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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, providing a strong safety baseline. The description adds relevant context about external calls: the default model is free (Workers AI Llama-3.3-70b), and passing _apiKey enables Anthropic probes with the user paying directly. This clarifies auth needs and the external nature of the calls 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 four sentences, each earning its place: purpose, model defaults/API key, return format, and use cases. It is front-loaded with the core function and contains no fluff or 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?

Despite no output schema, the description explicitly lists the per-model return fields (score, confidence, signals, raw_response) plus a combined view, covering return values. It also explains model selection and use cases, making the tool fully comprehensible for an agent to decide when to invoke it and how to interpret results.

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 baseline is 3. The description adds value beyond the schema by explaining the default model behavior when 'models' is omitted and the dependency between '_apiKey' and the 'anthropic' model option, which clarifies how parameters interact. This elevates it above the baseline.

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.' This is a specific verb+resource combination that distinguishes it from siblings like ask_pipeworx or deep_research, and the use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) further clarify its unique role.

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 context for when to use the tool ('Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring'), but it does not explicitly mention when not to use it or name alternative tools. It also explains model selection (default vs. Anthropic with _apiKey), which is practical guidance, but lacks explicit exclusions.

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