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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context beyond that: the default model is free (Workers AI Llama-3.3-70b), using Anthropic requires a BYO key with direct costs to the user, and the output includes per-model details. There is no contradiction with the 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?

The description is three sentences long, front-loaded with the core purpose, then model options and output structure, and finally use cases. Every sentence contributes meaning without redundancy or fluff.

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 having no output schema, the description explicitly lists the return fields per model (score, confidence, signals, raw_response) and mentions a combined view. It also covers cost behavior, default model, and practical use cases. For a 4-parameter tool with these annotations, this is fully self-contained.

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 the baseline is 3. The description adds extra meaning beyond the schema: it clarifies that `models` can be omitted for the default workers-ai, `_apiKey` is only needed if 'anthropic' is in models, and that providing a key incurs Anthropic charges. This enriches the parameter understanding beyond the raw field descriptions.

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 states the tool 'probes one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This is a specific verb ('probe') tied to a clear resource (LLMs about an entity) and output (visibility scores). It distinguishes itself from sibling tools by focusing on cross-model visibility scoring rather than general Q&A or entity resolution.

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 usage contexts: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' This clearly implies when to use the tool, but it does not explicitly name alternatives or exclusion criteria. Since no sibling alternatives are referenced, it stops short of the highest standard, but the context is clear.

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