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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds behavioral context beyond this by stating it probes each entity via ai_visibility_check, ranks results, and returns score/confidence/signal density. This gives the agent a clearer picture of what happens during execution without contradicting the 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?

Three sentences deliver a wealth of information: the core action, the underlying mechanism, the ranking behavior, a concrete use case, and the return format. No redundant words or filler. The most important information is front-loaded.

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?

Despite having no output schema, the description explicitly states what is returned (ranked list with score, confidence, signal density per entity). It also explains the probe mechanism, the subject-first ordering, and the intended use case. Combined with a fully documented schema and appropriate annotations, this gives the agent everything needed to decide when and how to invoke the tool.

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 baseline is 3. The description does not significantly expand on parameter semantics; it only mentions 'your brand + N competitors' which is already reflected in the entities parameter description. The output details are useful but not parameter-specific, so no extra credit beyond baseline.

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.' It clearly distinguishes from siblings like ai_visibility_check by emphasizing multi-entity comparison, ranking, and surfacing most/least recognized. The example use case further clarifies its unique role.

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 context: competitive AI-marketing audits, with an illustrative question. It implies when to use it (comparing multiple entities) but does not explicitly mention alternatives or when not to use it. Since it references ai_visibility_check, an agent might infer that single-entity checks are handled elsewhere, but the exclusion is not stated.

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