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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond those annotations: it returns a monthly series, includes the latest value, supports up to 10 years of history, and notes that unemployment data goes back to 1948. No contradiction with annotations.

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 compact and front-loaded: it opens with the core purpose, then lists measures, return shape, history depth, and use cases. Some redundancy exists with the schema's measure descriptions, but overall every sentence contributes useful information.

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 tool with an output schema and safe annotations, the description covers the data source, available measures, frequency, history limit, and historical start. Minor gaps include seasonal adjustment details and whether the 1948 start applies to all measures or only the unemployment rate, but these do not prevent correct 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?

Schema description coverage is 100%, with each parameter already documented in the schema. The description mostly restates the measure enum values and units that already appear in the schema, adding little beyond it. No defaults, formats, or examples are provided beyond what the schema already gives.

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 ('Get') and names a clear resource: U.S. national labor market data from the BLS Current Population Survey. It then enumerates four concrete measures, making the tool's scope obvious. It does not explicitly contrast with sibling tools like bls.macro.payrolls or bls.macro.cpi, but the title and measure list are sufficient for basic differentiation.

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 states explicit intended uses: 'macroeconomic research, labor market trend analysis, and policy evaluation.' This gives clear context for when to use the tool. It does not include when-not-to-use guidance or mention alternatives, but the stated use cases are specific enough to guide selection.

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