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

The description fully complements annotations. It explains that the tool is read-only, idempotent, and non-destructive. It details return structure (per-model score, confidence, signals, raw_response) and notes that Anthropic probing requires a provided API key with direct payment to Anthropic.

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, front-loaded with the main action. Each sentence adds value: purpose, default behavior, optional features, and use cases. No wasted words.

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 4 parameters, no output schema, and moderate complexity, the description fully covers inputs, models, optional context, and return structure. It explains the combined view and per-model details, making it self-contained.

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% with all parameters described. The description adds critical context: default model is Workers AI Llama-3.3-70b (free), _apiKey is passed straight through, and context helps disambiguate entities. This goes beyond the schema.

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 purpose: '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 'scan_competitor_ai_presence'.

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 explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains default vs. paid model usage. However, it does not explicitly list when to avoid this tool or compare with alternatives like 'deep_research'.

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