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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing a safe read operation. The description adds valuable context beyond annotations: the free default model (Workers AI Llama-3.3-70b), that Anthropic requires a BYO key with direct payment, and the per-model return structure including score, confidence, signals, and raw_response plus a combined view.

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 three sentences with no filler. It front-loads the core action and output (probe LLMs, score visibility), then covers defaults and return format, and ends with use cases. Every sentence earns its place.

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 purpose, usage contexts, default behavior, cost considerations, and return structure even though there is no output schema. It does not mention limitations, rate limits, or edge cases, but the tool's complexity is moderate and the provided information is sufficient for an agent to select and invoke it 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaning by specifying the default model for `models` and the cost implication of `_apiKey` ('you pay Anthropic directly'), which enriches the bare schema descriptions. However, most parameter meaning is already well-documented in the schema itself.

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 uses a specific verb 'Probe' with a clear object (LLMs) and a measurable outcome (visibility score 0-100). It clearly differentiates from sibling tools like ask_pipeworx by focusing on LLM knowledge visibility rather than general Q&A, and lists concrete use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring).

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 provides clear usage contexts: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly name alternatives or give when-not-to-use guidance, but it does explain when to pass `_apiKey` (to also probe Anthropic) and the default model behavior, which is actionable guidance for choosing this tool.

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