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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and behavioral traits. The description adds value by detailing the default model, cost implications for Anthropic, and the return structure ({score, confidence, signals, raw_response} + combined view). No contradiction 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 very concise (4-5 sentences) with the main purpose front-loaded. Every sentence adds value, covering purpose, default behavior, optional parameters, and return format. 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 complexity (4 params, no output schema), the description is complete: it explains the task, parameters, return format, and use cases. The agent has all necessary information to invoke the tool correctly.

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 documents parameters. The description adds context beyond schema: entity is a brand/business name; models lists supported values; _apiKey involves BYO key and direct payment; context for disambiguation. This enhances understanding for the agent.

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: probing LLMs to score visibility (0-100) per model. It specifies the verb (probe), resource (LLMs), and outcome (score). It also distinguishes by mentioning default and BYO model options, differentiating it from sibling tools like scan_competitor_ai_presence.

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 contexts: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains when to use the default model vs. providing an API key for Anthropic. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.

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