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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond these: it discloses that using Anthropic requires a BYO API key and that 'you pay Anthropic directly for those calls,' which is a crucial cost implication. It also notes which models are probed by default and the return shape. This enriches the safety/cost profile 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 compact and front-loaded. The first sentence states the core action and result. Subsequent sentences cover default behavior, API key usage, return format, and use cases without unnecessary verbosity. Every sentence contributes valuable information.

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?

Given that there is no output schema, the description compensates by explicitly listing the return structure: 'Returns per-model {score, confidence, signals, raw_response} + a combined view.' Combined with clear parameter documentation and usage scenarios, the description is sufficiently complete for an agent to select and 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?

Input schema coverage is 100%, so the baseline is 3. The description adds some nuance (e.g., 'Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic'), but most of this is already present in the schema descriptions for `models` and `_apiKey`. The description does not significantly extend parameter understanding beyond what the schema already provides.

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 specific 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 differentiates itself from sibling tools by focusing on multi-model visibility scoring rather than simple Q&A or entity resolution.

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: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives strong guidance on when to use it. However, it does not explicitly mention alternatives or when not to use it, so it stops short of full differentiation among sibling tools.

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