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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior. The description adds valuable behavioral context beyond annotations: default model choice (Workers AI Llama-3.3-70b, free), cost implications (BYO Anthropic key with direct payment), and the detailed return structure (score, confidence, signals, raw_response, combined view). No contradiction exists.

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, front-loaded with the core purpose, followed by configuration details and use cases. Every sentence contributes essential information 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 lacking an output schema, the description explicitly states the return format (per-model object with score, confidence, signals, raw_response, and combined view), covers all parameters (entity, models, _apiKey, context), and lists common usage scenarios. It is complete for a simple read-only probe tool with four parameters.

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 meaningful context by explaining that workers-ai is the free default and that providing an _apiKey enables Anthropic, with associated direct costs. This clarifies the models and _apiKey parameters beyond the schema 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 explicitly states the tool's function: probing one or more LLMs for what they know about an entity and scoring visibility (0-100) per model. It uses specific verbs and resource types (business/brand/product/topic) and clearly differentiates from sibling tools by focusing on AI visibility scoring with use cases like competitive monitoring.

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 provides clear usage context: default free model, optional Anthropic with BYO key and direct payment, and typical use cases such as AI-marketing audits, pre-launch checks, and competitive monitoring. It does not explicitly name alternative tools or exclusion criteria, but the context is sufficient for an agent to decide when to invoke this tool.

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