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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable transparency about cost implications (free default vs. BYO Anthropic key), model default (Workers AI Llama-3.3-70b), and the return structure, going beyond what annotations provide.

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 concise (three sentences), front-loaded with the core purpose, and each sentence earns its place: what it does, model/cost details, return format, and use cases. No wasted words.

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 the tool's moderate complexity (4 params, no output schema), the description provides a complete picture: it explains the return format (per-model {score, confidence, signals, raw_response} + combined view), notes cost behavior, and gives usage context. Annotations cover safety, so nothing critical is missing.

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 meaning beyond the schema by specifying the default model name and clarifying that passing `_apiKey` is required for Anthropic probing, which supplements the schema's parameter docs.

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 clearly specifies the action ('probe one or more LLMs'), the resource (LLMs), and the output (visibility score 0-100 per model). It is distinct from sibling tools like 'ask_pipeworx' or 'scan_competitor_ai_presence' by focusing on scoring model knowledge rather than Q&A or competitor scanning.

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 states clear use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), indicating when to use the tool. It does not explicitly mention alternatives, but the context is clear enough for an agent to select it appropriately.

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