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

The description discloses behavioral traits beyond annotations: it explains the probing mechanism, return format (per-model with score, confidence, signals, raw_response plus combined view), and the cost model (free default vs BYO key for Anthropic). 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 relatively concise at 3-4 sentences, front-loaded with the core action and result. It could be slightly more structured (e.g., bullet points for return fields), but every sentence adds value.

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 adequately explains the return structure (per-model object + combined view). All parameters are described with their roles, and the tool's purpose is fully contextualized for a user unfamiliar with it.

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%, so baseline is 3. The description adds value by explaining the default model for 'models' parameter, the purpose of '_apiKey' (BYO key), and the disambiguation role of 'context'. This exceeds baseline.

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 probes LLMs for awareness of a business/brand/product/topic and scores visibility. It explicitly mentions AI-marketing audits, distinguishing it from sibling tools like scan_competitor_ai_presence or compare_entities which have different scopes.

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 explicit use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use parameters like _apiKey for Anthropic. While it doesn't state when not to use, the context is clear and it implies alternatives exist among siblings.

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