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

Annotations already declare the operation read-only, open-world, idempotent, and non-destructive. The description adds cost-related behavior (free default vs. BYO Anthropic key with direct payment to Anthropic) and result structure (per-model score/confidence/signals/raw_response + combined view). It doesn't mention failure modes or rate limits, but the added context goes beyond 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, each with a distinct job: what it does, model options/cost, and return format/use cases. No filler or repetition. Front-loaded with the core purpose.

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?

The tool has 4 parameters (1 required), no output schema, and good annotations. The description explains return values, use cases, model defaults, and cost. It doesn't detail scoring methodology or pagination, but for this simple read-only probe, the description is largely complete. It could briefly mention that signals are contextual, but overall it's sufficient.

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 descriptions cover all 4 parameters (100%), so the baseline is 3. The description adds extra meaning for `models` by specifying the exact default model (Workers AI Llama-3.3-70b) and for `_apiKey` by clarifying the payment relationship. It doesn't add to `entity` or `context`, but the additional default/cost detail pushes it above baseline.

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 uses a specific verb ('probe') and resource ('one or more LLMs') with a clear outcome ('score visibility (0-100) per model'). It also mentions return format and use cases, making the tool's function unmistakable. However, it does not explicitly contrast with sibling tools like 'scan_competitor_ai_presence' or 'ask_pipeworx,' so it stops short of full sibling differentiation.

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 context: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to pass `_apiKey` (to probe Anthropic) and that workers-ai is the free default. It does not mention when to avoid this tool or name alternative tools, so it's clear but lacks exclusions.

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