How to Deploy an MCP Server Using Python: A Step-by-Step Guide for Beginners

What is MCP?

Why Use Python for Your MCP Server?

• Simplicity & readability: Python's syntax is clean & easy to learn, making it great for beginners.
• Rich libraries: With a plethora of libraries available, Python provides tools that help in implementing complex functionalities without reinventing the wheel.
• Community support: You'll find a massive community around Python willing to help you troubleshoot problems along the way.

Prerequisites for This Tutorial

• Python 3.10 or higher installed on your machine. If you haven't installed Python yet, you can download it from python.org.
• Familiarity with basic programming concepts; however, don’t worry, I’ll guide you through the main processes.
• A basic understanding of how to navigate your terminal (Command Prompt on Windows, or Terminal on macOS/Linux).

Step 1: Set Up Your Environment

Install

1 uv

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curl -LsSf https://astral.sh/uv/install.sh | sh

Create Your Project

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uv init mcp-server-demo
cd mcp-server-demo

Set Up Virtual Environment

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uv venv
source .venv/bin/activate  # For macOS/Linux
.virtualinat activate  # For Windows

Install Required Dependencies

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uv add "mcp[cli]" httpx

Step 2: Write Your Server Code

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touch server.py

Import Required Packages

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import httpx
from mcp.server.fastmcp import FastMCP

Initialize the MCP Server

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mcp = FastMCP("weather")  # Give your server a name

Define Basic Server Functionality

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NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"

async def make_nws_request(url: str) -> dict:  # Define a method for HTTP requests  
  headers = { 
      "User-Agent": USER_AGENT,  
      "Accept": "application/geo+json",  
  }  

  async with httpx.AsyncClient() as client:  
      response = await client.get(url, headers=headers, timeout=30.0)   
      response.raise_for_status()  # Ensure the response is valid  
      return response.json()

Adding MCP Tasks

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@mcp.tool()
async def get_alerts(state: str) -> str:
    url = f"{NWS_API_BASE}/alerts/active/area/{state}"
    data = await make_nws_request(url)

    if not data or "features" not in data:
        return "Unable to fetch alerts, no alerts found."

    alerts = [format_alert(feature) for feature in data["features"]]
    return "\n---\n".join(alerts)

@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
    points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
    points_data = await make_nws_request(points_url)

    if not points_data:
        return "Unable to fetch forecast data for the location."

    forecast_url = points_data["properties"]["forecast"]
    forecast_data = await make_nws_request(forecast_url)

    if not forecast_data:
        return "Unable to fetch detailed forecast."

    # Format the forecast response
    forecasts = []
    for period in forecast_data["properties"]["periods"][:5]:
        forecast = f"{period['name']}: Temperature: {period['temperature']}°{period['temperatureUnit']} Wind: {period['windSpeed']} {period['windDirection']} Forecast: {period['detailedForecast']}"
        forecasts.append(forecast)

    return "\n---\n".join(forecasts)

Running Your Server

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

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uv run server.py

Step 3: Integrate With Claude Desktop

Configure Claude Desktop

• MacOS:
  1 ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: `%APPDATA%

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{
  "mcpServers": {
    "weather": {
      "command": "uv",
      "args": ["--directory", "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather", "run", "server.py"]
    }
  }
}

Why Use Arsturn?

Why Arsturn?

• Easy-to-Use Interface: Arsturn’s platform is incredibly user-friendly, allowing you to build chatbots without any prior coding knowledge.
• Tailored Experiences: You can design chatbots that reflect your unique brand identity and cater specifically to your audience's needs.
• Insightful Analytics: Get valuable insights into audience engagement & behavior, allowing you to refine your strategies effortlessly.

How Arsturn Works

1. Design Your Chatbot: Customize your chatbot to align with your business goals.
2. Train With Your Data: Use your existing data to make the chatbot relevant and useful.
3. Engage Your Audience: Deploy the chatbot across your digital channels, and start engaging with your audience in real-time.

Conclusion