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

Discloses that the default model is free, that Anthropic calls require a BYO key and cost the user directly, and that results include per-model {score, confidence, signals, raw_response} plus a combined view. Annotations already establish read-only/idempotent behavior, so the description adds cost and model-selection context 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?

Four sentences pack the core action, cost behavior, return shape, and use cases without redundancy. The most important action is front-loaded in the first sentence.

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?

For a read-only probe with no output schema, the description supplies the return structure, default behavior, cost caveat, and typical applications. This is sufficient for an agent to select and invoke the tool correctly, matching the completeness of top-tier references.

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 all four parameters are already documented. The description adds value by explaining the default model for the `models` parameter and the cost implications of `_apiKey`, plus giving concrete `entity` examples. This builds on, rather than repeats, the schema.

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?

Clearly states it probes LLMs for knowledge about a business/brand/product/topic and produces a 0-100 visibility score per model. The verb 'probe' and specific output distinguish it from sibling tools like deep_research or compare_entities, which focus on different research objectives.

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?

Provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and notes the default free model vs BYO Anthropic key. However, it does not name alternative tools or say when not to use it, so it earns a 4 rather than 5.

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