Practical MCP with FastMCP & LangChain

mkdir -p $HOME/workspace/mcp-client
cd $HOME/workspace/mcp-client

# Create a new uv project with Python 3.12
uv init --bare --python 3.12

# Add the MCP SDK and OpenAI client as dependencies
uv add mcp==1.26.0 openai==2.21.0

cat <<EOF > client.py
import asyncio
import json
import os
import sys
from mcp import ClientSession
from mcp.client.streamable_http import streamable_http_client
from openai import OpenAI

async def main():
    if len(sys.argv) < 2:
        sys.exit("Usage: python client.py ")

    query = sys.argv[1]
    openai = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    model = "gpt-5-mini"
    url = "http://127.0.0.1:8000/mcp"

    # Connect to the MCP server using the streamable HTTP transport
    async with streamable_http_client(url) as (read, write, _):
        # Create an MCP client session
        async with ClientSession(read, write) as session:
            # Initialize the session (handshake with the server)
            await session.initialize()

            # Discover available tools from the MCP server
            available_mcp_tools = (await session.list_tools()).tools

            # Covert MCP tools to OpenAI tool format
            tools = [
                {
                    "type": "function",
                    "function": {
                        "name": mcp_tool.name,
                        "description": mcp_tool.description,
                        "parameters": mcp_tool.inputSchema,
                    },
                }
                for mcp_tool in available_mcp_tools
            ]

            # Define the initial messages for the chat completion
            messages = [{"role": "user", "content":