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 |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses the data source (open-meteo), geographic scope (Berlin, Frankfurt, Munich), time horizon (next 3 days), and threshold flags (28°C and 32°C), making behavior transparent. It doesn't mention potential latency or failure modes, but for a parameterless lookup this is adequate.
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?
A single bilingual sentence packs all essential information without redundancy, making it efficient and well-structured.
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 has no parameters, no annotations, and no output schema, the description covers the key aspects: what, where, when, and how the data is sourced. It effectively tells the user what to expect.
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?
There are no parameters, so the description doesn't need to explain any. The baseline score for zero parameters is 4, and the description's mention of the output scope is sufficient.
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 it provides a live heatwave outlook for Germany with specific cities, time range, and temperature thresholds, distinguishing it from the unrelated sibling tools.
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 use case is implied: any time a user asks for a heatwave forecast for major German cities over the next three days. It does not explicitly name alternatives, but the sibling tools are clearly unrelated, so context alone is sufficient.
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 targets a distinct calculation or data source: subsidies, BTU sizing, seal length, heating power, heatwave forecast, running cost, guide search/read, and dew point. No two tools have overlapping purposes, so an agent can easily select the right one.
All tool names use lowercase with underscores and German terms, but the semantic pattern is slightly mixed: most follow 'topic_noun' (e.g., btu_empfehlung), while two are 'object_verb' (ratgeber_lesen, ratgeber_suche). This is a minor deviation from a fully uniform convention.
Nine tools is well within the optimal 3-15 range for a niche advice server. Each tool addresses a specific aspect of HVAC/energy guidance, and the inclusion of guide search/read does not make the set feel bloated.
The server covers the core domain well: cooling and heating sizing, running costs, window sealing, ventilation, and heatwave data. It lacks dedicated tools for dehumidifier sizing or AC comparison, but these topics are accessible through the guide search/read tools, so agents can work around the gaps.