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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds meaningful context: default model is free, passing _apiKey incurs direct cost to the user, and it explains the return structure. This goes beyond the annotations and clarifies financial implications.

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, front-loaded with the primary action, and every sentence provides useful information. No fluff or repetition.

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?

With no output schema, the description compensates by listing the exact return fields ({score, confidence, signals, raw_response} + combined view). It also covers parameter defaults, cost implications, and use cases, making the tool understandable without additional documentation.

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 the specific model name (Llama-3.3-70b) and emphasizes that default is free, which is more specific than the schema's 'workers-ai (free default)'. It reinforces the relationship between _apiKey and anthropic model.

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 uses a specific verb ('Probe') and resource ('one or more LLMs'), and clearly states the output (visibility score 0-100 per model). It distinguishes itself from sibling tools by focusing on probing LLM knowledge for any entity, unlike tools like scan_competitor_ai_presence or validate_claim.

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 explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default model and optional Anthropic key. However, it does not explicitly contrast with alternative tools or state when not to use this tool.

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