MCP Weather Server — Demo

Demo project from the YouTube video: "What is MCP? Model Context Protocol Explained (2026)"

<div align="center"> <h3>MCP Tutorial: Connect Claude to Any Tool (2026)</h3> <a href="https://www.youtube.com/watch?v=40k3SIwlFVM"> <img src="https://img.youtube.com/vi/40k3SIwlFVM/maxresdefault.jpg" alt="Watch the MCP Tutorial" style="width:100%; max-width:600px;"> </a> <p><i>Click the image to watch the MCP guide on YouTube</i></p> </div>

This is a minimal Model Context Protocol (MCP) server written in Python. It exposes two tools that an AI assistant can call:

Tool	Description
get_weather	Returns weather data for a given city
list_cities	Lists all cities with available data

---

Prerequisites

• Python 3.10 or higher
• pip

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Setup & Run

# 1. Clone or download this folder
cd demo/

# 2. (Optional) Create a virtual environment
python -m venv .venv
source .venv/bin/activate        # macOS / Linux
.venv\Scripts\activate           # Windows

# 3. Install the MCP SDK
pip install -r requirements.txt

# 4. Run the server
python weather_server.py

The server starts and listens on stdio — it's ready for an MCP host (like Claude Desktop or a custom client) to connect.

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Connect to Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "weather": {
      "command": "python",
      "args": ["/full/path/to/demo/weather_server.py"]
    }
  }
}

Restart Claude Desktop. Then ask it:

"What's the weather in Tokyo?"

Claude will automatically call the get_weather tool and return:

🌍 Weather in Tokyo:
🌡️  Temperature: 18°C
☁️  Condition:   Clear
💧 Humidity:    55%
💨 Wind:        10 km/h NE

---

Extend to a Real Weather API

Replace the WEATHER_DATA dict with a live API call:

import httpx

async def fetch_live_weather(city: str) -> dict:
    url = f"https://api.openweathermap.org/data/2.5/weather"
    params = {"q": city, "appid": "YOUR_API_KEY", "units": "metric"}
    async with httpx.AsyncClient() as client:
        resp = await client.get(url, params=params)
        data = resp.json()
        return {
            "temp": data["main"]["temp"],
            "condition": data["weather"][0]["description"].title(),
            "humidity": data["main"]["humidity"],
            "wind": f"{data['wind']['speed']} m/s"
        }

---

Project Structure

demo/
├── weather_server.py   # MCP server — all logic here
├── requirements.txt    # pip install mcp
└── README.md           # This file

---

How MCP Works (Quick Recap)

Claude Desktop (Host)
    └── MCP Client (built into host)
            └── MCP Protocol (JSON-RPC 2.0 over stdio)
                    └── weather_server.py (YOUR server)
                            └── Returns weather data

The AI model never calls your server directly — the MCP client handles discovery, schema validation, and communication. You just implement the logic.

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Next Steps

• Add more tools: get_forecast, get_air_quality
• Switch transport from stdio to HTTP + SSE for a remote server
• Publish your server to the MCP community registry

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Official MCP Resources

📖 Documentation

Resource	Link
Official Docs	https://modelcontextprotocol.io/docs
Getting Started	https://modelcontextprotocol.io/introduction
All Examples	https://modelcontextprotocol.io/examples
GitHub Organization	https://github.com/modelcontextprotocol
All Official Servers	https://github.com/modelcontextprotocol/servers

🔌 Official MCP Server Examples (from the video)

These are production-ready servers maintained by Anthropic — install and use them today:

Server	What it does	GitHub
🐙 GitHub	Browse repos, read files, manage PRs and issues via AI	https://github.com/modelcontextprotocol/servers/tree/main/src/github
🗄️ PostgreSQL	Query your database with natural language	https://github.com/modelcontextprotocol/servers/tree/main/src/postgres
📁 Filesystem	Read and write local files directly from AI	https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem
🔍 Brave Search	Real-time web search inside any AI chat	https://github.com/modelcontextprotocol/servers/tree/main/src/brave-search
💬 Slack	Read channels, summarize threads, post messages	https://github.com/modelcontextprotocol/servers/tree/main/src/slack
🧠 Memory	Persistent AI memory via a knowledge graph	https://github.com/modelcontextprotocol/servers/tree/main/src/memory

📦 SDKs

Language	Install	GitHub
Python	pip install mcp	https://github.com/modelcontextprotocol/python-sdk
TypeScript / Node.js	npm install @modelcontextprotocol/sdk	https://github.com/modelcontextprotocol/typescript-sdk