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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds meaningful context: the default model is free, Anthropic probing requires a BYO key with direct payment, and the return shape is described. This goes beyond annotations without contradicting them.

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, followed by key details and use cases. Every sentence earns its place with no 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?

Despite lacking an output schema, the description provides enough detail on return values ('per-model {score, confidence, signals, raw_response} + a combined view') and usage context. Minor gaps (e.g., what 'signals' actually contain) are acceptable given the tool's moderate 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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining parameter behaviors beyond the schema: the default model (Workers AI Llama-3.3-70b), the free nature, and the cost implication of providing `_apiKey`. This enriches the understanding of the `models` and `_apiKey` 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 tool's purpose with a specific verb ('Probe'), resource ('LLMs'), and subject ('business / brand / product / topic'). It also specifies the output (visibility score 0-100 per model). However, it does not explicitly distinguish itself from sibling tools like scan_competitor_ai_presence, so it stops short of a 5.

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 context with concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the default model versus providing an `_apiKey`. It does not mention when not to use the tool or alternatives, so it earns a 4 rather than a 5.

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