Live-Hitzevorschau für Deutschland (nächste 3 Tage). — Live heatwave outlook for Germany: highest temperature over the next three days across Berlin, Frankfurt and Munich (open-meteo), flagged from 28 °C and 32 °C.
| Name | Required | Description | Default |
|---|---|---|---|
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the data source (open-meteo), the cities, and threshold flags, but does not explain what 'flagged' means in terms of output or any limits. This is partial transparency.
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 concise sentences, with the German and English versions side by side, and every detail (time range, cities, data source, thresholds) is relevant. No fluff.
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?
For a tool with no parameters and no output schema, the description is fairly complete: it states the purpose, scope, data source, and thresholds. However, it could clarify what the output format looks like and what 'flagged' specifically means.
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?
The tool has zero parameters, so the baseline is 4. The description adds value by specifying the scope (Germany, three cities) and the data source, which provides context beyond the empty 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 a live heatwave outlook for Germany, specifying the metric (highest temperature), the time range (next three days), and the cities covered (Berlin, Frankfurt, Munich), which distinguishes it from the sibling tools that focus on home energy advice.
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 implicitly defines when to use the tool—when a heatwave forecast for major German cities is needed—but does not explicitly mention alternatives or exclusions. The context is clear, though no direct comparison to sibling tools is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool addresses a distinct aspect of room climate: cooling capacity, heating power, window sealing, heatwave forecast, running costs, dew point ventilation, and guide search/read. No two tools have overlapping purposes.
All names follow a lowercase snake_case pattern and use German terms, but the structure is not uniform: most are noun compounds (btu_empfehlung, heizleistung_watt), while two end in verbs (ratgeber_lesen, taupunkt_lueften) and one uses a noun 'suche' instead of 'suchen'. This is a minor deviation from a strict verb_noun pattern.
Eight tools is well within the ideal 3-15 range and each tool contributes a clear function to the server's purpose. The count feels neither sparse nor bloated.
The tool set covers the core domain of room climate calculations and practical advice: cooling, heating, sealing, costs, weather, and ventilation. Minor gaps exist (e.g., no dedicated dehumidifier sizing tool), but the guide search can likely fill those niches.