but does not explicitly state when NOT to use it (e.g., single-entity checks). This is clear context, but lacks formal exclusions.

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