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

Annotations already communicate read-only, open-world, and idempotent behavior. The description adds valuable context beyond that: the free default model, the requirement to pass an Anthropic API key, and the fact that Anthropic calls are billed directly to the user. This is useful behavioral and cost disclosure.

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 compact and well-structured: it begins with a clear purpose, then explains defaults, return values, and use cases. Every sentence is informative and there is no unnecessary repetition or filler.

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 that there is no output schema, the description rightly includes the return structure ({score, confidence, signals, raw_response} + combined view). It also covers default behavior, costs, and target use cases, making it complete enough for a 4-parameter 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 extra meaning by naming the default model and explaining the relationship between `models` and `_apiKey`, including the cost implication—something the schema alone does not fully convey.

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 has a specific verb ('probe') and resource ('LLMs'), and clearly states the output (visibility score 0-100 per model). It is easy to understand what the tool does, but it does not explicitly distinguish itself from closely related sibling tools such as scan_competitor_ai_presence.

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 gives concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This provides clear guidance on when to use it, but there is no explicit 'when not to use' or comparison with alternative sibling tools.

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