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

Annotations already declare this as a safe read-only, idempotent operation. The description adds valuable context: the default free Workers AI model, the BYO Anthropic key with direct cost to the user, and the per-model response structure ({score, confidence, signals, raw_response}), which goes beyond annotation metadata.

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 with the main action up front, followed by key details (default model, cost, return format) and use cases. No filler or redundant phrasing.

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?

Despite lacking an output schema, the description explicitly states the return shape and covers key usage scenarios. For a simple read-only tool with fully documented params and annotations, this is adequately complete; it could mention the exact models supported or signal semantics, but that's not critical.

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?

All four parameters are already fully described in the schema (100% coverage), so the baseline is 3. The description adds some cross-parameter context (e.g., '_apiKey' is only needed if 'anthropic' is in the models array) and clarifies cost implications, but it doesn't provide syntax or format details beyond the schema.

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 clearly states the tool's function: querying LLMs about an entity and producing a visibility score (0-100) per model. It distinguishes itself from sibling read/search tools by focusing on AI brand awareness scoring rather than general 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 gives explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), which tells the agent when to choose this tool. However, it doesn't name alternative tools or state when not to use it, only the intended scenarios.

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