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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context beyond the annotations: the default Workers AI call is free, while Anthropic calls require a BYO API key and are billed directly to the user by Anthropic. This cost/privacy nuance is useful and not inferred from 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?

Three sentences, front-loaded with the core action, then details on models and return shape, then use cases. No fluff; each sentence earns its place. The structure is exemplary for a tool description of this complexity.

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?

For a 4-parameter tool with no output schema, the description covers the essential points: what it probes, how scoring works, default vs. optional model, return structure, and common use cases. It doesn't detail the 'combined view' or explain failure modes, but given the rich schema and annotations, it is sufficiently complete for an agent to invoke it correctly.

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% and each parameter has a thorough description. The tool description adds minimal extra information: it repeats the 'free default' and BYO key concepts already present in the schema. Since the schema already documents params well, the description provides only marginal value here, so the baseline of 3 is appropriate.

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 verb+resource: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This clearly distinguishes it from sibling tools like ask_pipeworx (which answers questions) and scan_competitor_ai_presence (which focuses specifically on competitor presence). The scope and output are unambiguous.

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') and explains the default model with an option to use Anthropic by passing a key. It does not name alternatives or state when not to use it, but the context is clear enough to guide appropriate selection.

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