List Windy forecast models, parameters, and levels

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

With no annotations, the description takes full responsibility. It discloses that no API call is made (no quota used), which is valuable. However, it omits details like whether data is static or dynamic, or any caching behavior, though for a catalog listing this is fairly transparent.

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 two sentences, front-loaded with the main purpose, and contains zero superfluous words. Every sentence adds value.

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 low complexity (one optional parameter with full schema), no output schema required, and the description covering the purpose and usage context, it is complete enough for an agent to select and invoke the tool 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 coverage is 100% with the parameter 'category' fully described via enum and default. The description does not add extra meaning about the parameter's effect, but the baseline is 3 due to high schema coverage, and no additional info is needed.

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 explicitly states the tool returns a catalog of forecast models, parameters, and pressure levels with compatibility info. It distinguishes itself from get_point_forecast by positioning as a prerequisite, ensuring clarity.

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 clearly indicates when to use the tool ('Use before get_point_forecast to choose valid model/parameter combinations'), providing explicit context for usage in relation to sibling tools.

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