MCP Servers

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="What's the weather forecast for San Francisco this week?",
        model="anthropic/claude-opus-4-5",
        mcp_servers=["windsornguyen/open-meteo-mcp"],  # Weather forecasts via Open-Meteo
    )

    print(result.final_output)

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

import asyncio
from dedalus_labs import AsyncDedalus, DedalusRunner
from dotenv import load_dotenv

load_dotenv()

def as_bullets(items: list[str]) -> str:
    """Format items as a bulleted list."""
    return "\n".join(f"• {item}" for item in items)

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

    result = await runner.run(
        input=(
            "Get the 7-day weather forecast for San Francisco "
            "and format the daily conditions as bullets using as_bullets."
        ),
        model="anthropic/claude-opus-4-5",
        mcp_servers=["windsornguyen/open-meteo-mcp"],
        tools=[as_bullets],
    )

    print(result.final_output)

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

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="Use your tools to summarize the Dedalus Python SDK repo in 5 bullet points.",
        model="openai/gpt-5.2",
        # External MCP URL!
        mcp_servers=["https://mcp.deepwiki.com/mcp"],
    )

    print(result.final_output)

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