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

Annotations confirm read-only, idempotent, non-destructive behavior. The description adds that Workers AI is free, Anthropic requires a key and external payment, and the return format includes per-model details and a combined view. 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 a single paragraph of four sentences, no wasted words. It front-loads the key action (probe and score), lists default model, special key requirement, return structure, and use cases. Every sentence earns its place.

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 100% schema coverage and no output schema, the description adequately explains all parameters, return structure, and use cases. It could elaborate on the scoring methodology, but the information provided is sufficient for selection and inference.

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?

All four parameters are described with added context beyond the schema. The description explains that 'entity' is the topic to probe, 'models' lists supported values, '_apiKey' is optional but needed for anthropic, and 'context' disambiguates common names. This adds meaningful guidance.

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 knowledge about a business/brand/product/topic and scores visibility. It specifies the default model, return fields, and distinguishes among sibling tools by framing use cases like AI-marketing audits and competitive monitoring.

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 explicitly lists use cases (AI-marketing audits, brand checks, competitive monitoring) and mentions that the default model is free while Anthropic requires a key with BYO billing. However, it does not explicitly state when not to use this tool or directly compare to siblings.

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