docs/quickstart/server.mdx

---
title: "For Server Developers"
description: "Get started building your own server to use in Claude for Desktop and other clients."
---

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 (including Claude) 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):

<Frame>
  <img src="/images/weather-alerts.png" />
</Frame>
<Frame>
  <img src="/images/current-weather.png" />
</Frame>

<Note>
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](/quickstart/client) as well as a [list of other clients here](/clients).
</Note>

<Accordion title="Why Claude for Desktop and not Claude.ai?">
  Because servers are locally run, MCP currently only supports desktop hosts. Remote hosts are in active development.
</Accordion>

### Core MCP Concepts

MCP servers can provide three main types of capabilities:

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

This tutorial will primarily focus on tools.

<Tabs>
<Tab title='Python'>

Let's get started with building our weather server! [You can find the complete code for what we'll be building here.](https://github.com/modelcontextprotocol/quickstart-resources/tree/main/weather-server-python)

### 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:

<CodeGroup>

```bash MacOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
```

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

</CodeGroup>

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:

<CodeGroup>
```bash MacOS/Linux
# 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
```

```powershell Windows
# 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] httpx

# Create our server file
new-item weather.py
```
</CodeGroup>

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`:
```python
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:

```python
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:

```python
@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:

```python
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

<Note>
Claude for Desktop is not yet available on Linux. Linux users can proceed to the [Building a client](/quickstart/client) tutorial to build an MCP client that connects to the server we just built.
</Note>

First, make sure you have Claude for Desktop installed. [You can install the latest version
here.](https://claude.ai/download) 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](https://code.visualstudio.com/) installed:

<Tabs>
<Tab title="MacOS/Linux">
```bash
code ~/Library/Application\ Support/Claude/claude_desktop_config.json
```
</Tab>
<Tab title="Windows">
```powershell
code $env:AppData\Claude\claude_desktop_config.json
```
</Tab>
</Tabs>

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:

<Tabs>
<Tab title="MacOS/Linux">
```json Python
{
    "mcpServers": {
        "weather": {
            "command": "uv",
            "args": [
                "--directory",
                "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather",
                "run",
                "weather.py"
            ]
        }
    }
}
```
</Tab>
<Tab title="Windows">
```json Python
{
    "mcpServers": {
        "weather": {
            "command": "uv",
            "args": [
                "--directory",
                "C:\\ABSOLUTE\\PATH\\TO\\PARENT\\FOLDER\\weather",
                "run",
                "weather.py"
            ]
        }
    }
}
```
</Tab>
</Tabs>

<Warning>
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.
</Warning>

<Note>
Make sure you pass in the absolute path to your server.
</Note>

This tells Claude for Desktop:
1. There's an MCP server named "weather"
2. To launch it by running `uv --directory /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather run weather`

Save the file, and restart **Claude for Desktop**.
</Tab>

<Tab title='Node'>
Let's get started with building our weather server! [You can find the complete code for what we'll be building here.](https://github.com/modelcontextprotocol/quickstart-resources/tree/main/weather-server-typescript)

### 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](https://nodejs.org/).
Verify your Node.js installation:
```bash
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:

<CodeGroup>
```bash MacOS/Linux
# 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
```

```powershell Windows
# Create a new directory for our project
md 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
md src
new-item src\index.ts
```
</CodeGroup>

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

```json 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:

```json 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`:
```typescript
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",
});
```

### Helper functions

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

```typescript
// 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:

```typescript
// 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:

```typescript
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

<Note>
Claude for Desktop is not yet available on Linux. Linux users can proceed to the [Building a client](/quickstart/client) tutorial to build an MCP client that connects to the server we just built.
</Note>

First, make sure you have Claude for Desktop installed. [You can install the latest version
here.](https://claude.ai/download) 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](https://code.visualstudio.com/) installed:

<Tabs>
<Tab title="MacOS/Linux">
```bash
code ~/Library/Application\ Support/Claude/claude_desktop_config.json
```
</Tab>
<Tab title="Windows">
```powershell
code $env:AppData\Claude\claude_desktop_config.json
```
</Tab>
</Tabs>

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:

<Tabs>
<Tab title="MacOS/Linux">
<CodeGroup>
```json Node
{
    "mcpServers": {
        "weather": {
            "command": "node",
            "args": [
                "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js"
            ]
        }
    }
}
```
</CodeGroup>
</Tab>
<Tab title="Windows">
<CodeGroup>
```json Node
{
    "mcpServers": {
        "weather": {
            "command": "node",
            "args": [
                "C:\\PATH\\TO\\PARENT\\FOLDER\\weather\\build\\index.js"
            ]
        }
    }
}
```
</CodeGroup>
</Tab>
</Tabs>

This tells Claude for Desktop:
1. There's an MCP server named "weather"
2. Launch it by running `node /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js`

Save the file, and restart **Claude for Desktop**.
</Tab>
<Tab title='Java'>

<Note>
This is a quickstart demo based on Spring AI MCP auto-configuraiton and boot starters. 
To learn how to create sync and async MCP Servers, manually, consult the [Java SDK Server](/sdk/java/mcp-server) documentation.
</Note>


Let's get started with building our weather server! 
[You can find the complete code for what we'll be building here.](https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/weather/starter-stdio-server)

For more information, see the [MCP Server Boot Starter](https://docs.spring.io/spring-ai/reference/api/mcp/mcp-server-boot-starter-docs.html) reference documentation.
For manual MCP Server implementation, refer to the [MCP Server Java SDK documentation](/sdk/java/mcp-server).

### System requirements

- Java 17 or higher installed.
- [Spring Boot 3.3.x](https://docs.spring.io/spring-boot/installing.html) or higher

### Set up your environment

Use the [Spring Initizer](https://start.spring.io/) to bootstrat the project.

You will need to add the following dependencies:

<Tabs>
  <Tab title="Maven">
  ```xml
  <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-mcp-server-spring-boot-starter</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework</groupId>
            <artifactId>spring-web</artifactId>
        </dependency>
  </dependencies>
  ```
  </Tab>
  <Tab title="Gradle">
  ```groovy
  dependencies {
    implementation platform("org.springframework.ai:spring-ai-mcp-server-spring-boot-starter")
    implementation platform("org.springframework:spring-web")   
  }
  ```
  </Tab>
</Tabs>

Then configure your application by setting the applicaiton properties:

<CodeGroup>

```bash application.properties
spring.main.bannerMode=off
logging.pattern.console=
```

```yaml application.yml
logging:
  pattern:
    console:
spring:
  main:
    banner-mode: off
```
</CodeGroup>

The [Server Configuration Properties](https://docs.spring.io/spring-ai/reference/api/mcp/mcp-server-boot-starter-docs.html#_configuration_properties) documents all available properties.

Now let's dive into building your server.

## Building your server

### Weather Service

Let's implement a [WeatheService.java](https://github.com/spring-projects/spring-ai-examples/blob/main/model-context-protocol/weather/starter-stdio-server/src/main/java/org/springframework/ai/mcp/sample/server/WeatherService.java) that uses a REST client to query the data from the National Weather Service API:

```java
@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 with auto-register the service in your applicaiton 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 Applicaiton

```java
@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 actionalble callbackes used by the MCP server.

### Running the server

Finally, let's build the server:

```bash
./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

<Note>
Claude for Desktop is not yet available on Linux.
</Note>

First, make sure you have Claude for Desktop installed. 
[You can install the latest version here.](https://claude.ai/download) 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](https://code.visualstudio.com/) installed:

<Tabs>
  <Tab title="MacOS/Linux">
  ```bash
  code ~/Library/Application\ Support/Claude/claude_desktop_config.json
  ```
  </Tab>
  <Tab title="Windows">
  ```powershell
  code $env:AppData\Claude\claude_desktop_config.json
  ```
  </Tab>
</Tabs>

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:

<Tabs>
  <Tab title="MacOS/Linux">
  ```json 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"
        ]
      }
    }
  }
  ```
  </Tab>

  <Tab title="Windows">
  ```json 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"
        ]
      }
    }
  }
  ```
  </Tab>
</Tabs>

<Note>
Make sure you pass in the absolute path to your server.
</Note>

This tells Claude for Desktop:
1. There's an MCP server named "my-weather-server"
2. 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:

```java
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 applicaiton using the `spring-ai-mcp-client-spring-boot-starter` dependency:

```xml
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-mcp-client-spring-boot-starter</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 Destop configuration:

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

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

For more information, see the [MCP Client Boot Starters](https://docs.spring.io/spring-ai/reference/api/mcp/mcp-server-boot-client-docs.html) reference documentation.

## More Java MCP Server examples

The [starter-webflux-server](https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/weather/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.

</Tab>
</Tabs>

### 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 hammer <img src="/images/claude-desktop-mcp-hammer-icon.svg" style={{display: 'inline', margin: 0, height: '1.3em'}} /> icon:

<Frame>
  <img src="/images/visual-indicator-mcp-tools.png" />
</Frame>

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

<Frame>
  <img src="/images/available-mcp-tools.png" />
</Frame>

If your server isn't being picked up by Claude for Desktop, proceed to the [Troubleshooting](#troubleshooting) section for debugging tips.

If the hammer 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?

<Frame>
  <img src="/images/current-weather.png" />
</Frame>
<Frame>
  <img src="/images/weather-alerts.png" />
</Frame>

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

## What's happening under the hood

When you ask a question:

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

## Troubleshooting

<AccordionGroup>
<Accordion title="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:
```bash
# Check Claude's logs for errors
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
```

**Server not showing up in Claude**

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

**Tool calls failing silently**

If Claude attempts to use the tools but they fail:

1. Check Claude's logs for errors
2. Verify your server builds and runs without errors
3. Try restarting Claude for Desktop

**None of this is working. What do I do?**

Please refer to our [debugging guide](/docs/tools/debugging) for better debugging tools and more detailed guidance.
</Accordion>
<Accordion title="Weather API Issues">
**Error: Failed to retrieve grid point data**

This usually means either:
1. The coordinates are outside the US
2. The NWS API is having issues
3. You're being rate limited

Fix:

- Verify you're using US coordinates
- Add a small delay between requests
- Check the NWS API status page

**Error: No active alerts for [STATE]**

This isn't an error - it just means there are no current weather alerts for that state. Try a different state or check during severe weather.
</Accordion>

</AccordionGroup>

<Note>
For more advanced troubleshooting, check out our guide on [Debugging MCP](/docs/tools/debugging)
</Note>

## Next steps

<CardGroup cols={2}>
  <Card
    title="Building a client"
    icon="outlet"
    href="/quickstart/client"
  >
    Learn how to build your own MCP client that can connect to your server
  </Card>
  <Card
    title="Example servers"
    icon="grid"
    href="/examples"
  >
    Check out our gallery of official MCP servers and implementations
  </Card>
  <Card
    title="Debugging Guide"
    icon="bug"
    href="/docs/tools/debugging"
  >
    Learn how to effectively debug MCP servers and integrations
  </Card>
  <Card
    title="Building MCP with LLMs"
    icon="comments"
    href="/building-mcp-with-llms"
  >
    Learn how to use LLMs like Claude to speed up your MCP development
  </Card>
</CardGroup>