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

Annotations already indicate read-only, idempotent, non-destructive. The description adds meaningful behavioral context: seasonally adjusted series, computed month-over-month and year-over-year changes from BLS levels, and units explanation for payrolls. The example output clarifies the return shape. 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?

Front-loaded with a one-line purpose, followed by return details, usage guidance, and an example. Every section serves a clear role; no filler or redundancy. The example JSON and units note add practical value without bloat.

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?

Despite no output schema, the description fully documents return values via a concrete JSON example and explains payroll units. It covers usage context, exclusions, and data source, making it self-sufficient for a no-parameter 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?

Tool has zero parameters, and baseline is 4. The description explicitly states 'Args: none,' which is sufficient. No parameter explanations needed.

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 it provides latest U.S. labour-market data from BLS with computed headline changes, and enumerates the specific metrics returned. It distinguishes itself from siblings via the when-NOT-to-use section explicitly naming state/metro detail, industry breakdowns, and JOLTS.

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?

Explicit 'When to use' and 'When NOT to use' sections provide clear context for labour-market assessment against alternatives. It names exclusions (state/metro, industry, JOLTS) and implies alternatives like macro_gdp or bls_cpi for other macro indicators.

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