ecmwf_forecast

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

No annotations are provided, so the description carries full burden. It only states it provides forecasts, but lacks disclosure on data freshness, update frequency, rate limits, output structure, or coordinate validation behavior.

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?

Single sentence is efficient, no filler words. Could be slightly rephrased to include more key info without losing conciseness, but it's well-front-loaded.

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?

With 11 parameters, many enums, and no output schema, the description is insufficient. It doesn't explain return format, date ranges, weather codes, or how to interpret results, leaving significant gaps.

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%, so baseline is 3. The description adds no parameter-specific guidance beyond what the schema already describes, such as meaning of daily vs hourly or default values.

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 verb 'Get' and resource 'weather forecast', specifies the source 'European Centre for Medium-Range Weather Forecasts', and mentions 'high-quality global forecasts', which distinguishes it from sibling tools like gfs_forecast or dwd_icon_forecast.

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 explicit when-to-use or when-not-to-use guidance. The phrase 'high-quality global forecasts' implies suitability for global needs, but doesn't explicitly compare to alternatives like weather_forecast or other regional models.

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