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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive nature. The description adds functional behavioral details: it probes each entity with ai_visibility_check, ranks by score, returns a ranked list with score, confidence, and signal density per entity, and treats the first entity as the subject. No contradiction with 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 two sentences with clear front-loading of the main action, followed by details. Every sentence adds value with no redundancy or filler.

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?

Although there is no output schema, the description sufficiently explains the return value ('ranked list with score, confidence, signal density per entity'). It also covers the entities parameter semantics. While it could mention output format more precisely, the description is adequate for a comparative 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 coverage is 100%, but the description adds meaningful context: it clarifies that the first entity in the 'entities' array is treated as the 'subject' for narrative, and for the 'models' parameter it says 'Omit for just workers-ai'. This adds interpretation beyond the schema 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 uses specific verbs like 'compare', 'probes', 'ranks', and 'surfaces', and clearly states it compares AI visibility across multiple entities side-by-side. It distinguishes from sibling tools like ai_visibility_check (single entity) and provides a concrete use case example.

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 explicitly states it is 'useful for competitive AI-marketing audits' and gives a concrete example. While it does not list when not to use or mention alternatives, the context implies usage for multi-entity comparison versus single-entity checks.

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