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

Annotations already declare readOnly=true and idempotent, so the description doesn't need to restate that. It adds valuable context by revealing the default model (Workers AI Llama-3.3-70b), cost implications of using Anthropic, and the precise return structure, making behavior transparent. No contradictions 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?

Three sentences, front-loaded with the core purpose, followed by defaults/cost and return structure. Every sentence earns its place, no filler.

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 there is no output schema, the description compensates by specifying the per-model and combined view, plus default model and use cases. It covers all essential aspects: what it does, default behavior, extension, and return 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 baseline is 3. The description goes beyond the schema by specifying that `models` defaults to workers-ai and that `_apiKey` is passed straight through and incurs direct cost, adding practical usage meaning.

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 first sentence clearly states the action and resource: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This specific verb+resource distinguishes it from siblings like scan_competitor_ai_presence or compare_entities, making it unmistakable what this tool does.

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 concludes with 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring,' giving explicit contexts where this tool fits. It also explains when to include `_apiKey` for Anthropic, but does not explicitly name alternatives, so it doesn't earn a 5.

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