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by robobobby

no_weather_forecast

Get weather forecasts for locations in Norway using MET Norway data. Specify a city or coordinates to receive hourly predictions for up to 72 hours ahead.

Instructions

Get weather forecast for a location in Norway using MET Norway (yr.no). Returns hourly data for the next N hours.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYesNorwegian city name or lat,lon coordinates
hoursNoHours ahead to forecast (default 24, max 72)

Implementation Reference

  • The handler function for the 'no_weather_forecast' tool, which fetches weather data based on the provided location and hours.
        async ({ location, hours }) => {
          try {
            const loc = await getLocation(location);
            const data = await fetchForecast(loc.lat, loc.lon);
            const ts = data.properties.timeseries;
            const maxHours = hours || 24;
            const slice = ts.slice(0, maxHours);
    
            const lines = [`## ${loc.name} — ${maxHours}h Forecast\n`];
            for (const entry of slice) {
              const t = new Date(entry.time);
              const time = t.toLocaleString("nb-NO", { timeZone: "Europe/Oslo", weekday: "short", day: "numeric", month: "short", hour: "2-digit", minute: "2-digit" });
              const inst = entry.data.instant.details;
              const symbol = entry.data.next_1_hours?.summary?.symbol_code
                || entry.data.next_6_hours?.summary?.symbol_code || "";
              const precip = entry.data.next_1_hours?.details?.precipitation_amount;
              lines.push(`**${time}:** ${inst.air_temperature}°C, ${symbolToText(symbol)}, wind ${inst.wind_speed} m/s${precip != null ? `, ${precip} mm` : ""}`);
            }
            lines.push(`\n*MET Norway Locationforecast 2.0*`);
            return { content: [{ type: "text", text: lines.join("\n") }] };
          } catch (err) {
            return { content: [{ type: "text", text: `Error: ${err.message}` }], isError: true };
          }
        }
      );
    }
  • Tool registration for 'no_weather_forecast' including name, description, and input schema.
    server.tool(
      "no_weather_forecast",
      "Get weather forecast for a location in Norway using MET Norway (yr.no). Returns hourly data for the next N hours.",
      {
        location: z.string().describe("Norwegian city name or lat,lon coordinates"),
        hours: z.number().min(1).max(72).optional().describe("Hours ahead to forecast (default 24, max 72)"),
      },
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the data source ('MET Norway (yr.no)') and that it 'Returns hourly data for the next N hours,' which adds some context. However, it lacks details on rate limits, authentication needs, error handling, or what the return format looks like (e.g., JSON structure). For a tool with no annotations, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, consisting of two sentences that efficiently convey the core functionality: getting weather forecasts for Norway with hourly data. There is no wasted language, and every sentence contributes essential information, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and basic behavior but lacks details on output format, error cases, or usage compared to siblings. Without annotations or an output schema, more context on what the return data looks like would be beneficial for an agent to use this tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with clear documentation for both parameters ('location' and 'hours'). The description adds minimal value beyond the schema, only implying that 'hours' controls the forecast duration ('the next N hours'). Since the schema already fully describes the parameters, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get weather forecast for a location in Norway using MET Norway (yr.no).' It specifies the verb ('Get'), resource ('weather forecast'), and geographic scope ('Norway'), distinguishing it from sibling tools like 'dk_weather_forecast' or 'se_weather_forecast'. However, it doesn't explicitly differentiate from 'no_current_weather' or 'no_compare_weather', which are related weather tools for Norway.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying 'for a location in Norway' and 'Returns hourly data for the next N hours,' suggesting this is for future forecasts rather than current conditions. However, it doesn't explicitly state when to use this tool versus alternatives like 'no_current_weather' or 'no_compare_weather', nor does it provide exclusions or prerequisites. The guidance is present but not comprehensive.

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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