unemployment

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

No annotations are provided, so the description carries full burden but offers no behavioral details. It doesn't disclose if this is a read-only fetch, requires authentication, has rate limits, returns real-time or historical data, or any error conditions. For a data-fetching tool with zero annotation coverage, this is a significant gap in transparency.

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 extremely concise with two words, front-loaded and zero waste. However, this conciseness comes at the cost of under-specification, but as per scoring rules, it earns full points for brevity and structure without redundant 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?

Given the complexity of economic data tools, no annotations, no output schema, and a parameter with 0% coverage, the description is completely inadequate. It doesn't explain what data is returned, how to interpret it, or any prerequisites, making it insufficient for an AI agent to use the tool effectively in context with its siblings.

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 input schema has one parameter ('datatype') with 0% description coverage, and the tool description adds no parameter semantics. It doesn't explain what 'datatype' means (e.g., types of unemployment data), valid values, or how it affects the fetch. With low schema coverage and no compensation in the description, this fails to provide meaningful parameter context.

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 'Fetch unemployment' restates the tool name with a generic verb, providing minimal additional context. It doesn't specify what kind of unemployment data (e.g., rate, claims, historical) or from what source, making it a tautology rather than a clear purpose statement. Compared to siblings like 'cpi' or 'gdp', it lacks differentiation in scope or data type.

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?

No guidance is provided on when to use this tool versus alternatives. With many sibling tools for economic data (e.g., 'cpi', 'gdp', 'nonfarm_payroll'), the description offers no context on whether this fetches current rates, historical trends, or specific datasets, leaving the agent with no usage criteria or exclusions.

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