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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds substantial value: it explains the default free model, the optional paid integration via _apiKey (including cost implication), and details the return structure (score, confidence, signals, raw_response per model + combined view). No contradictions with 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 four sentences, highly efficient. The first sentence immediately states the core purpose and output. Subsequent sentences provide essential details (default model, optional key, return structure, use cases) without redundancy or fluff.

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?

Given the moderate complexity (4 parameters with a conditional dependency) and the absence of an output schema, the description covers all essential aspects: purpose, parameter roles (including key handling), return fields, and use cases. It is complete enough for an agent to select and invoke the tool correctly.

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 enhances understanding by clarifying the free vs. paid distinction for models, explaining that _apiKey is only needed when probing Anthropic and is passed directly, and adding context on how the 'context' parameter helps disambiguate. These go beyond schema 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 clearly specifies the action ('probe one or more LLMs'), the resource ('business/brand/product/topic'), and the output ('score visibility 0-100 per model'). It distinguishes from siblings by explicitly listing use cases like AI-marketing audits, pre-launch brand checks, and competitive monitoring, which are not covered by other tools.

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 for when to use the tool (AI-marketing audits, brand checks) and explains model options (free Workers AI vs. paid Anthropic with BYO key). However, it does not explicitly exclude alternative tools or state when not to use it, missing a bit of directiveness.

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