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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint:false. The description adds that it returns parameter names, descriptions, data types, and requiredness, which is consistent with a safe read operation. It does not contradict annotations and offers useful context about the return content.

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?

A single, front-loaded sentence that conveys the purpose, scope, and output without wasted words. It is appropriately concise for the tool's simplicity.

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 tool has one parameter, an output schema, and strong annotations, the description is complete. It explains what the tool does, what it returns, and the parameter's role. No further context is needed.

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 0%, so the description must compensate. It explains the 'dataset' parameter as a specific BEA dataset with an example ('NIPA'), giving meaning beyond the bare schema type string. However, it could have provided more detail on dataset name constraints or sources.

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 uses a specific verb ('List'), identifies the exact resource ('required and optional parameters for a specific BEA dataset'), and states the output fields. This clearly distinguishes it from sibling tools like dataset_list or parameter_values.

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 clearly states the tool operates on a specific dataset and provides an example, making it clear this is for retrieving parameter metadata. It does not explicitly mention alternatives, but the context implies when to use this over dataset_list or parameter_values.

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