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

Beyond the annotations (read-only, open-world, idempotent, non-destructive), the description adds meaningful context: the default model and its cost (free), the BYO-key flow where users pay Anthropic directly, and the per-model return structure. It does not disclose rate limits or error behavior, but the added cost and key-handling details justify a 4.

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 three sentences: the first states purpose and output, the second explains model selection and key handling, the third lists use cases. Every sentence earns its place, information is front-loaded, and there is no redundancy or fluff.

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 no output schema, the description compensates by summarizing the return shape (per-model {score, confidence, signals, raw_response} plus combined view). It covers core usage, cost, and use cases. However, it does not explain what 'score', 'confidence', or 'signals' mean or how to interpret them, leaving a minor gap for such a 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 coverage is 100%, so the baseline is 3. The description adds minor value by noting the default model (`workers-ai`) and that `_apiKey` is needed for Anthropic, but these largely mirror the schema descriptions. No significant extra meaning is provided for `entity` or `context`.

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: probing LLMs for knowledge about an entity and producing a 0-100 visibility score per model. The verb 'probe' and specific output (score, confidence, signals) strongly convey purpose. However, it does not differentiate from the sibling tool 'scan_competitor_ai_presence', which likely overlaps in function, so it misses the explicit sibling differentiation needed for a 5.

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 clear use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains when to pass `_apiKey` (to include Anthropic) and notes the free default. Yet it does not offer explicit when-not-to-use guidance or mention alternative sibling tools, stopping 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.