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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses cost implications ('BYO key — you pay Anthropic directly for those calls') and the exact return payload ('per-model {score, confidence, signals, raw_response} + a combined view'). This adds valuable context not present in 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 efficiently cover the core action, configuration, output, and use cases. No repeated or unnecessary content; each clause contributes to selecting and invoking the tool correctly.

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 stating the return structure and typical uses. It could elaborate on the semantics of 'signals' or the 'combined view', but for a read-only probe with clear annotations and schema, this is sufficient.

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 input schema already fully describes all four parameters. The description adds only minor flavor (e.g., default model name, cost nuance) that is not essential to parameter understanding, meeting the baseline for high schema coverage.

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 action: 'Probe one or more LLMs for what they know about a business / brand / product / topic' and defines a measurable output ('score visibility (0-100) per model'). This clearly differentiates it from sibling tools that ask questions or perform 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?

It provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and configuration guidance (default model, optional _apiKey for Anthropic). However, it does not name any sibling alternatives or state when not to use, so it falls 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.