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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context beyond this: the default model is free, the optional Anthropic probe uses a BYO key and incurs direct charges, and the return shape includes per-model score, confidence, signals, and raw_response. This gives a clear behavioral picture without contradicting 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 compact and front-loaded, with the core action and output in the first sentence. The second sentence covers behavioral details (model defaults, BYO key) and return structure, and the final clause lists use cases. Every sentence earns its place; no filler.

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?

With no output schema, the description appropriately explains the return format (per-model object plus combined view). It also covers default model behavior, optional Anthropic integration, and relevant use cases. It could mention what 'signals' represents or potential rate limits, but the core functionality is well covered for a read-only probe tool.

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 each parameter is described in the schema. The description adds extra semantic value by explaining that 'models' can be omitted to use just the free default, and that '_apiKey' is only needed for Anthropic. This clarifies relationships between parameters beyond what the schema provides.

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 uses an explicit verb ('Probe') and specifies the resource (LLMs) and the exact output (visibility score 0-100 per model). It also clearly distinguishes this tool from siblings by focusing on AI visibility scoring rather than general Q&A or research, and mentions specific use cases like AI-marketing audits.

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 context ('Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring') and guidance on model selection (default Workers AI free, optional Anthropic with BYO key). It lacks explicit alternatives or when-not-to-use statements, but the context is strong enough to guide an agent.

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