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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral details: it internally calls ai_visibility_check for each entity, ranks by score, and returns a ranked list with per-entity metrics. No contradictions 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, each providing distinct information: main action, method, and usefulness. No unnecessary words. Front-loaded with the core purpose.

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?

Given no output schema, the description explains the return format (ranked list with score, confidence, signal density) and internal mechanics (uses ai_visibility_check per entity). Could detail ranking criteria more, but sufficiently complete for a wrapper 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?

All four parameters have 100% schema description coverage. The description enriches semantics by stating that the first entity in 'entities' is treated as the subject and that 'models' defaults to only workers-ai if omitted, which is not in the schema.

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 compares AI visibility across multiple entities side-by-side, using a specific verb ('Compare'), resource ('AI visibility'), and action (probing with ai_visibility_check, ranking, surfacing most/least recognized). It distinguishes itself from siblings like ai_visibility_check (single entity) and compare_entities (general 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?

The description explicitly states the tool is useful for competitive AI-marketing audits, giving an example question. It implies single-entity checks would use ai_visibility_check, but does not explicitly state when not to use or list alternatives. Still, context is clear.

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