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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds valuable behavioral context beyond annotations: it reveals cost implications (BYO Anthropic key, paid directly), default model choice (Workers AI Llama-3.3-70b free), and the structure of the response (per-model score, confidence, signals, raw_response plus combined view). This goes beyond the baseline and enriches the agent's understanding.

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: it starts with the core purpose, then covers default behavior, output format, and use cases in a few sentences. Every sentence contributes essential information, with no redundancy or fluff. It is highly efficient while still being comprehensive.

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?

There is no output schema, but the description explicitly describes the return format (per-model {score, confidence, signals, raw_response} plus a combined view), which addresses that gap. It also provides enough context for typical use cases and parameter interactions. Minor omissions like error handling or rate limits are not critical for this kind of read-only probe tool, so it earns a 4.

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 baseline is 3. The description adds meaningful relationships: it explains that `_apiKey` is only needed when 'anthropic' is in `models` and that the default model is free. It also clarifies the `context` parameter's purpose ('helps disambiguate common names'), which adds value beyond the raw 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 clearly states the action ('Probe one or more LLMs') and the resource ('what they know about a business / brand / product / topic') while specifying the output (visibility score 0-100 per model). It distinguishes itself from siblings by focusing on LLM visibility scoring, which is a unique capability among the listed tools.

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 use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to use the optional Anthropic model (by passing `_apiKey`). However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of a 5.

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