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

Annotations already declare the tool read-only, open-world, idempotent, and non-destructive. The description adds valuable behavioral detail beyond those flags: the free default model, the BYO-key cost implication ('you pay Anthropic directly'), and the per-model return shape. It does not mention rate limits or failure modes, but the safety profile is already covered by 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?

Three sentences, no filler, front-loaded with the primary action and scoring output. Every sentence contributes: what it does, how to get the optional paid model, what it returns, and when to use it. The structure is compact and scannable.

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 no output schema, the description explicitly lists the per-model return fields and a combined view. It also covers the free vs BYO-key distinction, example use cases, and the entity input is documented in schema. Combined with strong annotations and 100% schema coverage, this is a complete tool description.

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 the baseline is 3. The description adds meaningful semantics by explaining the default model for the 'models' parameter, the requirement/style of '_apiKey' for Anthropic, and that omitting 'models' gives just workers-ai. This goes beyond the raw schema descriptions.

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 opens with a specific verb ('Probe') and a clear resource ('one or more LLMs'), then states the core output: a visibility score (0-100) per model. It also clarifies the domain (business/brand/product/topic) and distinguishes the tool from siblings by focusing on LLM awareness scoring rather than general Q&A or research.

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 gives explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and clarifies the default model plus the _apiKey opt-in for Anthropic. It does not explicitly name alternative sibling tools or state when not to use it, but the context is clear enough for selection.

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