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

Annotations declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: regional models return input errors for out-of-coverage areas (not transient failures), unknown model names are passed upstream, and large queries spill to DataCanvas with truncated: true when canvas is not enabled. This goes beyond the annotations and helps the agent anticipate failure modes and data volume issues.

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 dense but well-organized, front-loading the core purpose and member naming, then covering models, failure modes, and data volume. It is long but every sentence carries information an agent needs. The model list is repeated in the schema, which is redundant, but the description's version adds member counts and coverage details that are useful for selection.

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 (12 parameters, 19 model options, ensemble output), the description is remarkably complete. It covers model selection, coverage limitations, output naming, data volume spill behavior, and the variable requirement. The output schema exists, so return values are documented elsewhere. An agent has everything needed to call this tool correctly and interpret results.

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 100%, so the schema already documents all parameters thoroughly. The description adds value by explaining the member-suffix naming convention for output columns, clarifying the distinction between hourly and daily variable names (including the temperature_2m_max/min exception), and emphasizing the requirement for at least one variable array. It also explains the canvas_id spill behavior in more detail than the schema.

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 probabilistic ensemble weather forecasts with up to 64 members and up to 16 days ahead, and explains the member-suffix column naming. It distinguishes this from sibling tools like openmeteo_get_forecast (deterministic) by emphasizing the ensemble nature and uncertainty quantification use cases.

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 explicitly explains when to use this tool (for probabilistic forecasts, exceedance probabilities, uncertainty quantification) and provides critical guidance on model selection, including regional model coverage limitations and the instruction to pick a global model or move coordinates rather than retrying. It also notes the requirement for at least one of hourly_variables or daily_variables, and explains the DataCanvas spill behavior for large queries.

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