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by robobobby

fi_compare_weather

Compare current weather conditions between two Finnish locations side by side for planning and analysis.

Instructions

Compare current weather between two Finnish locations side by side.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
location1YesFirst location (city name or coordinates)
location2YesSecond location

Implementation Reference

  • Registration and handler implementation for the 'fi_compare_weather' MCP tool. It fetches weather data for two locations using 'openMeteoFetch' and returns a side-by-side comparison.
    server.tool(
      "fi_compare_weather",
      "Compare current weather between two Finnish locations side by side.",
      {
        location1: z.string().describe("First location (city name or coordinates)"),
        location2: z.string().describe("Second location"),
      },
      async ({ location1, location2 }) => {
        try {
          const [loc1, loc2] = await Promise.all([getLocation(location1), getLocation(location2)]);
          const [data1, data2] = await Promise.all([
            openMeteoFetch({
              latitude: loc1.lat, longitude: loc1.lon,
              current: "temperature_2m,apparent_temperature,precipitation,snowfall,weather_code,wind_speed_10m,cloud_cover",
            }),
            openMeteoFetch({
              latitude: loc2.lat, longitude: loc2.lon,
              current: "temperature_2m,apparent_temperature,precipitation,snowfall,weather_code,wind_speed_10m,cloud_cover",
            }),
          ]);
          const c1 = data1.current, c2 = data2.current;
          const wx1 = WMO_CODES[c1.weather_code] || "", wx2 = WMO_CODES[c2.weather_code] || "";
          const lines = [
            `## Weather Comparison\n`,
            `| | ${loc1.name} | ${loc2.name} |`,
            `|---|---|---|`,
            `| **Conditions** | ${wx1} | ${wx2} |`,
            `| **Temperature** | ${c1.temperature_2m}°C | ${c2.temperature_2m}°C |`,
            `| **Feels like** | ${c1.apparent_temperature}°C | ${c2.apparent_temperature}°C |`,
            `| **Wind** | ${c1.wind_speed_10m} km/h | ${c2.wind_speed_10m} km/h |`,
            `| **Cloud cover** | ${c1.cloud_cover}% | ${c2.cloud_cover}% |`,
            `| **Precipitation** | ${c1.precipitation} mm | ${c2.precipitation} mm |`,
            c1.snowfall > 0 || c2.snowfall > 0 ? `| **Snowfall** | ${c1.snowfall} cm | ${c2.snowfall} cm |` : null,
            `\n*Open-Meteo*`,
          ].filter(Boolean);
          return { content: [{ type: "text", text: lines.join("\n") }] };
        } catch (err) {
          return { content: [{ type: "text", text: `Error: ${err.message}` }], isError: true };
        }
      }
    );
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'current weather' but doesn't specify what data is returned (e.g., temperature, humidity), format, or any limitations like rate limits or authentication needs. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('compare current weather', 'two Finnish locations', 'side by side') contributes directly to understanding the tool's function.

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 basic purpose but lacks details on output format, error handling, or sibling differentiation. Without annotations or output schema, more behavioral context would improve completeness.

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 description adds minimal value beyond the input schema, which already has 100% coverage with clear parameter descriptions. It implies both parameters are locations but doesn't elaborate on valid formats (e.g., 'Helsinki' vs coordinates) or constraints. Baseline 3 is appropriate since the schema does most of the work.

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 action ('compare current weather') and resource ('between two Finnish locations'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'fi_current_weather' or 'dk_compare_weather', which would require mentioning the side-by-side comparison aspect as unique.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'fi_current_weather' (for single location) or 'fi_weather_forecast' (for future predictions). It also lacks context about prerequisites or limitations, such as whether both locations must be in Finland.

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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