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

Annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true. The description adds valuable behavioral context beyond that: it states the default model is free, notes that Anthropic calls require a BYO key and 'you pay Anthropic directly,' and describes the return structure (per-model score, confidence, signals, raw_response + combined view). This goes well beyond the 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, each carrying distinct information: the primary function and output, default model and cost implications, return shape, and use cases. It is front-loaded and free of redundancy.

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 having no output schema, the description explicitly states the per-model return fields and combined view. It covers when to use, parameter nuances (including the optional Anthropic key), and cost implications. For a 4-parameter tool with no output schema, this is thoroughly complete.

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 each parameter already has a description. The description enriches these by naming the default model (Workers AI Llama-3.3-70b) and clarifying that _apiKey is passed straight through to Anthropic. This adds meaning beyond the schema's baseline definitions.

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 opens with a specific verb 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 clearly states the action, scope, and key output, distinguishing it from sibling data-query tools by focusing on LLM knowledge and visibility scoring.

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 lists explicit use cases — 'AI-marketing audits, pre-launch brand checks, competitive monitoring' — and explains when to provide an API key and the default model behavior. However, it doesn't name alternative tools or explicitly state when not to use it, so it stops short of a 5.

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