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 covered. The description adds valuable context beyond annotations: the cost implication of using Anthropic ('you pay Anthropic directly'), the default model choice, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). This is useful, though it doesn't cover failure modes or rate limits.

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 compact and front-loaded with the primary purpose. Every sentence adds value: purpose, default behavior/cost, return format, and use cases. There is no redundancy or filler.

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 explains the return format precisely. It covers default behavior, API key usage, cost implications, and typical use cases. With 4 parameters (1 required) and no output schema, this description is fully adequate for an agent to understand the tool's behavior and expected outputs.

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 ('Workers AI Llama-3.3-70b (free)') and clarifying the relationship between `_apiKey` and `models` (passing `_apiKey` enables Anthropic probing). It also explains the score scale (0-100), which is not fully captured in the schema properties.

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 states the action ('Probe one or more LLMs'), the resource (LLMs), and the specific output (visibility score 0-100 per model). It distinguishes itself from sibling tools by emphasizing scoring and per-model analysis, and it mentions concrete use cases like AI-marketing audits and 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 explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default model and optional Anthropic probing. It does not explicitly mention alternatives or exclusions, but the guidance is clear enough for an agent to select this tool for relevant tasks.

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