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

Annotations already declare read-only, idempotent, non-destructive behavior, and the description adds valuable context beyond that: the default free Workers AI model, the BYO-API-key cost implication for Anthropic calls, and the per-model return structure. It does not mention potential timeouts or error handling, but given the strong annotation coverage, the added cost and model behavior details warrant a 4.

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 (~70 words), front-loaded with the core purpose in the first sentence, and every subsequent sentence adds necessary detail (default behavior, cost, return format, use cases). There is zero 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?

With no output schema, the description compensates by explicitly listing the return shape (per-model {score, confidence, signals, raw_response} + combined view). It covers default model, optional auth, and realistic use cases. Minor gaps remain around failure modes or cache behavior, but for a 4-parameter tool this is well-rounded and nearly 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 meaning beyond the schema by naming the exact default model ('Workers AI Llama-3.3-70b'), clarifying that omitting 'models' yields just the free default, and explaining the _apiKey pass-through semantics (BYO key, direct billing). This enrichment pushes the score to 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 uses specific verbs ('Probe', 'score visibility') and a clear resource ('one or more LLMs'), with a concrete output range (0-100 per model). It distinguishes itself from general question-answering tools by emphasizing per-model scoring and a combined view, making the purpose unambiguous.

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 clearly states when to use the tool ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), providing strong contextual guidance. However, it does not explicitly name alternatives or state when not to use it, especially given the similar sibling tool 'scan_competitor_ai_presence', so it misses full exclusion guidance.

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