For Server Developers - Model Context Protocol

In this tutorial, we’ll build a simple MCP weather server and connect it to a host, Claude for Desktop. We’ll start with a basic setup, and then progress to more complex use cases.

What we’ll be building

Many LLMs do not currently have the ability to fetch the forecast and severe weather alerts. Let’s use MCP to solve that!

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 guides on building your own client as well as a list of other clients here.

Why Claude for Desktop and not Claude.ai?

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

System requirements

Python 3.10 or higher installed.
You must use the Python MCP SDK 1.2.0 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

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]" httpx

# Create our server file
touch 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 httpx
from mcp.server.fastmcp import FastMCP

# Initialize FastMCP server
mcp = FastMCP("weather")

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

The FastMCP 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 httpx.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__":
    # Initialize and run the server
    mcp.run(transport='stdio')

Your server is complete! Run uv run weather.py to confirm that everything’s working.

Let’s now test your server from an existing MCP host, Claude for Desktop.

Testing your server with Claude for Desktop

Claude for Desktop is not yet available on Linux. Linux users can proceed to the Building a client tutorial to build an MCP client that connects to the server we just built.

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 ~/Library/Application\ Support/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:

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

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

Python
{
    "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.

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:

Python
LLMs like Claude

System requirements

Python 3.10 or higher installed.
You must use the Python MCP SDK 1.2.0 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

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]" httpx

# Create our server file
touch 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 httpx
from mcp.server.fastmcp import FastMCP

# Initialize FastMCP server
mcp = FastMCP("weather")

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

The FastMCP 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 httpx.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__":
    # Initialize and run the server
    mcp.run(transport='stdio')

Your server is complete! Run uv run weather.py to confirm that everything’s working.

Let’s now test your server from an existing MCP host, Claude for Desktop.

Testing your server with Claude for Desktop

Claude for Desktop is not yet available on Linux. Linux users can proceed to the Building a client tutorial to build an MCP client that connects to the server we just built.

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.

For example, if you have VS Code installed:

code ~/Library/Application\ Support/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:

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

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

Python
{
    "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.

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

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 16 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/sdk zod
npm install -D @types/node typescript

# Create our files
mkdir src
touch 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",
    "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 the instance

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

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

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

// Create server instance
const server = new McpServer({
  name: "weather",
  version: "1.0.0",
  capabilities: {
    resources: {},
    tools: {},
  },
});

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:

// Register weather tools
server.tool(
  "get-alerts",
  "Get weather alerts for a state",
  {
    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 === 0) {
      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.tool(
  "get-forecast",
  "Get weather forecast for a location",
  {
    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,
        },
      ],
    };
  },
);

Running the server

Finally, implement the main function to run the server:

async function main() {
  const transport = new StdioServerTransport();
  await server.connect(transport);
  console.error("Weather MCP Server running on stdio");
}

main().catch((error) => {
  console.error("Fatal error in main():", error);
  process.exit(1);
});

Make sure to run npm run build to build your server! This is a very important step in getting your server to connect.

Let’s now test your server from an existing MCP host, Claude for Desktop.

Testing your server with Claude for Desktop

Claude for Desktop is not yet available on Linux. Linux users can proceed to the Building a client tutorial to build an MCP client that connects to the server we just built.

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.

For example, if you have VS Code installed:

code ~/Library/Application\ Support/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": "node",
            "args": [
                "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js"
            ]
        }
    }
}

{
    "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.

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>
      <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=

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 ([email protected])")
			.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 with auto-register the service in your application context. The Spring AI @Tool annotation, making 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 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 a 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

Claude for Desktop is not yet available on Linux.

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.

For example, if you have VS Code installed:

code ~/Library/Application\ Support/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:

java
{
  "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"
      ]
    }
  }
}

java
{
  "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"
      ]
    }
  }
}

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

Make sure you pass in the absolute path to your server.

This tells Claude for Desktop:

There’s an MCP server named “my-weather-server”
To launch it by running java -jar /ABSOLUTE/PATH/TO/PARENT/FOLDER/mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar

Save the file, and restart Claude for Desktop.

Testing your server with Java client

Create a MCP Client manually

Use the McpClient to connect to the server:

var stdioParams = ServerParameters.builder("java")
  .args("-jar", "/ABSOLUTE/PATH/TO/PARENT/FOLDER/mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar")
  .build();

var stdioTransport = new StdioClientTransport(stdioParams);

var mcpClient = McpClient.sync(stdioTransport).build();

mcpClient.initialize();

ListToolsResult toolsList = mcpClient.listTools();

CallToolResult weather = mcpClient.callTool(
  new CallToolRequest("getWeatherForecastByLocation",
      Map.of("latitude", "47.6062", "longitude", "-122.3321")));

CallToolResult alert = mcpClient.callTool(
  new CallToolRequest("getAlerts", Map.of("state", "NY")));

mcpClient.closeGracefully();

Use MCP Client Boot Starter

Create a new boot starter application using the spring-ai-starter-mcp-client dependency:

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

and set the spring.ai.mcp.client.stdio.servers-configuration property to point to your claude_desktop_config.json. You can re-use the existing Anthropic Desktop configuration:

spring.ai.mcp.client.stdio.servers-configuration=file:PATH/TO/claude_desktop_config.json

When you start your client application, the auto-configuration will create, automatically MCP clients from the claude_desktop_config.json.

For more information, see the MCP Client Boot Starters reference documentation.

More Java MCP Server examples

The starter-webflux-server demonstrates how to create a MCP server using SSE transport. It showcases how to define and register MCP Tools, Resources, and Prompts, using the Spring Boot’s auto-configuration capabilities.

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:

Kotlin
LLMs like Claude

System requirements

Java 17 or higher installed.

Set up your environment

First, let’s install java and gradle if you haven’t already. You can download java from official Oracle JDK website. Verify your java installation:

java --version

Now, let’s create and set up your project:

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

# Initialize a new kotlin project
gradle init

After running gradle init, you will be presented with options for creating your project. Select Application as the project type, Kotlin as the programming language, and Java 17 as the Java version.

Alternatively, you can create a Kotlin application using the IntelliJ IDEA project wizard.

After creating the project, add the following dependencies:

val mcpVersion = "0.4.0"
val slf4jVersion = "2.0.9"
val ktorVersion = "3.1.1"

dependencies {
    implementation("io.modelcontextprotocol:kotlin-sdk:$mcpVersion")
    implementation("org.slf4j:slf4j-nop:$slf4jVersion")
    implementation("io.ktor:ktor-client-content-negotiation:$ktorVersion")
    implementation("io.ktor:ktor-serialization-kotlinx-json:$ktorVersion")
}

Also, add the following plugins to your build script:

plugins {
    kotlin("plugin.serialization") version "your_version_of_kotlin"
    id("com.github.johnrengelman.shadow") version "8.1.1"
}

Now let’s dive into building your server.

Building your server

Setting up the instance

Add a server initialization function:

// Main function to run the MCP server
fun `run mcp server`() {
    // Create the MCP Server instance with a basic implementation
    val server = Server(
        Implementation(
            name = "weather", // Tool name is "weather"
            version = "1.0.0" // Version of the implementation
        ),
        ServerOptions(
            capabilities = ServerCapabilities(tools = ServerCapabilities.Tools(listChanged = true))
        )
    )

    // Create a transport using standard IO for server communication
    val transport = StdioServerTransport(
        System.`in`.asInput(),
        System.out.asSink().buffered()
    )

    runBlocking {
        server.connect(transport)
        val done = Job()
        server.onClose {
            done.complete()
        }
        done.join()
    }
}

Weather API helper functions

Next, let’s add functions and data classes for querying and converting responses from the National Weather Service API:

// Extension function to fetch forecast information for given latitude and longitude
suspend fun HttpClient.getForecast(latitude: Double, longitude: Double): List<String> {
    val points = this.get("/points/$latitude,$longitude").body<Points>()
    val forecast = this.get(points.properties.forecast).body<Forecast>()
    return forecast.properties.periods.map { period ->
        """
            ${period.name}:
            Temperature: ${period.temperature} ${period.temperatureUnit}
            Wind: ${period.windSpeed} ${period.windDirection}
            Forecast: ${period.detailedForecast}
        """.trimIndent()
    }
}

// Extension function to fetch weather alerts for a given state
suspend fun HttpClient.getAlerts(state: String): List<String> {
    val alerts = this.get("/alerts/active/area/$state").body<Alert>()
    return alerts.features.map { feature ->
        """
            Event: ${feature.properties.event}
            Area: ${feature.properties.areaDesc}
            Severity: ${feature.properties.severity}
            Description: ${feature.properties.description}
            Instruction: ${feature.properties.instruction}
        """.trimIndent()
    }
}

@Serializable
data class Points(
    val properties: Properties
) {
    @Serializable
    data class Properties(val forecast: String)
}

@Serializable
data class Forecast(
    val properties: Properties
) {
    @Serializable
    data class Properties(val periods: List<Period>)

    @Serializable
    data class Period(
        val number: Int, val name: String, val startTime: String, val endTime: String,
        val isDaytime: Boolean, val temperature: Int, val temperatureUnit: String,
        val temperatureTrend: String, val probabilityOfPrecipitation: JsonObject,
        val windSpeed: String, val windDirection: String,
        val shortForecast: String, val detailedForecast: String,
    )
}

@Serializable
data class Alert(
    val features: List<Feature>
) {
    @Serializable
    data class Feature(
        val properties: Properties
    )

    @Serializable
    data class Properties(
        val event: String, val areaDesc: String, val severity: String,
        val description: String, val instruction: String?,
    )
}

Implementing tool execution

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

// Create an HTTP client with a default request configuration and JSON content negotiation
val httpClient = HttpClient {
    defaultRequest {
        url("https://api.weather.gov")
        headers {
            append("Accept", "application/geo+json")
            append("User-Agent", "WeatherApiClient/1.0")
        }
        contentType(ContentType.Application.Json)
    }
    // Install content negotiation plugin for JSON serialization/deserialization
    install(ContentNegotiation) { json(Json { ignoreUnknownKeys = true }) }
}

// Register a tool to fetch weather alerts by state
server.addTool(
    name = "get_alerts",
    description = """
        Get weather alerts for a US state. Input is Two-letter US state code (e.g. CA, NY)
    """.trimIndent(),
    inputSchema = Tool.Input(
        properties = buildJsonObject {
            putJsonObject("state") {
                put("type", "string")
                put("description", "Two-letter US state code (e.g. CA, NY)")
            }
        },
        required = listOf("state")
    )
) { request ->
    val state = request.arguments["state"]?.jsonPrimitive?.content
    if (state == null) {
        return@addTool CallToolResult(
            content = listOf(TextContent("The 'state' parameter is required."))
        )
    }

    val alerts = httpClient.getAlerts(state)

    CallToolResult(content = alerts.map { TextContent(it) })
}

// Register a tool to fetch weather forecast by latitude and longitude
server.addTool(
    name = "get_forecast",
    description = """
        Get weather forecast for a specific latitude/longitude
    """.trimIndent(),
    inputSchema = Tool.Input(
        properties = buildJsonObject {
            putJsonObject("latitude") { put("type", "number") }
            putJsonObject("longitude") { put("type", "number") }
        },
        required = listOf("latitude", "longitude")
    )
) { request ->
    val latitude = request.arguments["latitude"]?.jsonPrimitive?.doubleOrNull
    val longitude = request.arguments["longitude"]?.jsonPrimitive?.doubleOrNull
    if (latitude == null || longitude == null) {
        return@addTool CallToolResult(
            content = listOf(TextContent("The 'latitude' and 'longitude' parameters are required."))
        )
    }

    val forecast = httpClient.getForecast(latitude, longitude)

    CallToolResult(content = forecast.map { TextContent(it) })
}

Running the server

Finally, implement the main function to run the server:

fun main() = `run mcp server`()

Make sure to run ./gradlew build to build your server. This is a very important step in getting your server to connect.

Let’s now test your server from an existing MCP host, Claude for Desktop.

Testing your server with Claude for Desktop

Claude for Desktop is not yet available on Linux. Linux users can proceed to the Building a client tutorial to build an MCP client that connects to the server we just built.

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.

For example, if you have VS Code installed:

code ~/Library/Application\ Support/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": "java",
            "args": [
                "-jar",
                "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/libs/weather-0.1.0-all.jar"
            ]
        }
    }
}

This tells Claude for Desktop:

There’s an MCP server named “weather”
Launch it by running java -jar /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/libs/weather-0.1.0-all.jar

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:

C#
LLMs like Claude
.NET 8 or higher

System requirements

.NET 8 SDK or higher installed.

Set up your environment

First, let’s install dotnet if you haven’t already. You can download dotnet from official Microsoft .NET website. Verify your dotnet installation:

dotnet --version

Now, let’s create and set up your project:

# Create a new directory for our project
mkdir weather
cd weather
# Initialize a new C# project
dotnet new console

After running dotnet new console, you will be presented with a new C# project. You can open the project in your favorite IDE, such as Visual Studio or Rider. Alternatively, you can create a C# application using the Visual Studio project wizard. After creating the project, add NuGet package for the Model Context Protocol SDK and hosting:

# Add the Model Context Protocol SDK NuGet package
dotnet add package ModelContextProtocol --prerelease
# Add the .NET Hosting NuGet package
dotnet add package Microsoft.Extensions.Hosting

Now let’s dive into building your server.

Building your server

Open the Program.cs file in your project and replace its contents with the following code:

using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using ModelContextProtocol;
using System.Net.Http.Headers;

var builder = Host.CreateEmptyApplicationBuilder(settings: null);

builder.Services.AddMcpServer()
    .WithStdioServerTransport()
    .WithToolsFromAssembly();

builder.Services.AddSingleton(_ =>
{
    var client = new HttpClient() { BaseAddress = new Uri("https://api.weather.gov") };
    client.DefaultRequestHeaders.UserAgent.Add(new ProductInfoHeaderValue("weather-tool", "1.0"));
    return client;
});

var app = builder.Build();

await app.RunAsync();

When creating the ApplicationHostBuilder, ensure you use CreateEmptyApplicationBuilder instead of CreateDefaultBuilder. This ensures that the server does not write any additional messages to the console. This is only neccessary for servers using STDIO transport.

This code sets up a basic console application that uses the Model Context Protocol SDK to create an MCP server with standard I/O transport.

Weather API helper functions

Create an extension class for HttpClient which helps simplify JSON request handling:

using System.Text.Json;

internal static class HttpClientExt
{
    public static async Task<JsonDocument> ReadJsonDocumentAsync(this HttpClient client, string requestUri)
    {
        using var response = await client.GetAsync(requestUri);
        response.EnsureSuccessStatusCode();
        return await JsonDocument.ParseAsync(await response.Content.ReadAsStreamAsync());
    }
}

Next, define a class with the tool execution handlers for querying and converting responses from the National Weather Service API:

using ModelContextProtocol.Server;
using System.ComponentModel;
using System.Globalization;
using System.Text.Json;

namespace QuickstartWeatherServer.Tools;

[McpServerToolType]
public static class WeatherTools
{
    [McpServerTool, Description("Get weather alerts for a US state.")]
    public static async Task<string> GetAlerts(
        HttpClient client,
        [Description("The US state to get alerts for.")] string state)
    {
        using var jsonDocument = await client.ReadJsonDocumentAsync($"/alerts/active/area/{state}");
        var jsonElement = jsonDocument.RootElement;
        var alerts = jsonElement.GetProperty("features").EnumerateArray();

        if (!alerts.Any())
        {
            return "No active alerts for this state.";
        }

        return string.Join("\n--\n", alerts.Select(alert =>
        {
            JsonElement properties = alert.GetProperty("properties");
            return $"""
                    Event: {properties.GetProperty("event").GetString()}
                    Area: {properties.GetProperty("areaDesc").GetString()}
                    Severity: {properties.GetProperty("severity").GetString()}
                    Description: {properties.GetProperty("description").GetString()}
                    Instruction: {properties.GetProperty("instruction").GetString()}
                    """;
        }));
    }

    [McpServerTool, Description("Get weather forecast for a location.")]
    public static async Task<string> GetForecast(
        HttpClient client,
        [Description("Latitude of the location.")] double latitude,
        [Description("Longitude of the location.")] double longitude)
    {
        var pointUrl = string.Create(CultureInfo.InvariantCulture, $"/points/{latitude},{longitude}");
        using var jsonDocument = await client.ReadJsonDocumentAsync(pointUrl);
        var forecastUrl = jsonDocument.RootElement.GetProperty("properties").GetProperty("forecast").GetString()
            ?? throw new Exception($"No forecast URL provided by {client.BaseAddress}points/{latitude},{longitude}");

        using var forecastDocument = await client.ReadJsonDocumentAsync(forecastUrl);
        var periods = forecastDocument.RootElement.GetProperty("properties").GetProperty("periods").EnumerateArray();

        return string.Join("\n---\n", periods.Select(period => $"""
                {period.GetProperty("name").GetString()}
                Temperature: {period.GetProperty("temperature").GetInt32()}°F
                Wind: {period.GetProperty("windSpeed").GetString()} {period.GetProperty("windDirection").GetString()}
                Forecast: {period.GetProperty("detailedForecast").GetString()}
                """));
    }
}

Running the server

Finally, run the server using the following command:

dotnet run

This will start the server and listen for incoming requests on standard input/output.

Testing your server with Claude for Desktop

Claude for Desktop is not yet available on Linux. Linux users can proceed to the Building a client tutorial to build an MCP client that connects to the server we just built.

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 ~/Library/Application\ Support/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": "dotnet",
            "args": [
                "run",
                "--project",
                "/ABSOLUTE/PATH/TO/PROJECT",
                "--no-build"
            ]
        }
    }
}

{
    "mcpServers": {
        "weather": {
            "command": "dotnet",
            "args": [
                "run",
                "--project",
                "/ABSOLUTE/PATH/TO/PROJECT",
                "--no-build"
            ]
        }
    }
}

{
    "mcpServers": {
        "weather": {
            "command": "dotnet",
            "args": [
                "run",
                "--project",
                "C:\\ABSOLUTE\\PATH\\TO\\PROJECT",
                "--no-build"
            ]
        }
    }
}

This tells Claude for Desktop:

There’s an MCP server named “weather”
Launch it by running dotnet run /ABSOLUTE/PATH/TO/PROJECT Save the file, and restart Claude for Desktop.

Test with commands

Let’s make sure Claude for Desktop is picking up the two tools we’ve exposed in our weather server. You can do this by looking for the “Search and tools” icon:

After clicking on the slider icon, you should see two tools listed:

If your server isn’t being picked up by Claude for Desktop, proceed to the Troubleshooting section for debugging tips.

If the tool settings icon has shown up, you can now test your server by running the following commands in Claude for Desktop:

What’s the weather in Sacramento?
What are the active weather alerts in Texas?

Since this is the US National Weather service, the queries will only work for US locations.

What’s happening under the hood

When you ask a question:

The client sends your question to Claude
Claude analyzes the available tools and decides which one(s) to use
The client executes the chosen tool(s) through the MCP server
The results are sent back to Claude
Claude formulates a natural language response
The response is displayed to you!

Troubleshooting

Claude for Desktop Integration Issues

Getting logs from Claude for Desktop

Claude.app logging related to MCP is written to log files in ~/Library/Logs/Claude:

mcp.log will contain general logging about MCP connections and connection failures.
Files named mcp-server-SERVERNAME.log will contain error (stderr) logging from the named server.

You can run the following command to list recent logs and follow along with any new ones:

# Check Claude's logs for errors
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log

Server not showing up in Claude

Check your claude_desktop_config.json file syntax
Make sure the path to your project is absolute and not relative
Restart Claude for Desktop completely

Tool calls failing silently

If Claude attempts to use the tools but they fail:

Check Claude’s logs for errors
Verify your server builds and runs without errors
Try restarting Claude for Desktop

None of this is working. What do I do?

Please refer to our debugging guide for better debugging tools and more detailed guidance.

Weather API Issues

For more advanced troubleshooting, check out our guide on Debugging MCP

Next steps

Building a client

Learn how to build your own MCP client that can connect to your server

Example servers

Check out our gallery of official MCP servers and implementations

Debugging Guide

Learn how to effectively debug MCP servers and integrations

Building MCP with LLMs

Learn how to use LLMs like Claude to speed up your MCP development

Get Started

Tutorials

Concepts

Development

​What we’ll be building

​Core MCP Concepts

​Prerequisite knowledge

​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

​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

​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

​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

​Testing your server with Java client

​Create a MCP Client manually

​Use MCP Client Boot Starter

​More Java MCP Server examples

​Prerequisite knowledge

​System requirements

​Set up your environment

​Building your server

​Setting up the instance

​Weather API helper functions

​Implementing tool execution

​Running the server

​Testing your server with Claude for Desktop

​Prerequisite knowledge

​System requirements

​Set up your environment

​Building your server

​Weather API helper functions

​Running the server

​Testing your server with Claude for Desktop

​Test with commands

​What’s happening under the hood

​Troubleshooting

​Next steps

Building a client

Example servers

Debugging Guide

Building MCP with LLMs

What we’ll be building

Core MCP Concepts

Prerequisite knowledge

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

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

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

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

Testing your server with Java client

Create a MCP Client manually

Use MCP Client Boot Starter

More Java MCP Server examples

Prerequisite knowledge

System requirements

Set up your environment

Building your server

Setting up the instance

Weather API helper functions

Implementing tool execution

Running the server

Testing your server with Claude for Desktop

Prerequisite knowledge

System requirements

Set up your environment

Building your server

Weather API helper functions

Running the server

Testing your server with Claude for Desktop

Test with commands

What’s happening under the hood

Troubleshooting

Next steps