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

Beyond the read-only/idempotent annotations, the description adds meaningful behavioral details: default model (Workers AI Llama-3.3-70b) is free, passing _apiKey incurs direct Anthropic costs, and the return structure includes score, confidence, signals, and raw_response plus a combined view.

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: it states the core function, explains the default/cost tradeoff, and lists concrete use cases. There is no redundancy or unnecessary detail.

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 covers the key elements needed for an agent: what the tool does, how to extend it (adding Anthropic), what the response looks like, and when it is useful. It is self-sufficient for selection and invocation.

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?

All parameters are already well-documented in the schema (100% coverage), so the baseline is 3. The description adds value by clarifying the free default model and the cost implication of providing an Anthropic API key, which goes beyond the schema's description.

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') and clearly states the output (visibility score 0-100 per model). It distinguishes itself from siblings like scan_competitor_ai_presence by focusing on per-model scoring and combining results.

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?

Explicit use cases are provided: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' However, it does not mention when not to use this tool or point to alternatives, so it falls short of a 5.

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