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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context: it probes each entity with ai_visibility_check, ranks by score, and surfaces which is most/least recognized. It also describes the return structure (score, confidence, signal density). 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 four well-structured sentences with the main purpose front-loaded. Every sentence adds value: purpose, behavior, use case, return format. 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?

Given the tool has 4 parameters, no output schema, and moderate complexity, the description explains the return structure (ranked list with score, confidence, signal density) and the underlying process (probes ai_visibility_check). It covers the essential behavioral and output aspects, though it omits potential edge cases (e.g., ties) or limitations.

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 schema already describes each parameter. The tool description adds meaning beyond the schema: it explains that the first entity is treated as the 'subject' for narrative and the rest as competitors, and provides context on the 'context' parameter ('disambiguates common names'). This additional guidance enhances understanding.

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 purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies a specific verb ('compare'), resource ('AI visibility'), and scope ('multiple entities'). The 'side-by-side' and ranking aspects differentiate it from siblings 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?

The description provides clear usage context: 'Useful for competitive AI-marketing audits' with an example question. It implies when to use (multi-entity comparison) but does not explicitly state alternatives or when not to use. The context signals list ai_visibility_check as a sibling, hinting at the single-entity alternative, but the description could be more explicit.

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