Build an MCP server - Model Context Protocol

In this tutorial, we’ll build a simple MCP weather server and connect it to a host, Claude for Desktop.

What we’ll be building

We’ll build a server that exposes two tools: get_alerts and get_forecast. Then we’ll connect the server to an MCP host (in this case, Claude for Desktop):

Servers can connect to any client. We’ve chosen Claude for Desktop here for simplicity, but we also have a guide on building your own client.

Core MCP Concepts

MCP servers can provide three main types of capabilities:

Resources: File-like data that can be read by clients (like API responses or file contents)
Tools: Functions that can be called by the LLM (with user approval)
Prompts: Pre-written templates that help users accomplish specific tasks

This tutorial will primarily focus on tools.

Let’s get started with building our weather server! You can find the complete code for what we’ll be building here.

Prerequisite knowledge

This quickstart assumes you have familiarity with:

Python
LLMs like Claude

Logging in MCP Servers

When implementing MCP servers, be careful about how you handle logging:For STDIO-based servers: Never write to stdout. Writing to stdout will corrupt the JSON-RPC messages and break your server. The print() function writes to stdout by default, so keep it out of a STDIO server entirely.For HTTP-based servers: Standard output logging is fine since it doesn’t interfere with HTTP responses.

Best Practices

Use the standard library logging module, which writes to stderr.
Create one logger per module with logging.getLogger(__name__) and call it from your tools.

Quick Examples

import logging

logger = logging.getLogger(__name__)

# ❌ Bad (STDIO)
print("Processing request")

# ✅ Good (STDIO)
logger.info("Processing request")  # writes to stderr

System requirements

Python 3.10 or higher installed.
You must use the Python MCP SDK 2.0.0 or higher.

Set up your environment

First, let’s install uv and set up our Python project and environment:

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

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Make sure to restart your terminal afterwards to ensure that the uv command gets picked up.Now, let’s create and set up our project:

# Create a new directory for our project
uv init weather
cd weather

# Create virtual environment and activate it
uv venv
source .venv/bin/activate

# Install dependencies
uv add "mcp[cli]"

# Create our server file
touch weather.py

# Create a new directory for our project
uv init weather
cd weather

# Create virtual environment and activate it
uv venv
.venv\Scripts\activate

# Install dependencies
uv add mcp[cli]

# Create our server file
new-item weather.py

Now let’s dive into building your server.

Building your server

Importing packages and setting up the instance

Add these to the top of your weather.py:

from typing import Any

import httpx2
from mcp.server import MCPServer

# Initialize MCPServer
mcp = MCPServer("weather")

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

httpx2 is the HTTP client the SDK itself depends on, so installing mcp already brought it in.The MCPServer class uses Python type hints and docstrings to automatically generate tool definitions, making it easy to create and maintain MCP tools.

Helper functions

Next, let’s add our helper functions for querying and formatting the data from the National Weather Service API:

async def make_nws_request(url: str) -> dict[str, Any] | None:
    """Make a request to the NWS API with proper error handling."""
    headers = {"User-Agent": USER_AGENT, "Accept": "application/geo+json"}
    async with httpx2.AsyncClient() as client:
        try:
            response = await client.get(url, headers=headers, timeout=30.0)
            response.raise_for_status()
            return response.json()
        except Exception:
            return None


def format_alert(feature: dict) -> str:
    """Format an alert feature into a readable string."""
    props = feature["properties"]
    return f"""
Event: {props.get("event", "Unknown")}
Area: {props.get("areaDesc", "Unknown")}
Severity: {props.get("severity", "Unknown")}
Description: {props.get("description", "No description available")}
Instructions: {props.get("instruction", "No specific instructions provided")}
"""

Implementing tool execution

The tool execution handler is responsible for actually executing the logic of each tool. Let’s add it:

@mcp.tool()
async def get_alerts(state: str) -> str:
    """Get weather alerts for a US state.

    Args:
        state: Two-letter US state code (e.g. CA, NY)
    """
    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 or no alerts found."

    if not data["features"]:
        return "No active alerts for this state."

    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:
    """Get weather forecast for a location.

    Args:
        latitude: Latitude of the location
        longitude: Longitude of the location
    """
    # First get the forecast grid endpoint
    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 this location."

    # Get the forecast URL from the points response
    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 periods into a readable forecast
    periods = forecast_data["properties"]["periods"]
    forecasts = []
    for period in periods[:5]:  # Only show next 5 periods
        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 the server

Finally, let’s initialize and run the server:

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

Your server is complete! Run uv run weather.py to start the MCP server, which will listen for messages from MCP hosts.Let’s now test your server from an existing MCP host, Claude for Desktop.

Testing your server with Claude for Desktop

First, make sure you have Claude for Desktop installed. You can install the latest version here. If you already have Claude for Desktop, make sure it’s updated to the latest version.We’ll need to configure Claude for Desktop for whichever MCP servers you want to use. To do this, open your Claude for Desktop App configuration at ~/Library/Application Support/Claude/claude_desktop_config.json in a text editor. Make sure to create the file if it doesn’t exist.For example, if you have VS Code installed:

code ~/.config/Claude/claude_desktop_config.json

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

code $env:AppData\Claude\claude_desktop_config.json

You’ll then add your servers in the mcpServers key. The MCP UI elements will only show up in Claude for Desktop if at least one server is properly configured.In this case, we’ll add our single weather server like so:

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

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

You may need to put the full path to the uv executable in the command field. You can get this by running which uv on macOS/Linux or where uv on Windows.

Make sure you pass in the absolute path to your server. You can get this by running pwd on macOS/Linux or cd on Windows Command Prompt. On Windows, remember to use double backslashes (\\) or forward slashes (/) in the JSON path.

This tells Claude for Desktop:

There’s an MCP server named “weather”
To launch it by running uv --directory /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather run weather.py

Save the file, and restart Claude for Desktop.

Let’s get started with building our weather server! You can find the complete code for what we’ll be building here.

Prerequisite knowledge

This quickstart assumes you have familiarity with:

TypeScript
LLMs like Claude

Logging in MCP Servers

When implementing MCP servers, be careful about how you handle logging:For STDIO-based servers: Never use console.log(), as it writes to standard output (stdout) by default. Writing to stdout will corrupt the JSON-RPC messages and break your server.For HTTP-based servers: Standard output logging is fine since it doesn’t interfere with HTTP responses.

Best Practices

Use console.error() which writes to stderr, or use a logging library that writes to stderr or files.

Quick Examples

// ❌ Bad (STDIO)
console.log("Server started");

// ✅ Good (STDIO)
console.error("Server started"); // stderr is safe

System requirements

For TypeScript, make sure you have the latest version of Node installed.

Set up your environment

First, let’s install Node.js and npm if you haven’t already. You can download them from nodejs.org. Verify your Node.js installation:

node --version
npm --version

For this tutorial, you’ll need Node.js version 20 or higher.Now, let’s create and set up our project:

# Create a new directory for our project
mkdir weather
cd weather

# Initialize a new npm project
npm init -y

# Install dependencies
npm install @modelcontextprotocol/server zod
npm install -D @types/node typescript

# Create our files
mkdir src
touch src/index.ts

# Create a new directory for our project
md weather
cd weather

# Initialize a new npm project
npm init -y

# Install dependencies
npm install @modelcontextprotocol/server zod
npm install -D @types/node typescript

# Create our files
md src
new-item src\index.ts

Update your package.json to add type: “module” and a build script:

package.json

{
  "type": "module",
  "bin": {
    "weather": "./build/index.js"
  },
  "scripts": {
    "build": "tsc && chmod 755 build/index.js"
  },
  "files": ["build"]
}

Create a tsconfig.json in the root of your project:

tsconfig.json

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "Node16",
    "moduleResolution": "Node16",
    "types": ["node"],
    "outDir": "./build",
    "rootDir": "./src",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "forceConsistentCasingInFileNames": true
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules"]
}

Now let’s dive into building your server.

Building your server

Importing packages and setting up constants

Add these to the top of your src/index.ts:

import { McpServer } from "@modelcontextprotocol/server";
import { serveStdio } from "@modelcontextprotocol/server/stdio";
import { z } from "zod";

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

Helper functions

Next, let’s add our helper functions for querying and formatting the data from the National Weather Service API:

// Helper function for making NWS API requests
async function makeNWSRequest<T>(url: string): Promise<T | null> {
  const headers = {
    "User-Agent": USER_AGENT,
    Accept: "application/geo+json",
  };

  try {
    const response = await fetch(url, { headers });
    if (!response.ok) {
      throw new Error(`HTTP error! status: ${response.status}`);
    }
    return (await response.json()) as T;
  } catch (error) {
    console.error("Error making NWS request:", error);
    return null;
  }
}

interface AlertFeature {
  properties: {
    event?: string;
    areaDesc?: string;
    severity?: string;
    status?: string;
    headline?: string;
  };
}

// Format alert data
function formatAlert(feature: AlertFeature): string {
  const props = feature.properties;
  return [
    `Event: ${props.event || "Unknown"}`,
    `Area: ${props.areaDesc || "Unknown"}`,
    `Severity: ${props.severity || "Unknown"}`,
    `Status: ${props.status || "Unknown"}`,
    `Headline: ${props.headline || "No headline"}`,
    "---",
  ].join("\n");
}

interface ForecastPeriod {
  name?: string;
  temperature?: number;
  temperatureUnit?: string;
  windSpeed?: string;
  windDirection?: string;
  shortForecast?: string;
}

interface AlertsResponse {
  features: AlertFeature[];
}

interface PointsResponse {
  properties: {
    forecast?: string;
  };
}

interface ForecastResponse {
  properties: {
    periods: ForecastPeriod[];
  };
}

Implementing tool execution

The tool execution handler is responsible for actually executing the logic of each tool. Let’s add it, inside a createServer() function that builds the server instance and registers both tools on it:

// Build a server instance and register its tools. serveStdio calls this
// whenever it needs an instance, so every call must return a fresh one.
function createServer() {
  const server = new McpServer({
    name: "weather",
    version: "1.0.0",
  });

  server.registerTool(
    "get_alerts",
    {
      description: "Get weather alerts for a state",
      inputSchema: z.object({
        state: z
          .string()
          .length(2)
          .describe("Two-letter state code (e.g. CA, NY)"),
      }),
    },
    async ({ state }) => {
      const stateCode = state.toUpperCase();
      const alertsUrl = `${NWS_API_BASE}/alerts?area=${stateCode}`;
      const alertsData = await makeNWSRequest<AlertsResponse>(alertsUrl);

      if (!alertsData) {
        return {
          content: [
            {
              type: "text",
              text: "Failed to retrieve alerts data",
            },
          ],
        };
      }

      const features = alertsData.features || [];
      if (!features.length) {
        return {
          content: [
            {
              type: "text",
              text: `No active alerts for ${stateCode}`,
            },
          ],
        };
      }

      const formattedAlerts = features.map(formatAlert);
      const alertsText = `Active alerts for ${stateCode}:\n\n${formattedAlerts.join("\n")}`;

      return {
        content: [
          {
            type: "text",
            text: alertsText,
          },
        ],
      };
    },
  );

  server.registerTool(
    "get_forecast",
    {
      description: "Get weather forecast for a location",
      inputSchema: z.object({
        latitude: z
          .number()
          .min(-90)
          .max(90)
          .describe("Latitude of the location"),
        longitude: z
          .number()
          .min(-180)
          .max(180)
          .describe("Longitude of the location"),
      }),
    },
    async ({ latitude, longitude }) => {
      // Get grid point data
      const pointsUrl = `${NWS_API_BASE}/points/${latitude.toFixed(4)},${longitude.toFixed(4)}`;
      const pointsData = await makeNWSRequest<PointsResponse>(pointsUrl);

      if (!pointsData) {
        return {
          content: [
            {
              type: "text",
              text: `Failed to retrieve grid point data for coordinates: ${latitude}, ${longitude}. This location may not be supported by the NWS API (only US locations are supported).`,
            },
          ],
        };
      }

      const forecastUrl = pointsData.properties?.forecast;
      if (!forecastUrl) {
        return {
          content: [
            {
              type: "text",
              text: "Failed to get forecast URL from grid point data",
            },
          ],
        };
      }

      // Get forecast data
      const forecastData = await makeNWSRequest<ForecastResponse>(forecastUrl);
      if (!forecastData) {
        return {
          content: [
            {
              type: "text",
              text: "Failed to retrieve forecast data",
            },
          ],
        };
      }

      const periods = forecastData.properties?.periods || [];
      if (periods.length === 0) {
        return {
          content: [
            {
              type: "text",
              text: "No forecast periods available",
            },
          ],
        };
      }

      // Format forecast periods
      const formattedForecast = periods.map((period: ForecastPeriod) =>
        [
          `${period.name || "Unknown"}:`,
          `Temperature: ${period.temperature || "Unknown"}°${period.temperatureUnit || "F"}`,
          `Wind: ${period.windSpeed || "Unknown"} ${period.windDirection || ""}`,
          `${period.shortForecast || "No forecast available"}`,
          "---",
        ].join("\n"),
      );

      const forecastText = `Forecast for ${latitude}, ${longitude}:\n\n${formattedForecast.join("\n")}`;

      return {
        content: [
          {
            type: "text",
            text: forecastText,
          },
        ],
      };
    },
  );

  return server;
}

Running the server

Finally, serve the server over stdio:

serveStdio(createServer, {
  onerror: (error) => console.error("Weather MCP Server error:", error),
});
console.error("Weather MCP Server running on stdio");

serveStdio negotiates the protocol revision with each client, so the same server answers both 2026-07-28 clients and clients that still open with the 2025-era initialize handshake. It calls createServer() whenever it needs an instance, including once for a server/discover probe that is discarded if the client falls back to initialize, which is why the factory returns a new McpServer on every call. Wiring the server by hand with server.connect(new StdioServerTransport()) still compiles, but a server started that way only serves 2025-era clients.Make sure to run npm run build to build your server! This is a very important step in getting your server to connect.To check that the server answers 2026-07-28 clients, list its tools with the Inspector CLI in the modern protocol era:

npx @modelcontextprotocol/inspector --cli node build/index.js --protocol-era modern --method tools/list

The Inspector defaults to the legacy era, which only exercises the 2025-era handshake, so run the same command once more with --protocol-era legacy. Both runs list get_alerts and get_forecast.Let’s now test your server from an existing MCP host, Claude for Desktop.

Testing your server with Claude for Desktop

code ~/.config/Claude/claude_desktop_config.json

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

code $env:AppData\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "weather": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js"]
    }
  }
}

{
  "mcpServers": {
    "weather": {
      "command": "node",
      "args": ["C:\\PATH\\TO\\PARENT\\FOLDER\\weather\\build\\index.js"]
    }
  }
}

This tells Claude for Desktop:

There’s an MCP server named “weather”
Launch it by running node /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js

Save the file, and restart Claude for Desktop.

This is a quickstart demo based on Spring AI MCP auto-configuration and boot starters. To learn how to create sync and async MCP Servers, manually, consult the Java SDK Server documentation.

Let’s get started with building our weather server! You can find the complete code for what we’ll be building here.For more information, see the MCP Server Boot Starter reference documentation. For manual MCP Server implementation, refer to the MCP Server Java SDK documentation.

Logging in MCP Servers

When implementing MCP servers, be careful about how you handle logging:For STDIO-based servers: Never use System.out.println() or System.out.print(), as they write to standard output (stdout). Writing to stdout will corrupt the JSON-RPC messages and break your server.For HTTP-based servers: Standard output logging is fine since it doesn’t interfere with HTTP responses.

Best Practices

Use a logging library that writes to stderr or files.
Ensure any configured logging library will not write to stdout.

System requirements

Java 17 or higher installed.
Spring Boot 3.3.x or higher

Set up your environment

Use the Spring Initializer to bootstrap the project.You will need to add the following dependencies:

<dependencies>
      <dependency>
          <groupId>org.springframework.ai</groupId>
          <artifactId>spring-ai-starter-mcp-server</artifactId>
      </dependency>

      <dependency>
          <groupId>org.springframework</groupId>
          <artifactId>spring-web</artifactId>
      </dependency>
</dependencies>

dependencies {
  implementation platform("org.springframework.ai:spring-ai-starter-mcp-server")
  implementation platform("org.springframework:spring-web")
}

Then configure your application by setting the application properties:

spring.main.bannerMode=off
logging.pattern.console=

logging:
  pattern:
    console:
spring:
  main:
    banner-mode: off

The Server Configuration Properties documents all available properties.Now let’s dive into building your server.

Building your server

Weather Service

Let’s implement a WeatherService.java that uses a REST client to query the data from the National Weather Service API:

@Service
public class WeatherService {

	private final RestClient restClient;

	public WeatherService() {
		this.restClient = RestClient.builder()
			.baseUrl("https://api.weather.gov")
			.defaultHeader("Accept", "application/geo+json")
			.defaultHeader("User-Agent", "WeatherApiClient/1.0 (your@email.com)")
			.build();
	}

  @Tool(description = "Get weather forecast for a specific latitude/longitude")
  public String getWeatherForecastByLocation(
      double latitude,   // Latitude coordinate
      double longitude   // Longitude coordinate
  ) {
      // Returns detailed forecast including:
      // - Temperature and unit
      // - Wind speed and direction
      // - Detailed forecast description
  }

  @Tool(description = "Get weather alerts for a US state")
  public String getAlerts(
      @ToolParam(description = "Two-letter US state code (e.g. CA, NY)") String state
  ) {
      // Returns active alerts including:
      // - Event type
      // - Affected area
      // - Severity
      // - Description
      // - Safety instructions
  }

  // ......
}

The @Service annotation will auto-register the service in your application context. The Spring AI @Tool annotation makes it easy to create and maintain MCP tools.The auto-configuration will automatically register these tools with the MCP server.

Create your Boot Application

@SpringBootApplication
public class McpServerApplication {

	public static void main(String[] args) {
		SpringApplication.run(McpServerApplication.class, args);
	}

	@Bean
	public ToolCallbackProvider weatherTools(WeatherService weatherService) {
		return  MethodToolCallbackProvider.builder().toolObjects(weatherService).build();
	}
}

Uses the MethodToolCallbackProvider utils to convert the @Tools into actionable callbacks used by the MCP server.

Running the server

Finally, let’s build the server:

./mvnw clean install

This will generate an mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar file within the target folder.Let’s now test your server from an existing MCP host, Claude for Desktop.

Testing your server with Claude for Desktop

code ~/.config/Claude/claude_desktop_config.json

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

code $env:AppData\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "spring-ai-mcp-weather": {
      "command": "java",
      "args": [
        "-Dspring.ai.mcp.server.stdio=true",
        "-jar",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar"
      ]
    }
  }

​What we’ll be building

​Core MCP Concepts

​Prerequisite knowledge

​Logging in MCP Servers

​Best Practices

​Quick Examples

​System requirements

​Set up your environment

​Building your server

​Importing packages and setting up the instance

​Helper functions

​Implementing tool execution

​Running the server

​Testing your server with Claude for Desktop

​Prerequisite knowledge

​Logging in MCP Servers

​Best Practices

​Quick Examples

​System requirements

​Set up your environment

​Building your server

​Importing packages and setting up constants

​Helper functions

​Implementing tool execution

​Running the server

​Testing your server with Claude for Desktop

​Logging in MCP Servers

​Best Practices

​System requirements

​Set up your environment

​Building your server

​Weather Service

​Create your Boot Application

​Running the server

​Testing your server with Claude for Desktop

What we’ll be building

Core MCP Concepts

Prerequisite knowledge

Logging in MCP Servers

Best Practices

Quick Examples

System requirements

Set up your environment

Building your server

Importing packages and setting up the instance

Helper functions

Implementing tool execution

Running the server

Testing your server with Claude for Desktop

Prerequisite knowledge

Logging in MCP Servers

Best Practices

Quick Examples

System requirements

Set up your environment

Building your server

Importing packages and setting up constants

Helper functions

Implementing tool execution

Running the server

Testing your server with Claude for Desktop

Logging in MCP Servers

Best Practices

System requirements

Set up your environment

Building your server

Weather Service

Create your Boot Application

Running the server

Testing your server with Claude for Desktop