weather_warnings
Retrieve the highest official weather warning level for a specified German city using DWD data.
Instructions
Amtliche DWD-Wetterwarnungen einer Stadt (hoechste Warnstufe).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Retrieve the highest official weather warning level for a specified German city using DWD data.
Amtliche DWD-Wetterwarnungen einer Stadt (hoechste Warnstufe).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds the key behavioral detail that only the 'highest warning level' is returned, which is not covered by annotations. However, it does not describe return structure, pagination, or error 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?
The description is a single sentence, concise and front-loaded with the core purpose. However, it is in German, which may hinder an AI agent that operates primarily in English. Still, it efficiently communicates the essential function.
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 simplicity (one string parameter, no output schema, read-only), the description covers the basic purpose but omits important details: how to find the slug, what the output format looks like, and how warnings are structured. For a complete understanding, an agent would need additional context.
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% and the parameter 'slug' has no description in the schema. The tool description does not explain what 'slug' means (likely a city identifier) or how to obtain it. This is a critical gap for a single-parameter tool.
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 official DWD weather warnings for a city, specifically the highest warning level. The verb is implied ('gives'), the resource is specific ('weather warnings'), and it distinguishes from siblings like 'weather' which likely provides general weather forecasts.
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 offers no guidance on when to use this tool versus the many sibling tools (e.g., 'weather', 'flood', 'air_quality'). It does not state prerequisites, limitations, or alternative tools for different scenarios. The user must infer from the name and context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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