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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, so safety is covered. The description adds valuable transparency by revealing it 'Probes each entity with ai_visibility_check', ranks by score, and returns a 'ranked list with score, confidence, signal density per entity'. This goes beyond annotations to describe the tool's internal behavior and output format.

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. The first states the core purpose, the second explains the mechanism (probing and ranking), and the third provides a use case and expected output. No filler, well 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?

There is no output schema, but the description compensates by stating the return type: 'ranked list with score, confidence, signal density per entity'. It also explains the role of the first entity (your brand) and competitor relation. Some minor omissions exist, such as potential latency from multiple external calls, but overall it's adequately complete given the rich annotations and schema.

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% with all four parameters described in detail (models, _apiKey, context, entities). The description does not add any parameter-level meaning beyond what the schema already provides; it reinforces the entities array but doesn't deepen understanding. Thus the baseline of 3 applies.

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' and specifies that it 'ranks by score' and 'surfaces which is most/least recognized'. It distinguishes this from sibling ai_visibility_check by focusing on multiple entities and from compare_entities by specifying the AI visibility domain.

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: 'Useful for competitive AI-marketing audits' with a concrete example query. It implicitly indicates this is the multi-entity counterpart to ai_visibility_check, but doesn't explicitly state when NOT to use it or mention alternatives like compare_entities. This is a strong context but lacks explicit exclusions.

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