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

Annotations already cover readOnly/openWorld/idempotent/destructive hints. The description adds meaningful behavioral context beyond those: cost implications ('you pay Anthropic directly'), the default model fallback, and the return structure ('per-model {score, confidence, signals, raw_response} + a combined view'). 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?

Three sentences, no filler. The core purpose is front-loaded, followed by key configuration notes and use cases. Every sentence earns its place: one for what it does, one for how to configure, one for output and applicability.

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 rich annotations, the description covers the essential contract: it describes the output shape, default model, optional Anthropic usage with cost, and real-world scenarios. It is sufficient for an agent to decide whether and how to invoke the tool.

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 description coverage is 100%, so baseline is 3. The description adds value by explaining the relationship between `models` and `_apiKey` (e.g., key only needed if Anthropic is in models), default behavior when `models` is omitted, and that the key is passed through to api.anthropic.com. This enriches the schema's static parameter descriptions.

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.' This is a specific verb+resource+outcome, and it distinguishes itself from siblings by focusing on LLM knowledge scoring and per-model visibility metrics.

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 explicitly lists use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies when to pass the `_apiKey` (to also probe Anthropic) and that the default model is free. However, it does not name specific alternatives or state when not to use this tool, so it stops short of full exclusion guidance.

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