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

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond these annotations: the default model (Workers AI Llama-3.3-70b) is free, while using Anthropic requires a BYO key and direct payment. It also discloses the return structure (per-model {score, confidence, signals, raw_response} + combined view), which is useful for setting expectations. No contradiction with 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 two sentences with no redundancy. It front-loads the core action, then provides default behavior, output format, and use cases. Every sentence contributes new information, making it highly efficient and well-structured.

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 the moderate complexity (4 params, 1 required) and the absence of an output schema, the description is fairly complete. It states the return format (per-model score, confidence, signals, raw_response + combined view) and provides relevant use cases. Minor gaps exist, such as not detailing the 'combined view' or 'signals', but this is acceptable given the concise format.

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 parameters are well documented. The description adds meaningful semantics beyond the schema: it clarifies cost implications of the _apiKey parameter ('you pay Anthropic directly') and reinforces the default model behavior for the 'models' parameter. While the schema already explains the API key purpose, the cost disclosure is an extra dimension that elevates the score 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.' The verb 'probe' is specific, the resource (LLMs) is identified, and the output (visibility score) is explicit. It distinguishes itself from sibling tools like scan_competitor_ai_presence by focusing on direct LLM probing with a 0-100 score.

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 offers clear context for when to use the tool: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains default model behavior and optional Anthropic probing. However, it does not explicitly state alternatives or when not to use it, so it lacks the exclusionary guidance that would merit a 5.

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