Crop-weather stress in Brazilian producing belts

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

With no annotations provided, the description carries the transparency burden. It discloses that data is updated twice daily, includes forecast + observed data, covers 72h frost risk, and costs $0.005 via x402. It's a read tool with no destructive/state-changing implications, and the update cadence and cost disclosure add value. It doesn't describe the return format or how the 'stress' is quantified, which would further aid 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 a single, information-dense sentence that front-loads the core value (weather stress data), enumerates covered commodities, and appends practical logistics (update cadence, cost). Every clause earns its place. Slightly more could be said about output granularity, but there's no waste.

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 zero-parameter, read-only data retrieval tool with no output schema and no annotations, the description covers the essential scope: what commodities are covered, what data signals are included (rain, dry days, frost risk), temporal coverage (observed + forecast, 72h frost), and commercial terms ($0.005 via x402). It's essentially complete for the tool's simplicity; the only minor gap is specifying the geographic granularity of the belts.

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 tool has zero parameters and 100% schema coverage (empty schema), so there are no parameter semantics to add beyond the description's coverage of what data is returned. Since parameter count is 0, the baseline is 4 per the rubric — the description appropriately explains what the tool returns without needing param-level detail.

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 the tool reports crop-weather stress (rain, dry days, frost risk) for Brazilian producing belts that price global commodities, listing the specific commodities covered. It uses specific verbs ('Observed + FORECAST') and a concrete resource (commodities/belts), distinguishing it well from sibling commodity tools like commodities_fogo or commodities_briefing.

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 it covers specific commodities (coffee, soybeans, sugar, corn, orange, cocoa) which helps scope when to use it, but it doesn't explicitly say when NOT to use it or name alternative tools (e.g., commodities_hidrologia for hydrology, commodities_fogo for fire). The scoping by commodity list implicitly guides usage but lacks explicit exclusions or alternatives.

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