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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is clear. The description adds behavioral detail: it calls external LLMs, requires a BYO key for Anthropic (with cost implication), and returns per-model scores. It does not disclose potential rate limits or caching, but overall adds sufficient context beyond 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 long with no redundant information. The first sentence front-loads the core action and output, followed by model details, return format, and use cases. Every sentence adds value, making it efficient for an AI agent to parse.

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 lacking an output schema, the description details the return structure (per-model object with score, confidence, signals, raw_response, plus combined view). Use cases are provided. Missing elements like error handling or pagination are acceptable given the tool's simplicity and the presence of comprehensive annotations.

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 enriches parameter semantics by explaining the default model behavior, the conditional use of _apiKey, and the disambiguation role of context. This goes beyond the schema's descriptions, adding practical guidance for parameter values.

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 the verb 'probe' and specifies the resource (LLMs) and the output (visibility score 0-100 per model). It clearly distinguishes the tool's purpose from siblings like ask_pipeworx by focusing on multi-model visibility scoring rather than single entity Q&A. The mention of specific use cases (AI-marketing audits, pre-launch brand checks) further clarifies its niche.

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 states when to use the tool (for AI-marketing audits, pre-launch brand checks, competitive monitoring) and gives model selection guidance (default workers-ai, optional Anthropic with BYO key). However, it does not explicitly list when not to use it or suggest alternative tools for narrower questions, leaving some ambiguity for an AI agent.

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