weather_forecast

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

No annotations are provided, so the description fully shoulders the burden. It discloses the blend of empirical and Gumbel methods, the forced PASS signal when method_divergence > 0.15, the outlier model, coordinate alignment, and cost ($0.02). No contradictions.

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 compact yet information-rich. Each sentence adds value: model composition, threshold behavior, signal logic, coordinate alignment, city list, direction, and cost. No redundant or superfluous content.

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 tool's complexity (multi-model, probabilistic methods, signal logic) and the existence of an output schema, the description provides thorough coverage of inputs, processing, outputs (including signal, outlier, probabilities), and cost. It is complete for an agent to use correctly.

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?

Schema description coverage is 0%, so the description must add meaning. It enumerates cities, explains threshold usage (when given, returns probabilities and method_divergence), and clarifies direction as 'greater' or 'less'. However, it does not specify the date format or provide examples for all parameters.

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 provides weather probability using a 7-model consensus and 80 ensemble members. It lists specific cities, threshold, direction, and return fields (signal, probability, outlier). This distinguishes it from all sibling tools, which are non-weather.

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 implies usage for precise weather probability aligned to NWS ASOS stations used by Kalshi/Polymarket, suggesting use in prediction markets. However, it does not explicitly state when not to use it or provide alternative tools. Given siblings are unrelated, this is not a major omission.

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