[MCP] 서버와 클라이언트 구현하기

from mcp.server.fastmcp import FastMCP

# Initialize FastMCP server with configuration
mcp = FastMCP(
    "Weather",  # Name of the MCP server
    instructions="You are a weather assistant that can answer questions about the weather in a given location.",  # Instructions for the LLM on how to use this tool
    host="0.0.0.0",  # Host address (0.0.0.0 allows connections from any IP)
    port=8005,  # Port number for the server
)


@mcp.tool()
async def get_weather(location: str) -> str:
    """
    Get current weather information for the specified location.

    This function simulates a weather service by returning a fixed response.
    In a production environment, this would connect to a real weather API.

    Args:
        location (str): The name of the location (city, region, etc.) to get weather for

    Returns:
        str: A string containing the weather information for the specified location
    """
    # Return a mock weather response
    # In a real implementation, this would call a weather API
    return f"It's always Sunny in {location}"


if __name__ == "__main__":
    # Start the MCP server with stdio transport
    # stdio transport allows the server to communicate with clients
    # through standard input/output streams, making it suitable for
    # local development and testing
    mcp.run(transport="stdio")

# math_server.py
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Math")

@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

@mcp.tool()
def multiply(a: int, b: int) -> int:
    """Multiply two numbers"""
    return a * b

if __name__ == "__main__":
    mcp.run(transport="stdio")

# Create server parameters for stdio connection
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

from langchain_mcp_adapters.tools import load_mcp_tools
from langgraph.prebuilt import create_react_agent

from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o")

server_params = StdioServerParameters(
    command="python",
    # Make sure to update to the full absolute path to your math_server.py file
    args=["/path/to/math_server.py"],
)

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        # Initialize the connection
        await session.initialize()

        # Get tools
        tools = await load_mcp_tools(session)

        # Create and run the agent
        agent = create_react_agent(model, tools)
        agent_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o")

async with MultiServerMCPClient(
    {
        "math": {
            "command": "python",
            # Make sure to update to the full absolute path to your math_server.py file
            "args": ["/path/to/math_server.py"],
            "transport": "stdio",
        },
        "weather": {
            # make sure you start your weather server on port 8000
            "url": "http://localhost:8005/sse",
            "transport": "sse",
        }
    }
) as client:
    agent = create_react_agent(model, client.get_tools())
    math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
    weather_response = await agent.ainvoke({"messages": "what is the weather in seoul?"})