MCP Integration

{
    "mcp_url": "https://your-mcp-server.com/financial-data"
}

{
    "mcp_config": {
        "type": "mcp",
        "url": "https://your-mcp-server.com/financial-data",
        "transport": "streamable_http",
        "authorization_token": "your-mcp-server-token",
        "headers": {"X-Custom-Header": "value"},
        "timeout": 10
    }
}

{
    "mcp_configs": {
        "connections": [
            {"type": "mcp", "url": "https://your-mcp-server.com/financial-data"},
            {"type": "mcp", "url": "https://your-mcp-server.com/market-news"}
        ]
    }
}

import os
import requests
from dotenv import load_dotenv

load_dotenv()

API_KEY = os.getenv("SWARMS_API_KEY")
BASE_URL = "https://api.swarms.world"

headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}

def create_quant_agent_with_mcp():
    """Create a quantitative agent that integrates with MCP server"""
    
    payload = {
        "agent_config": {
            "agent_name": "MCP-Enabled Quantitative Analyst",
            "description": "Quantitative analyst with MCP server integration for real-time data access",
            "system_prompt": (
                "You are a Quantitative Data Analyst with direct access to MCP servers. "
                "Use the MCP tools to fetch real-time financial data, perform statistical analysis, "
                "and provide actionable insights. Always verify data quality and include source attribution."
            ),
            "model_name": "gpt-4.1",
            "role": "quantitative_analyst",
            "max_loops": 3,
            "max_tokens": 16384,
            "temperature": 0.3,
            "mcp_url": "https://your-mcp-server.com/financial-data",
            "streaming_on": False
        },
        "task": (
            "Connect to the MCP server and fetch the latest market data for AAPL, TSLA, and MSFT. "
            "Calculate volatility, Sharpe ratio, and correlation coefficients. "
            "Provide portfolio optimization recommendations based on the analysis."
        )
    }
    
    return payload

def run_mcp_agent():
    """Execute the MCP-enabled quantitative agent"""
    
    payload = create_quant_agent_with_mcp()
    
    try:
        response = requests.post(
            f"{BASE_URL}/v1/agent/completions",
            headers=headers,
            json=payload
        )
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error: {e}")
        return None

if __name__ == "__main__":
    result = run_mcp_agent()
    if result:
        print("MCP Agent executed successfully!")
        print(f"Job ID: {result.get('job_id', 'N/A')}")
    else:
        print("Failed to execute MCP agent")