Labor Forecast

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

Annotations already establish readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds value by specifying the authentication mechanism (headers or query parameter) and the Pro tier selection behavior. However, it omits rate limits, error handling behavior, data source attribution, or cache behavior that would help an agent understand operational constraints.

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 consists of two efficient sentences with zero redundancy. The first sentence front-loads the domain and available metrics; the second sentence provides authentication details. Every word earns its place.

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 presence of an output schema, the description appropriately omits return value details. However, with 0% schema coverage and two parameters, the description should document both 'indicator' and 'timeframe' explicitly. It covers the former but misses the latter, and provides no differentiation from the numerous sibling forecast tools, leaving actionable gaps in context.

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?

With 0% schema description coverage, the description partially compensates by enumerating the five possible indicator values in prose ('unemployment, participation, EPOP...'). However, it completely omits the 'timeframe' parameter and its default value of 'monthly', leaving significant semantic gaps that the schema fails to cover.

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 specific domain (US labor market) and lists the exact indicators available (unemployment, participation, EPOP, manufacturing/finance payrolls), which distinguishes it from sibling forecast tools like forecast_gdp or forecast_commodities. While it lacks an explicit verb, the combination of the tool name 'forecast_labor', title, and enumerated indicators makes the purpose clear.

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 provides no guidance on when to use this tool versus the nine sibling forecast tools (e.g., forecast_gdp vs forecast_labor). It does not mention prerequisites, data freshness, or selection criteria. The only usage-related information is the optional API key authentication mechanism.

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