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

Annotations (readOnlyHint, idempotentHint, destructiveHint) already establish safety, and the description adds valuable behavioral context: it probes each entity with ai_visibility_check, ranks by score, and returns a structured ranked list. This explains the internal mechanism and output format, going beyond the annotations without contradicting them.

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, front-loaded with the core function, followed by a use case and return format. Every sentence earns its place 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?

With no output schema, the description adequately explains the return value ('ranked list with score, confidence, signal density per entity') and the operational approach. It does not detail model-specific behavior (e.g., requiring _apiKey for anthropic), but the schema covers those parameter details, so the description is sufficiently complete for its complexity.

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%, with each parameter already described in detail (e.g., entities as 2-8 items, first treated as subject). The description mentions 'your brand + N competitors' and 'score, confidence, signal density per entity,' but does not add new parameter meaning beyond the schema. Baseline 3 is appropriate.

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 a specific action ('Compare AI visibility across multiple entities side-by-side') and resource (AI presence across entities). It distinguishes itself from sibling ai_visibility_check by emphasizing multi-entity comparison and ranking, and from generic compare_entities by focusing on AI recognition scores.

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 concrete use case ('competitive AI-marketing audits') and an example query, clearly indicating when to use the tool. It does not explicitly exclude alternatives or state when not to use it, but the multi-entity focus strongly differentiates it from single-entity probes.

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