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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: the default model is free (Workers AI), while using Anthropic requires a BYO key and incurs direct user billing. This goes beyond the annotations, though it does not cover rate limits or possible error behavior.

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 two sentences long, front-loaded with the core function, and efficiently includes model defaults, return format, and use cases. Every sentence earns its place with zero 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?

Given there is no output schema, the description compensates by explicitly listing per-model result fields ({score, confidence, signals, raw_response}) and the combined view. It also covers default model, optional Anthropic key, and practical use cases, making the tool's behavior and purpose clear for a 4-parameter tool with only one required parameter.

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 the baseline is 3. The description supplements the schema by clarifying the default model's cost (free), and explicitly notes that the _apiKey is a BYO key with direct payment to Anthropic. This adds semantic value beyond what the schema already provides, justifying a 4 rather than a 3.

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 ('Probe'), names the resource ('one or more LLMs'), the subject ('business / brand / product / topic'), and the concrete outcome ('score visibility (0-100) per model'). It clearly distinguishes itself from siblings by focusing on LLM knowledge visibility scoring rather than direct Q&A or research.

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 ('Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring') and clarifies when to use the Anthropic model ('pass _apiKey to also probe Anthropic'). However, it does not explicitly contrast itself with sibling tools or state when NOT to use this tool, so it falls 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.