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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds useful context: default model is free (Workers AI Llama-3.3-70b), Anthropic requires a BYO API key with direct cost, and it returns per-model scores with confidence, signals, and raw_response. This exceeds the annotations without contradiction.

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 purpose, then key options, return format, and use cases. Every sentence adds value and there is no wasted text.

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 no output schema, the description explicitly states the return format (per-model {score, confidence, signals, raw_response} + combined view). It also covers default model, optional models, and API key behavior. All 4 parameters are documented in the schema, and the description ties them together. It is complete for the tool's 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 slight nuance: it explains that workers-ai is the free default and that _apiKey is 'BYO key — you pay Anthropic directly,' which complements the schema. However, it does not significantly enhance the already well-documented parameters.

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 clearly states the action (probe LLMs) and the resource (a business/brand/product/topic) with a specific output (visibility score 0-100). It is specific and unambiguous, but it does not explicitly differentiate from sibling tools like scan_competitor_ai_presence, so it misses the top score.

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 use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives context for when to use the tool, but it does not mention exclusions or alternatives like 'use this instead of X.'

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