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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond these: it explicitly states it probes with ai_visibility_check, ranks by score, and returns a list with score, confidence, and signal density per entity. This explains the internal orchestration and output shape, exceeding the annotation baseline.

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, front-loaded with the core purpose, followed by the mechanism and a concrete use case example. Every sentence adds value: the first defines the operation, the second explains the underlying call, and the third illustrates applicability and return structure. No wasted words.

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's moderate complexity (4 params, no output schema) and strong annotations, the description sufficiently covers what it does, how it works, and what it returns. It explicitly states the output includes ranking, score, confidence, and signal density, compensating for the missing output schema. The use case example also aids understanding.

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 of 3 is appropriate. The description does not add parameter-specific details beyond the schema; the schema already documents the entities array (first is subject, rest competitors), models, _apiKey, and context. The description's mention of 'your brand + N competitors' reinforces but does not extend 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's function: compare AI visibility across multiple entities side-by-side. It uses a specific verb ('probes'), identifies the resource ('each entity'), and distinguishes itself from the sibling tool ai_visibility_check by emphasizing multi-entity comparison and ranking. The purpose is unmistakable.

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 implies when to use this tool: for competitive AI-marketing audits, comparing multiple entities. It clearly indicates it wraps ai_visibility_check for multiple probes and returns a ranked comparison. However, it lacks explicit exclusions or alternatives (e.g., 'use ai_visibility_check for single-entity checks'), so it falls short of a 5.

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