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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds important behavioral context beyond this: the default model is free Workers AI, while passing `_apiKey` triggers Anthropic calls and 'you pay Anthropic directly for those calls.' This external cost dependency is a valuable disclosure not captured by 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 sentences, each earning its place: core function, configuration nuance, and return format/use cases. Front-loaded with the primary action, no redundancy or 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?

The description covers the core function, configuration (default vs optional key), output shape (per-model + combined view), and use cases. With full parameter schema coverage and strong annotations, this is nearly complete for a read-only tool. It doesn't mention error handling or rate limits, but those are not critical given the tool's simplicity and annotation context.

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 the baseline is 3. The description adds meaning beyond the schema by explaining the default model when 'models' is omitted, and by clarifying that `_apiKey` is only needed for Anthropic and incurs direct costs. This cross-parameter context helps an agent reason about parameter selection and side effects.

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's function: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It uses a specific verb ('probe') and resource ('LLMs'), and adds concrete output details (per-model score, confidence, signals, raw_response), making it distinct and non-tautological.

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?

It explicitly names use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains configuration prerequisites (pass `_apiKey` to probe Anthropic), providing clear context for when to use the tool. However, it doesn't explicitly compare with sibling tools or state when not to use it, so a 4 is appropriate.

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