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Zack Lee
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---
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title: "Your first MCP server"
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description: "Get started with MCP by building a weather app in 5 minutes"
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---
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Connect AI to anything. MCP (Model Context Protocol) lets your AI applications seamlessly interact with data sources, tools, and services.
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<Note>
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**Real-world example:** In this quickstart, we'll build an AI-powered weather app that lets users ask about current conditions in any city. Our app will use MCP to connect to a weather API.
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</Note>
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## Installation
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<Tabs>
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<Tab title="Python">
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```bash
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pip install mcp-sdk # Using pip
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uv add mcp-sdk # Using uv (recommended)
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```
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**Requirements:**
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- Python 3.8 or higher
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- pip or uv package manager
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</Tab>
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<Tab title="TypeScript">
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```bash
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npm install @modelcontextprotocol/sdk
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```
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**Requirements:**
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- Node.js 16 or higher
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- TypeScript 4.7 or higher
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- npm or yarn package manager
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</Tab>
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</Tabs>
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## Step 1: Create an MCP Server
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Our server will expose a tool that the AI can use to fetch the current weather for any city.
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<Tabs>
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<Tab title="Python">
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```python weather_server.py
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from mcp.server import Server
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from mcp.server.stdio import stdio_server
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import aiohttp
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server = Server("weather-server")
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@server.list_tools()
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async def list_tools():
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return [{
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"name": "get_weather",
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"description": "Get current weather for a city",
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"inputSchema": {
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"type": "object",
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"properties": {
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"city": {"type": "string"}
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}
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}
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}]
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@server.call_tool()
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async def call_tool(name: str, args: dict):
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if name == "get_weather":
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async with aiohttp.ClientSession() as session:
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city = args["city"]
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url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid=YOUR_API_KEY"
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async with session.get(url) as resp:
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data = await resp.json()
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return data["weather"][0]["description"]
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async def main():
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async with stdio_server() as (read, write):
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await server.run(read, write)
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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```
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To make real API requests, sign up for a free API key at [OpenWeatherMap](https://openweathermap.org).
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</Tab>
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<Tab title="TypeScript">
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```typescript weather_server.ts
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import { Server } from "@modelcontextprotocol/sdk/server";
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import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio";
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import axios from "axios";
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const server = new Server({
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name: "weather-server",
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version: "1.0.0",
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});
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server.setRequestHandler(ListToolsRequestSchema, async () => {
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return {
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tools: [{
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name: "get_weather",
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description: "Get current weather for a city",
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inputSchema: {
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type: "object",
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properties: {
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city: { type: "string" }
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}
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}
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}]
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};
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});
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server.setRequestHandler(CallToolRequestSchema, async (request) => {
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if (request.params.name === "get_weather") {
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const city = request.params.arguments?.city as string;
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const url = `https://api.openweathermap.org/data/2.5/weather?q=${city}&appid=YOUR_API_KEY`;
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const resp = await axios.get(url);
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return {
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toolResult: resp.data.weather[0].description
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};
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}
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throw new Error("Tool not found");
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});
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async function main() {
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const transport = new StdioServerTransport();
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await server.connect(transport);
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}
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main().catch(console.error);
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```
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To make real API requests, sign up for a free API key at [OpenWeatherMap](https://openweathermap.org).
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</Tab>
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</Tabs>
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## Step 2: Integrate the Server into Your AI App
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<Note>
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This example uses a mock AI model. In a real app, you would replace this with your actual AI model code that generates a response based on the user's message and any MCP context.
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</Note>
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<Tabs>
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<Tab title="Python">
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```python app.py
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from mcp.client import ClientSession
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from mcp.client.stdio import stdio_client
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import re
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def extract_city(message):
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match = re.search(r"in\s+(\w+)", message)
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if match:
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return match.group(1)
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else:
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return None
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async def get_weather(city):
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async with stdio_client(command="python weather_server.py") as (read, write):
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async with ClientSession(read, write) as session:
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await session.initialize()
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return await session.call_tool("get_weather", {"city": city})
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async def handle_message(message):
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city = extract_city(message)
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if city:
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weather = await get_weather(city)
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return f"The current weather in {city} is: {weather}"
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else:
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return generate_response(message) # Your existing AI logic
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async def main():
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while True:
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message = input("> ")
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response = await handle_message(message)
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print(response)
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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```
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</Tab>
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<Tab title="TypeScript">
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```typescript app.ts
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import { Client } from "@modelcontextprotocol/sdk/client";
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import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio";
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import { CallToolResultSchema } from "@modelcontextprotocol/sdk/types";
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import readline from "readline";
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function extractCity(message: string): string | null {
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const match = message.match(/in\s+(\w+)/);
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if (match) {
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return match[1];
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} else {
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return null;
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}
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}
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async function getWeather(city: string): Promise<string> {
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const transport = new StdioClientTransport({
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command: "ts-node weather_server.ts",
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});
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const client = new Client({
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name: "weather-app",
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version: "1.0.0",
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});
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await client.connect(transport);
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const result = await client.request(
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{
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method: "tools/call",
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params: {
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name: "get_weather",
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arguments: { city }
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}
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},
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CallToolResultSchema
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);
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return result.toolResult as string;
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}
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async function handleMessage(message: string): Promise<string> {
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const city = extractCity(message);
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if (city) {
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const weather = await getWeather(city);
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return `The current weather in ${city} is: ${weather}`;
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} else {
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return generateResponse(message); // Your existing AI logic
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}
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}
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const rl = readline.createInterface({
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input: process.stdin,
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output: process.stdout
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});
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rl.prompt();
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rl.on("line", async (line) => {
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const response = await handleMessage(line);
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console.log(response);
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rl.prompt();
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});
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```
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</Tab>
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</Tabs>
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## Step 3: Run It!
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<Tabs>
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<Tab title="Python">
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In one terminal, start the server:
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```bash
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python weather_server.py
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```
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In another terminal, start the app:
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```bash
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python app.py
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```
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</Tab>
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<Tab title="TypeScript">
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In one terminal, start the server:
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```bash
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ts-node weather_server.ts
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```
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In another terminal, start the app:
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```bash
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ts-node app.ts
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```
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</Tab>
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</Tabs>
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Now chat with your AI assistant! Try asking about the weather in various cities.
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```
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> What's the weather like in Tokyo?
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The current weather in Tokyo is: few clouds
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> How about London?
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The current weather in London is: scattered clouds
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```
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## Why MCP Rocks
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- **Effortless integrations.** No custom code needed to connect AI to any API, database, or service.
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- **Async built-in.** MCP client/server communication is fully asynchronous out of the box.
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- **Type-safe.** Requests and responses are validated against strict schemas.
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## Next Steps
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<CardGroup>
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<Card title="📚 Learn MCP Concepts" icon="book-open" href="/concepts">
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Understand resources, tools, prompts and more
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</Card>
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<Card title="🏗️ Advanced Integrations" icon="wrench" href="/integrations">
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Databases, APIs, embeddings, vectorstores...
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</Card>
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<Card title="🌐 Build a Web App" icon="browser" href="/webapps">
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Expose your AI through an API or website
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</Card>
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<Card title="🧪 Testing and Debugging" icon="bug" href="/testing">
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Ensure your app is rock solid
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</Card>
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</CardGroup>
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## Getting Help
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<CardGroup>
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<Card title="💬 Join Our Community" icon="discord" href="https://discord.gg/modelcontextprotocol">
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Get support and see what others are building
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</Card>
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<Card title="📖 API Reference" icon="code" href="/api-reference">
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Look up library classes and functions
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</Card>
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<Card title="❓ FAQs" icon="circle-question" href="/faq">
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Quick answers to common questions
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</Card>
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<Card title="🐞 Issue Tracker" icon="github" href="https://github.com/modelcontextprotocol/python-sdk/issues">
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Report bugs or suggest improvements
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</Card>
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</CardGroup>
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