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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, fully covering safety and idempotency. The description adds valuable behavioral context: it calls ai_visibility_check per entity, ranks results, and returns a structured list with score, confidence, and signal density. No contradictions.

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: purpose, process, and use case. Every sentence adds distinct value. It is front-loaded with the core action and avoids redundancy with the schema or annotations.

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?

For a tool with 4 parameters and no output schema, the description adequately covers inputs, process, and output structure (ranked list with score/confidence/signal density). Minor gap: it does not explain how confidence or signal density should be interpreted, but given the tool's simplicity, this is acceptable.

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 descriptions for each parameter. The description adds critical semantic meaning: the first entity is treated as the 'subject' for narrative, while others are competitors. It also clarifies that context is shared across all probes and that models and _apiKey control which AI models are used.

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 'Compare AI visibility across multiple entities side-by-side' with a specific verb and resource. It distinguishes from sibling 'ai_visibility_check' by noting it probes multiple entities and ranks them, and from 'compare_entities' by specifying the AI visibility domain.

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 clear use case ('competitive AI-marketing audits') and a concrete example question. It implicitly suggests when to use (comparative analysis) but does not explicitly exclude single-entity use or mention alternatives like 'ai_visibility_check'.

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