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

Annotations already indicate read-only, idempotent behavior. The description adds context about the free default model and BYO-key for Anthropic, and clarifies payment responsibility, which aids transparency beyond 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 concise, with four sentences covering action, options, return, and use cases. It is front-loaded with the primary action and efficiently uses space.

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 schema covers all parameters and annotations provide behavioral hints, the description offers a fairly complete picture including use cases and return format. Lacking output schema, it still provides enough detail for an agent to understand the tool's output structure.

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 schema fully describes parameters. The description reinforces that models include 'workers-ai' and 'anthropic' and that '_apiKey' is conditional, but adds little new semantic information.

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 it probes LLMs about an entity and scores visibility (0-100) per model, with a specific return format. It distinguishes the tool's purpose from many siblings, though it does not explicitly differentiate from the similar 'scan_competitor_ai_presence' tool.

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 mentions use cases like AI-marketing audits and pre-launch brand checks, and explains the default model and optional Anthropic requirement. It does not provide explicit guidance on when not to use the tool or mention alternatives.

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