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Weather APIs return data. Users want recommendations. An agent with access to weather data can translate forecasts into actionable advice for specific situations. This example uses the Open Meteo MCP server to fetch weather data, then interprets it for a specific use case (outdoor event planning). 
import asyncio
from dedalus_labs import AsyncDedalus, DedalusRunner
from dotenv import load_dotenv

load_dotenv()

async def main():
    client = AsyncDedalus()
    runner = DedalusRunner(client)

    result = await runner.run(
        input="""I'm planning an outdoor wedding in San Francisco next weekend.
        
        Please provide:
        1. Current weather conditions
        2. 7-day forecast with daily details
        3. Precipitation probability
        4. Temperature highs and lows
        5. Wind and UV conditions
        6. Specific recommendations for outdoor event planning""",
        model="openai/gpt-4o-mini",
        mcp_servers=["joerup/open-meteo-mcp"]
    )

    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())

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Open Meteo Capabilities

The joerup/open-meteo-mcp server provides:
  • Current conditions
  • Multi-day forecasts (hourly and daily)
  • Historical weather data
  • Weather alerts
  • Global coverage (no API key required)

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Beyond Raw Data

Any API can fetch weather. The agent interprets it: wind affecting outdoor events, rain probability suggesting backup plans, UV levels for guest safety, temperature changes through the day. Same pattern applies to any data-to-advice task. Health metrics become fitness recommendations. Market data becomes investment suggestions. Sensor readings become maintenance alerts.