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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds valuable behavioral context beyond annotations: default model (Workers AI Llama-3.3-70b, free), the BYO-key model for Anthropic, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). This transparency helps the agent understand cost implications and expectations without contradicting 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?

The description is tightly written—four sentences covering purpose, default behavior, return shape, and use cases. Information is front-loaded with the core verb and outcome. No redundant text. Slightly dense but well-structured; every sentence earns its place. A 4 reflects strong conciseness without being too sparse.

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?

There is no output schema, so the description compensates by detailing the per-model response fields and the combined view. All four parameters are already covered by the schema (100% coverage). The description does not explain edge cases (e.g., what happens if _apiKey is passed without 'anthropic' in models), but the schema and description together give the agent sufficient information for safe invocation. With moderate complexity, this is adequately complete.

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%, so baseline is 3. The description adds meaningful semantics: 'pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls)' explains the _apiKey lifecycle and cost responsibility. It also states 'Omit for just workers-ai' for the models parameter, clarifying default behavior. This goes beyond schema descriptions, justifying a 4.

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 the tool from sibling tools like scan_competitor_ai_presence by its focus on per-model scoring and LLM knowledge probing rather than broader market scanning. The purpose is unambiguous and immediately actionable.

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 explicitly states use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' This provides clear context for when to invoke the tool. It does not explicitly name alternatives or exclusions, but the use-case framing gives strong guidance. Given the rich sibling list, a brief mention of alternatives would push this to 5.

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