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Quickstart

Quickstart

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):

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

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 focuses on tools, but we have intermediate tutorials if you’d like to learn more about Resources and Prompts.

Prerequisite knowledge

This quickstart assumes you have familiarity with:

  • Python
  • LLMs like Claude

System requirements

For Python, make sure you have Python 3.9 or higher installed.

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:

```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 httpx

Remove template file

rm hello.py

Create our files

mkdir -p src/weather touch src/weather/init.py touch src/weather/server.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 httpx

# Clean up boilerplate code
rm hello.py

# Create our files
md src
md src\weather
new-item src\weather\__init__.py
new-item src\weather\server.py

Add this code to pyproject.toml:

...rest of config

[build-system]
requires = [ "hatchling",]
build-backend = "hatchling.build"

[project.scripts]
weather = "weather:main"

Add this code to __init__.py:

from . import server
import asyncio

def main():
    """Main entry point for the package."""
    asyncio.run(server.main())

# Optionally expose other important items at package level
__all__ = ['main', 'server']

Now let’s dive into building your server.

Building your server

Importing packages

Add these to the top of your server.py:

from typing import Any
import asyncio
import httpx
from mcp.server.models import InitializationOptions
import mcp.types as types
from mcp.server import NotificationOptions, Server
import mcp.server.stdio

Setting up the instance

Then initialize the server instance and the base URL for the NWS API:

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

server = Server("weather")

Implementing tool listing

We need to tell clients what tools are available. The list_tools() decorator registers this handler:

@server.list_tools()
async def handle_list_tools() -> list[types.Tool]:
    """
    List available tools.
    Each tool specifies its arguments using JSON Schema validation.
    """
    return [
        types.Tool(
            name="get-alerts",
            description="Get weather alerts for a state",
            inputSchema={
                "type": "object",
                "properties": {
                    "state": {
                        "type": "string",
                        "description": "Two-letter state code (e.g. CA, NY)",
                    },
                },
                "required": ["state"],
            },
        ),
        types.Tool(
            name="get-forecast",
            description="Get weather forecast for a location",
            inputSchema={
                "type": "object",
                "properties": {
                    "latitude": {
                        "type": "number",
                        "description": "Latitude of the location",
                    },
                    "longitude": {
                        "type": "number",
                        "description": "Longitude of the location",
                    },
                },
                "required": ["latitude", "longitude"],
            },
        ),
    ]

This defines our two tools: get-alerts and get-forecast.

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(client: httpx.AsyncClient, 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"
    }

    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 concise string."""
    props = feature["properties"]
    return (
        f"Event: {props.get('event', 'Unknown')}\n"
        f"Area: {props.get('areaDesc', 'Unknown')}\n"
        f"Severity: {props.get('severity', 'Unknown')}\n"
        f"Status: {props.get('status', 'Unknown')}\n"
        f"Headline: {props.get('headline', 'No headline')}\n"
        "---"
    )

Implementing tool execution

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

@server.call_tool()
async def handle_call_tool(
    name: str, arguments: dict | None
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
    """
    Handle tool execution requests.
    Tools can fetch weather data and notify clients of changes.
    """
    if not arguments:
        raise ValueError("Missing arguments")
  
    if name == "get-alerts":
        state = arguments.get("state")
        if not state:
            raise ValueError("Missing state parameter")

        # Convert state to uppercase to ensure consistent format
        state = state.upper()
        if len(state) != 2:
            raise ValueError("State must be a two-letter code (e.g. CA, NY)")

        async with httpx.AsyncClient() as client:
            alerts_url = f"{NWS_API_BASE}/alerts?area={state}"
            alerts_data = await make_nws_request(client, alerts_url)

            if not alerts_data:
                return [types.TextContent(type="text", text="Failed to retrieve alerts data")]

            features = alerts_data.get("features", [])
            if not features:
                return [types.TextContent(type="text", text=f"No active alerts for {state}")]

            # Format each alert into a concise string
            formatted_alerts = [format_alert(feature) for feature in features[:20]] # only take the first 20 alerts
            alerts_text = f"Active alerts for {state}:\n\n" + "\n".join(formatted_alerts)

            return [
                types.TextContent(
                    type="text",
                    text=alerts_text
                )
            ]
    elif name == "get-forecast":
        try:
            latitude = float(arguments.get("latitude"))
            longitude = float(arguments.get("longitude"))
        except (TypeError, ValueError):
            return [types.TextContent(
                type="text",
                text="Invalid coordinates. Please provide valid numbers for latitude and longitude."
            )]
            
        # Basic coordinate validation
        if not (-90 <= latitude <= 90) or not (-180 <= longitude <= 180):
            return [types.TextContent(
                type="text",
                text="Invalid coordinates. Latitude must be between -90 and 90, longitude between -180 and 180."
            )]

        async with httpx.AsyncClient() as client:
            # First get the grid point
            lat_str = f"{latitude}"
            lon_str = f"{longitude}"
            points_url = f"{NWS_API_BASE}/points/{lat_str},{lon_str}"
            points_data = await make_nws_request(client, points_url)

            if not points_data:
                return [types.TextContent(type="text", text=f"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).")]

            # Extract forecast URL from the response
            properties = points_data.get("properties", {})
            forecast_url = properties.get("forecast")
            
            if not forecast_url:
                return [types.TextContent(type="text", text="Failed to get forecast URL from grid point data")]

            # Get the forecast
            forecast_data = await make_nws_request(client, forecast_url)
            
            if not forecast_data:
                return [types.TextContent(type="text", text="Failed to retrieve forecast data")]

            # Format the forecast periods
            periods = forecast_data.get("properties", {}).get("periods", [])
            if not periods:
                return [types.TextContent(type="text", text="No forecast periods available")]

            # Format each period into a concise string
            formatted_forecast = []
            for period in periods:
                forecast_text = (
                    f"{period.get('name', 'Unknown')}:\n"
                    f"Temperature: {period.get('temperature', 'Unknown')}°{period.get('temperatureUnit', 'F')}\n"
                    f"Wind: {period.get('windSpeed', 'Unknown')} {period.get('windDirection', '')}\n"
                    f"{period.get('shortForecast', 'No forecast available')}\n"
                    "---"
                )
                formatted_forecast.append(forecast_text)

            forecast_text = f"Forecast for {latitude}, {longitude}:\n\n" + "\n".join(formatted_forecast)

            return [types.TextContent(
                type="text",
                text=forecast_text
            )]
    else:
        raise ValueError(f"Unknown tool: {name}")

Running the server

Finally, implement the main function to run the server:

async def main():
    # Run the server using stdin/stdout streams
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await server.run(
            read_stream,
            write_stream,
            InitializationOptions(
                server_name="weather",
                server_version="0.1.0",
                capabilities=server.get_capabilities(
                    notification_options=NotificationOptions(),
                    experimental_capabilities={},
                ),
            ),
        )

# This is needed if you'd like to connect to a custom client
if __name__ == "__main__":
    asyncio.run(main())

Your server is complete! Run uv run src/weather/server.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.

Next, open your Claude for Desktop App configuration at ~/Library/Application Support/Claude/claude_desktop_config.json in a text editor.

For example, if you have VS Code installed:

code ~/Library/Application\ Support/Claude/claude_desktop_config.json
code $env:AppData\Claude\claude_desktop_config.json

Add this configuration (replace the parent folder path):

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

This tells Claude for Desktop:

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

Save the file, and restart Claude for Desktop.

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:

```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

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

{
  "type": "module",
  "bin": {
    "weather": "./build/index.js"
  },
  "scripts": {
    "build": "tsc && node -e \"require('fs').chmodSync('build/index.js', '755')\"",
  },
  "files": [
    "build"
  ],
}

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

{
  "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

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

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import {
  CallToolRequestSchema,
  ListToolsRequestSchema,
} from "@modelcontextprotocol/sdk/types.js";
import { z } from "zod";

Setting up the instance

Then initialize the NWS API base URL, validation schemas, and server instance:

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

// Define Zod schemas for validation
const AlertsArgumentsSchema = z.object({
  state: z.string().length(2),
});

const ForecastArgumentsSchema = z.object({
  latitude: z.number().min(-90).max(90),
  longitude: z.number().min(-180).max(180),
});

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

Implementing tool listing

We need to tell clients what tools are available. This server.setRequestHandler call will register this list for us:

// List available tools
server.setRequestHandler(ListToolsRequestSchema, async () => {
  return {
    tools: [
      {
        name: "get-alerts",
        description: "Get weather alerts for a state",
        inputSchema: {
          type: "object",
          properties: {
            state: {
              type: "string",
              description: "Two-letter state code (e.g. CA, NY)",
            },
          },
          required: ["state"],
        },
      },
      {
        name: "get-forecast",
        description: "Get weather forecast for a location",
        inputSchema: {
          type: "object",
          properties: {
            latitude: {
              type: "number",
              description: "Latitude of the location",
            },
            longitude: {
              type: "number",
              description: "Longitude of the location",
            },
          },
          required: ["latitude", "longitude"],
        },
      },
    ],
  };
});

This defines our two tools: get-alerts and get-forecast.

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:

// Handle tool execution
server.setRequestHandler(CallToolRequestSchema, async (request) => {
  const { name, arguments: args } = request.params;

  try {
    if (name === "get-alerts") {
      const { state } = AlertsArgumentsSchema.parse(args);
      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).slice(0, 20) // only take the first 20 alerts;
      const alertsText = `Active alerts for ${stateCode}:\n\n${formattedAlerts.join(
        "\n"
      )}`;

      return {
        content: [
          {
            type: "text",
            text: alertsText,
          },
        ],
      };
    } else if (name === "get-forecast") {
      const { latitude, longitude } = ForecastArgumentsSchema.parse(args);

      // 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,
          },
        ],
      };
    } else {
      throw new Error(`Unknown tool: ${name}`);
    }
  } catch (error) {
    if (error instanceof z.ZodError) {
      throw new Error(
        `Invalid arguments: ${error.errors
          .map((e) => `${e.path.join(".")}: ${e.message}`)
          .join(", ")}`
      );
    }
    throw error;
  }
});

Running the server

Finally, implement the main function to run the server:

// Start 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

Unfortunately, Claude for Desktop is not yet available on Linux. Linux users can proceed to the [Building a client](/tutorials/building-a-client) tutorial for a workaround.

First, make sure you have Claude for Desktop installed. You can install the latest version here.

Next, open your Claude for Desktop App configuration at ~/Library/Application Support/Claude/claude_desktop_config.json in a text editor.

For example, if you have VS Code installed:

```bash code ~/Library/Application\ Support/Claude/claude_desktop_config.json ``` ```powershell code $env:AppData\Claude\claude_desktop_config.json ```

Add this configuration (replace the parent folder path):

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

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.

Test with commands

First, 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:

After clicking on the hammer 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 advice.

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:

  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

**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. Server not showing up in Claude

  1. Check your configuration file syntax
  2. Make sure the path to your project is correct
  3. Restart Claude for Desktop completely

You can also check Claude’s logs for errors like so:

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

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 runs without errors
  3. Try restarting Claude for Desktop

Next steps

Learn how to build your an MCP client that can connect to your server Check out our gallery of official MCP servers and implementations Understand how MCP connects clients, servers, and LLMs