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?
Even though no annotations exist, the description discloses the key behavioral aspects: the time span (3 days), locations (3 cities), data source (open-meteo), and warning thresholds (28°C and 32°C). It doesn't mention that it's read-only, but the concept of an outlook implies it only returns data; this is adequate for a no-parameter tool.
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 bilingual sentence, about 25 words, that covers all the necessary facts without redundancy. It front-loads the purpose and adds precision.
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?
The tool is simple (no params, no output schema) and the description covers the essential information: what it computes and for which locations. It could be clearer on how the output is formatted (e.g., a list of temperatures), but this is a minor gap for such a straightforward tool.
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?
With zero parameters, the schema is fully self-contained. The description adds value by explaining what the tool provides, which contextualizes the expected input (none).
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 explicitly states the tool's function: providing a live heatwave outlook for Germany over the next three days, with specific thresholds and cities. This is clearly distinct from sibling tools about building energy efficiency, so an agent can select it unambiguously.
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 context is clear: use this tool to retrieve a heatwave forecast for Berlin, Frankfurt, and Munich. However, unlike the get_calls example, it does not name alternative tools or specify when not to use it, so it lacks explicit usage boundaries.
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 indoor-climate or energy question: subsidy eligibility, cooling load, window sealing, device selection, heating load, heatwave outlook, running costs, guide retrieval, and dew-point ventilation. Only geraet_wahl vs btu_empfehlung could be muddled by an agent, but their descriptions explicitly layer one above the other.
Tool names follow a mostly predictable lowercase underscore pattern with German domain nouns, e.g. btu_empfehlung, heizleistung_watt, taupunkt_lueften. The two ratgeber tools are verb-like (ratgeber_suche, ratgeber_lesen) rather than noun phrases, which is a minor deviation but still readable.
Ten tools is well within the ideal range and each tool pulls its weight for a specialized climate-advice server. The set covers calculations, forecasts, content search, and content retrieval without padding.
The domain is well covered: sizing for cooling and heating, device selection, running costs, window sealing, ventilation/dew point, heatwave info, subsidies, and citable guides. A live current-weather or humidity-observation tool would round out the 'weather' side, but agents can work around that gap.