meteofrance_forecast

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

With no annotations, the description must disclose behavioral traits. It mentions models but does not describe outcomes for out-of-range coordinates, data freshness, rate limits, or the nature of the operation (read-only). Incomplete for safety assessment.

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 concise sentence with 15 words. No redundancy. Front-loaded with the core action ('Get weather forecast') and key differentiator (French models).

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 tool complexity (11 params, no output schema, many sibling tools), the description is too sparse. It lacks details on geographical scope, output format, model selection logic, and typical use cases. The mention of models helps but is insufficient.

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 the schema already documents each parameter. The description adds context about models but does not enhance understanding of individual parameters beyond what the schema provides. Baseline 3 is appropriate.

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?

Description clearly states it gets weather forecast from Météo-France models (AROME, ARPEGE). This distinguishes it from sibling tools like dwd_icon_forecast or gfs_forecast, as it is specific to French models.

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 guidance on when to use this tool vs alternatives. It hints at regional focus (France, Europe via models) but does not explicitly state that it should be used for French/European regions or how it compares to other regional forecast tools.

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