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

The annotations already declare readOnly, openWorld, and idempotent hints. The description adds valuable context beyond these: default model is free (Workers AI Llama-3.3-70b), Anthropic calls require a BYO key and cost the user directly, and the return structure includes per-model fields. No contradiction 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?

Three dense sentences: the whole description is under 350 characters, front-loaded with the primary action, then model details, return shape, and use cases. Every sentence carries distinct information; no fluff or repetition.

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?

Despite no output schema, the description specifies the return fields ({score, confidence, signals, raw_response} + combined view). Combined with 100% parameter schema coverage and well-disclosed annotations (read-only, idempotent), the only marginal gap is explaining what 'signals' means, but this is a minor nuance. The description fully equips an agent to invoke the 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 descriptions provide 100% coverage, but the description enriches key meanings: it explains the default/free model, the meaning of `_apiKey` (BYO, you pay Anthropic), and the semantics of omitted `models` (default to workers-ai). This adds value beyond the raw schema, though slightly redundant with the schema.

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 and resource: 'Probe one or more LLMs for what they know...' and defines the output as a visibility score (0-100) per model. It distinguishes itself from siblings by focusing on LLM knowledge scoring, clearly relevant to AI-marketing audits and brand checks.

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?

Usage context is clear: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to pass `_apiKey` (to probe Anthropic) and the default model. However, it does not explicitly state when not to use it or mention alternative tools for related tasks.

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