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

With no annotations provided, the description carries the behavioral transparency burden. It discloses several meaningful behaviors: opening every source, classifying sources as real/invented/paywalled, returning a public receipt URL, and costing $1.00 in USDC. It could add more about failure modes or external calls, but it already reveals significant behavioral detail beyond a simple operation.

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?

With four short sentences, the description is economical and well-structured. It front-loads the core function, then the output, then the critical reasoning for when to call it, and finally the cost. Every sentence provides useful context and nothing feels redundant.

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 no output schema or annotations, the description does a strong job of covering the primary purpose, output type ('public receipt URL'), rationale, and pricing. It could be more complete by explaining the optional engine/question parameters and what additional context those provide, but overall it gives adequate context for an agent to decide to use the tool.

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 description coverage is only 33% because only 'answer' has a schema description, while 'engine' and 'question' have no meaning explained. The tool description elaborates on what constitutes the 'answer' and what verification will do with it, but it does not describe the purpose or expected values of the engine or question 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 starts with a specific action: 'Check an entire AI answer' and details exactly what it does with cited sources—'open every source it cited, say which are real, which are missing or invented, and which are merely paywalled.' This clearly distinguishes it from siblings like verify_claim or verify_source by focusing on the whole answer and its collection of sources.

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 gives a strong, explicit usage signal: 'Call before storing or repeating another model's answer — an invented source that enters your state is re-reasoned over on every later run.' It provides context for when to call, though it does not explicitly reference alternatives or state when not to use it.

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