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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond annotations: it reveals that the tool internally calls ai_visibility_check per entity, ranks results, and returns a ranked list with score, confidence, and signal density. This is useful procedural 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?

The description is three sentences, front-loaded with the core comparison purpose, followed by a concrete use-case example and a clear summary of return contents. No wasted words; every sentence earns its place.

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 moderate complexity (4 params, 1 required) and no output schema, so the description's mention of the ranked list fields (score, confidence, signal density) is sufficient to set expectations. It could further clarify edge cases or error behavior, but the current text is adequate for an agent to select and invoke the tool correctly.

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 input schema already documents all parameters (entities, models, _apiKey, context). The description adds no parameter-specific meaning beyond what the schema provides—it mentions entities, competitors, and the first-entity-as-subject convention, but these are already 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 opens with a specific verb+resource: "Compare AI visibility across multiple entities side-by-side," which clearly distinguishes it from the sibling single-entity tool ai_visibility_check. It also states the outcome (ranks by score, surfaces most/least recognized), fully covering purpose.

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 provides a clear use case: competitive AI-marketing audits with a concrete example question. It implies this tool is for multi-entity comparisons and mentions probing with ai_visibility_check, but it doesn't explicitly state when NOT to use it or name alternative tools for single-entity checks. Clear context, but no formal exclusions.

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