Documentation Index
Fetch the complete documentation index at: https://docs.dedaluslabs.ai/llms.txt
Use this file to discover all available pages before exploring further.
Weather APIs return data. Users want recommendations. An agent with access to weather data can translate forecasts into actionable advice for specific situations.
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-4.1",
mcp_servers=["windsor/open-meteo-mcp"]
)
print(result.final_output)
if __name__ == "__main__":
asyncio.run(main())
Open Meteo Capabilities
The windsor/open-meteo-mcp server provides:
- Current conditions
- Multi-day forecasts (hourly and daily)
- Historical weather data
- Weather alerts
- Global coverage (no API key required)
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.