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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds context about the dataset's source and composition, but doesn't disclose any additional behavioral traits like authentication or rate limits, which is acceptable for a simple info tool.

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 concise and front-loaded: the first sentence states the core purpose, and the second adds valuable context about the dataset. Every sentence earns its place with no redundancy.

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 parameterless tool with no output schema and annotations covering safety, the description is complete. It tells the agent what the tool does, what the dataset contains, and why it's useful, leaving no critical gaps for invocation.

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 zero parameters, the baseline is 4. The description needs no parameter explanation, and the schema is trivially complete.

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 ('Get details and pricing') and the resource ('US Bank Regulatory Stress Dataset'), and elaborates on the dataset's contents. It distinguishes from the sibling lookup_bank_stress_metrics by focusing on dataset-level info rather than per-bank metrics.

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 a clear use case: for agents building bank-screening or credit-risk models that need a raw data input. It implicitly contrasts with the sibling tool by framing this as obtaining dataset details/pricing rather than querying metrics, though it doesn't explicitly name the alternative.

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