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 valuable behavioral context by revealing that it delegates to ai_visibility_check, ranks results, and returns a ranked list with specific fields. This goes beyond annotations without contradicting them.

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?

Three sentences, each earning its place: the first states the core function, the second describes the mechanics and output, and the third gives a concrete use-case example. No wasted words, front-loaded with purpose, and highly scannable.

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 compensates by naming the return elements ('ranked list with score, confidence, signal density per entity'). It also provides real-world context. It could go further by detailing edge cases (e.g., what happens if an entity fails to probe), but given the annotations and schema coverage, this is sufficiently complete.

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 coverage is 100%, so parameters are already well-documented. The description enhances this by explaining the entities array structure: 'First entry treated as the "subject" for narrative; rest are competitors'. It also clarifies that models and _apiKey are for probe configuration, adding meaning beyond the schema's descriptions.

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's function: 'Compare AI visibility across multiple entities side-by-side'. It specifies the method (probes with ai_visibility_check, ranks by score) and the output (which is most/least recognized). This distinguishes it from sibling tools like ai_visibility_check (single entity) and compare_entities (generic 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 explicit use-case context: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"'. It also implies the relationship to ai_visibility_check by mentioning it as the probing mechanism. However, it doesn't explicitly state when not to use it or name alternative tools for single-entity checks, so it falls short of a 5.

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