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

No annotations exist, so the description carries the burden. It discloses what data is included and its provenance, but does not describe return format, time range, update frequency, or whether data is point-in-time or current. The 'not financial advice' caveat adds some behavioral context.

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?

Two concise sentences with no filler. The core content description is front-loaded, and the usage restriction is a single clear clause at the end.

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 no-parameter tool with no output schema, the description reasonably covers what the tool returns, units, provenance, and usage limits. Minor gaps include lack of time-range or formatting details, but these are not critical for selecting and invoking a parameterless data-retrieval 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?

There are zero parameters and the schema is empty, so the baseline is 4. The description compensates by explaining exactly what data content the tool provides, which is the only semantics relevant here.

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?

Description names a specific resource: selected Treasury yields and BLS inflation, employment, and wage series, with observation dates, units, and provenance. It clearly distinguishes this macro/economic-data tool from the company-focused siblings like get_company_research and get_fundamentals.

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?

States the intended context ('For research and education only; not financial advice') and implies use when economic context is needed, but does not explicitly state when to prefer this over sibling tools or mention any exclusions.

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