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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false, ensuring the agent knows this is a safe, non-destructive operation. The description goes further by detailing the default model, optional Anthropic key (with cost implication), and the return structure (score, confidence, signals, raw_response per model plus combined view). No contradictions.

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 a single, well-structured paragraph that front-loads the core action and output, then adds details efficiently. Every sentence provides essential information without 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 no output schema, the description adequately describes the return value. Parameters are fully covered via schema and additional context. The tool's complexity (multi-model probing, scoring) is well explained, leaving no major gaps.

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%, and the description adds meaningful context beyond the schema: it explains the default model, clarifies that '_apiKey' is only for Anthropic, and that 'context' helps disambiguate. This enriches the agent's understanding of parameter usage.

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 specifies a concrete action ('Probe one or more LLMs') and a measurable output ('score visibility 0-100 per model'). It clearly distinguishes from sibling tools by focusing on multi-model probing and scoring, not just asking a single model.

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 lists explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), providing clear context for when to use. It does not, however, exclude other tools or specify when not to use it.

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