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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable context beyond annotations, including the free default model ('Workers AI Llama-3.3-70b'), the BYO-key auth/cost behavior ('you pay Anthropic directly'), and the returned structure ('per-model {score, confidence, signals, raw_response} + a combined view'). It does not contradict the 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 three sentences, front-loaded with the core action, and every sentence contributes unique value: operation, parameters/billing, output format, and use cases. No fluff or repetition of schema details.

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 no output schema, so the description compensates by listing the per-model return object and combined view. It covers the main operation, parameters, use cases, and cost/auth context. However, it does not detail what 'signals' or the 'combined view' contain, which is a minor gap for a tool with this complexity.

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 the baseline is 3. The description adds meaning beyond the schema by naming the exact default model ('Llama-3.3-70b') and clarifying the purpose of the `_apiKey` parameter (BYO key, direct billing to Anthropic). This is useful information not present in the parameter descriptions.

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 for what they know about a business / brand / product / topic') and adds a concrete outcome ('score visibility (0-100) per model'). This clearly distinguishes it from sibling tools like scan_competitor_ai_presence, which appear narrower in scope, by covering general brand/product/entity visibility across multiple models.

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 lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives clear context for when to use the tool, though it does not explicitly mention when not to use it or name alternative sibling tools.

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