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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable behavioral details: the default model (Workers AI Llama 3.3, free), the requirement for a user-provided API key to probe Anthropic (with direct payment to Anthropic), and the return structure (per-model score, confidence, signals, raw_response plus 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 compact three-sentence paragraph. The first sentence states the core purpose, the second adds functional details (default model and optional Anthropic), and the third provides use cases and return summary. Information is front-loaded and each sentence earns its place without redundancy.

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?

The description covers the core functionality, parameters, default behavior, and return structure. Given the absence of an output schema, it compensates by describing the per-model and combined view. However, it could be improved by noting any rate limits or model availability constraints. Overall, it is sufficiently complete for an agent 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 baseline is 3. The description enhances parameter understanding by specifying that 'workers-ai' is the default model and that '_apiKey' is only needed if probing Anthropic. It also clarifies that 'context' helps disambiguate. This adds value beyond the 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 clearly states the verb 'Probe', the resource 'LLMs', and the outcome 'score visibility (0-100) per model'. It also specifies the default model and an optional provider (Anthropic), making the tool's function unambiguous. While not explicitly differentiating from every sibling, its focus on AI visibility scoring is distinct from tools like deep_research or entity_profile.

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 concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. This guides when to use the tool effectively. However, it does not explicitly mention when not to use it or offer alternatives among siblings, slightly reducing the score from perfect.

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