MCP सर्वर

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"],  # Open-Meteo के माध्यम से मौसम पूर्वानुमान
    )

    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:
    """items को बुलेट सूची के रूप में फ़ॉर्मैट करें।"""
    return "\n".join(f"• {item}" for item in items)

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

    result = await runner.run(
        input=(
            "सैन फ्रांसिस्को के लिए 7 दिनों का मौसम पूर्वानुमान प्राप्त करें "
            "और as_bullets का उपयोग करके दैनिक स्थितियों को बुलेट्स के रूप में फॉर्मेट करें।"
        ),
        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",
        # बाहरी MCP URL!
        mcp_servers=["https://mcp.deepwiki.com/mcp"],
    )

    print(result.final_output)

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