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

Read-only, idempotent, non-destructive traits are already in annotations. The description usefully adds that an optional API key in headers or query string promotes to Pro when valid, which is an auth-behavior detail not visible in the schema.

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 short sentences; the first is front-loaded with the domain and coverage, the second adds the only non-obvious operational detail. No wasted words.

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?

With a rich input schema (enums and defaults), output schema present, and annotations covering safety, the description supplies the remaining essential context: geographic scope and auth upgrade path. The absence of sibling-selection guidance is a minor gap given the overlapping forecast_manufacturing 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?

The schema has 0% coverage but the description paraphrases all five indicator enum values (unemployment, participation, EPOP, manufacturing/finance payrolls). It does not explicitly mention the timeframe parameter or its default/monthly options, though those are self-explanatory enums.

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 identifies the resource as US labor-market data and enumerates the covered series (unemployment, participation, EPOP, payrolls), which distinguishes it from sibling forecast tools by domain. It lacks an explicit verb like 'retrieves' or 'forecasts', but the noun-phrase style is unambiguous.

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?

There is no statement about when to choose this tool over siblings; e.g., employees_in_manufacturing could overlap with forecast_manufacturing. It neither gives decision criteria nor exclusions.

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