Mastering US Weather Intelligence: A Practical Guide to Weather MCP Server

In today’s world where weather patterns are becoming increasingly unpredictable, having access to reliable, real-time weather information isn’t just convenient—it’s essential for safety and planning. Whether you’re planning a weekend hike in Colorado, managing agricultural operations in Iowa, or developing applications that require accurate weather data, knowing how to access authoritative weather information makes all the difference.

This guide introduces you to Weather MCP Server, a powerful yet straightforward tool that connects you directly to the National Weather Service’s official data. Unlike commercial weather services with their limitations and costs, this open-source solution puts accurate, government-provided weather information at your fingertips—completely free of charge.

Understanding Weather MCP Server: Your Direct Line to Official Weather Data

Weather MCP Server is a specialized application built on the Model Context Protocol (MCP) framework that serves as a bridge between users and the National Weather Service (NWS) API. Think of it as a translator that takes the complex language of official weather data and converts it into information you can easily understand and use.

The National Weather Service, part of the National Oceanic and Atmospheric Administration (NOAA), is the United States’ official source for weather forecasts, warnings, and advisories. When you use Weather MCP Server, you’re accessing the same data that powers official government weather alerts and forecasts—without the technical hurdles of working directly with the NWS API.

What Makes Weather MCP Server Different?

Many weather applications and services exist, but Weather MCP Server stands apart for several key reasons:

  • Direct from the source: It pulls data straight from the National Weather Service, eliminating intermediaries
  • No cost barriers: Unlike many commercial weather APIs with usage limits or subscription fees
  • Open and transparent: As an open-source project, you can examine and verify how it works
  • Precision-focused: Designed specifically for US locations with accurate state and coordinate-based queries

This isn’t another flashy weather app with animations and social features. Weather MCP Server is a no-nonsense tool for people who need reliable weather data they can trust and integrate into their workflows.

Core Features That Deliver Real Value

Weather MCP Server offers four essential capabilities that address specific weather information needs. Let’s explore each one in detail.

1. Real-Time Weather Alerts by State

The get_alerts function provides immediate access to active weather alerts for any US state. Simply specify the two-letter state code (like “CA” for California or “NY” for New York), and the server retrieves all current weather warnings and advisories for that state.

These alerts come directly from the National Weather Service and include critical information about:

  • Severe thunderstorms
  • Flood warnings
  • Fire weather conditions
  • Winter storm alerts
  • Tornado watches and warnings
  • And other official weather emergencies

Unlike consumer weather apps that might prioritize dramatic presentation over substance, Weather MCP Server delivers the factual information you need without sensationalism. When weather threatens safety, clarity matters more than flashy graphics.

2. Precision Weather Forecasts by Coordinates

The get_forecast function gives you detailed weather predictions for any specific location within the United States. By providing latitude and longitude coordinates (like 37.7749, -122.4194 for San Francisco), you receive a comprehensive forecast covering up to five time periods.

This level of precision is invaluable when you need weather information for a specific location rather than a general city area. The forecast includes:

  • Temperature trends
  • Precipitation probabilities
  • Wind conditions
  • Humidity levels
  • And other essential weather metrics

For applications requiring location-specific weather data—such as agricultural planning, outdoor event coordination, or transportation logistics—this feature provides the accuracy that generic city-level forecasts cannot match.

3. Health Check: Ensuring Reliability

The health_check function serves as a simple verification tool to confirm that the Weather MCP Server is operating correctly. This might seem basic, but in production environments where weather data informs critical decisions, knowing your data source is functioning properly is essential.

When integrated into larger systems, this health check can trigger automated responses if the server becomes unavailable, ensuring your applications can handle weather data interruptions gracefully.

4. Root Tool: Discoverability Made Simple

The root function provides essential information about the server itself and lists all available tools. This feature supports discoverability—helping new users understand what the server can do without needing extensive documentation.

For developers integrating Weather MCP Server into their applications, this self-describing capability simplifies the development process and reduces the learning curve.

Technical Requirements: Setting Realistic Expectations

Before diving into installation, let’s clarify what you’ll need to run Weather MCP Server successfully. Being upfront about requirements helps avoid frustration later.

Essential Components

Weather MCP Server requires two fundamental components:

  1. Python 3.13 or newer: The programming language that powers the server
  2. uv tool: A fast dependency manager that streamlines installation

These requirements ensure the server runs efficiently and integrates smoothly with modern development environments. While Python is widely used, the specific version requirement (3.13+) reflects the project’s use of the latest language features for optimal performance.

Why These Specific Requirements?

You might wonder why the project specifies Python 3.13+ rather than an older, more widely available version. This choice reflects a commitment to leveraging the latest improvements in the Python ecosystem:

  • Enhanced performance and memory management
  • New language features that simplify development
  • Better security practices incorporated in recent releases

Similarly, the recommendation to use uv (rather than standard pip) addresses a common pain point in Python development—slow dependency installation. uv significantly accelerates this process, making setup more efficient.

Step-by-Step Installation Guide

Let’s walk through the installation process with clear explanations at each stage. Understanding what’s happening during installation helps troubleshoot issues and builds confidence in the process.

Step 1: Obtaining the Source Code

The first step involves downloading the Weather MCP Server code from its GitHub repository. Open your terminal or command prompt and enter:

git clone https://github.com/rajendrakumaryadav/weather-mcp-server.git
cd weather-mcp-server

This creates a local copy of the project on your computer. The git clone command downloads all necessary files, while cd weather-mcp-server navigates into the newly created directory.

Note: If you don’t have Git installed, you can download the repository as a ZIP file from GitHub and extract it to your preferred location.

Step 2: Installing Dependencies

With the code in place, the next step installs required libraries. Weather MCP Server relies on three core dependencies:

  • fastmcp (version 2.11.0 or newer): The framework that implements the Model Context Protocol
  • httpx (version 0.28.1 or newer): A modern HTTP client for Python
  • mcp[cli] (version 1.12.3 or newer): Command-line interface components for MCP

You have two recommended methods for installing these dependencies:

Method A: Using requirements.txt

uv pip install -r requirements.txt

This approach installs exactly the dependencies specified in the requirements file, ensuring compatibility.

Method B: Using pyproject.toml

uv pip install -r <(uv pip compile pyproject.toml)

This method compiles dependencies from the project configuration file, providing flexibility while maintaining version constraints.

Weather MCP Server Usage Guide

Figure 1: Visual guide showing how to configure and use the Weather MCP Server in various development environments

Configuration for Different Development Environments

Weather MCP Server accommodates various development workflows. Let’s explore setup procedures for popular environments.

VS Code Configuration

For Visual Studio Code users, the project includes a pre-configured .vscode/mcp.json file. This file contains all necessary settings to integrate Weather MCP Server with VS Code’s MCP extension.

Simply open the project folder in VS Code, and the environment should recognize the MCP configuration automatically. You can then start the server directly from the command palette without additional setup.

PyCharm Configuration

PyCharm users benefit from a dedicated setup process. The configuration process involves:

  1. Opening the project in PyCharm
  2. Navigating to Run/Debug Configurations
  3. Adding a new Python configuration
  4. Specifying the main.py file as the script path
PyCharm MCP Server Setup

Figure 2: Step-by-step visual guide for configuring Weather MCP Server in PyCharm

This integration allows PyCharm users to leverage their familiar development environment while accessing weather data services.

MCP Server Configuration Example

Figure 3: Example of MCP server configuration showing the interaction flow between client and server

Practical Usage Scenarios

Now that installation and configuration are complete, let’s explore how to actually use Weather MCP Server in real situations.

Starting the Server

After installation, launch the server with this simple command:

uv run main.py

This command starts the MCP server, making its tools available for use. The server will remain active until you stop it (typically by pressing Ctrl+C in the terminal).

Using the Weather Alert Tool

To retrieve active weather alerts for a specific state, use the get_alerts function with a two-letter state code:

get_alerts("CA")

This command returns all active weather alerts for California. You might receive information about:

  • Red flag warnings for fire weather conditions
  • Flash flood watches
  • High wind alerts
  • Or other relevant weather emergencies

The output presents this information in a clear, structured format that’s easy to read and process.

Accessing Detailed Weather Forecasts

For location-specific forecasts, use the get_forecast function with precise coordinates:

get_forecast(37.7749, -122.4194)

This example retrieves the forecast for San Francisco (latitude 37.7749, longitude -122.4194). The response includes up to five forecast periods with detailed weather information for each.

Verifying Server Status

Before relying on weather data, confirm the server is functioning properly:

health_check()

A successful check returns a simple confirmation message, ensuring your connection to the weather data source is active.

Discovering Available Tools

New users can explore available functionality with:

root()

This command displays server information and lists all accessible tools, serving as a built-in reference guide.

Project Structure Explained

Understanding the project organization helps when working with or extending Weather MCP Server. The structure follows logical conventions that make maintenance and development more straightforward.

Key Components

  • main.py: The heart of the application, containing server implementation and tool definitions
  • pyproject.toml: Project metadata and dependency specifications in modern Python format
  • .vscode/mcp.json: Pre-configured settings for Visual Studio Code integration

This clean organization reflects thoughtful design—each component has a clear purpose without unnecessary complexity. Developers can quickly locate relevant files, and new contributors can understand the project structure with minimal orientation.

API Reference: Understanding the Technical Interface

Let’s examine each tool’s technical specifications to ensure proper usage.

get_alerts(state: str) → str

Function: Retrieves formatted weather alerts for the specified US state code
Parameters:

  • state: Two-letter US state abbreviation (e.g., “TX” for Texas, “FL” for Florida)
    Return: Formatted string containing active weather alerts for the state

This function connects to the National Weather Service API to fetch current alerts. The response includes all active warnings and advisories for the requested state, presented in a human-readable format.

get_forecast(latitude: float, longitude: float) → str

Function: Provides detailed weather forecast for specific coordinates
Parameters:

  • latitude: Decimal degree latitude (positive for north, negative for south)
  • longitude: Decimal degree longitude (positive for east, negative for west)
    Return: Formatted forecast string covering up to five time periods

The forecast includes temperature, precipitation probability, wind conditions, and other relevant weather metrics for the specified location.

health_check() → str

Function: Verifies server operational status
Parameters: None
Return: Confirmation message indicating server is running

This simple check ensures the server is active and ready to process requests, crucial for automated systems that depend on weather data.

root() → str

Function: Provides server description and tool listing
Parameters: None
Return: Information about the server and available tools

This self-describing capability supports discoverability, helping users understand available functionality without external documentation.

Why This Matters for Real-World Applications

Weather MCP Server isn’t just another technical toy—it solves genuine problems for various users.

For Developers

When building applications that require weather data, Weather MCP Server eliminates the complexity of directly interfacing with the NWS API. Instead of wrestling with authentication, rate limits, and response parsing, developers get a simplified interface that handles these details behind the scenes.

The clean tool structure (get_alerts, get_forecast, etc.) maps directly to common weather information needs, reducing development time and maintenance overhead.

For Data Analysts

Weather patterns influence countless business metrics. With precise location-based forecasts, analysts can:

  • Correlate weather conditions with sales patterns
  • Adjust inventory forecasts based on seasonal weather trends
  • Model climate impacts on operational efficiency

The direct connection to National Weather Service data ensures analytical models use authoritative information rather than potentially biased commercial sources.

For Educational Purposes

Weather MCP Server serves as an excellent teaching tool for:

  • Learning about API integration
  • Understanding weather data structures
  • Practicing Python development with real-world applications

Its straightforward design makes it accessible for students while demonstrating professional development practices.

Frequently Asked Questions

Let’s address common questions based solely on the information available in the documentation.

What is Model Context Protocol (MCP)?

Model Context Protocol is the framework that Weather MCP Server implements. It provides a standardized way for applications to interact with specialized tools and data sources. The protocol enables clear communication between client applications and server functionality.

Can I use this outside the United States?

Weather MCP Server specifically targets US locations because it connects to the National Weather Service API, which primarily covers the United States and its territories. The documentation doesn’t indicate support for international locations.

What happens if the National Weather Service API is unavailable?

The documentation doesn’t specify error handling procedures for NWS API outages. In practice, the server would likely return an error message indicating it cannot connect to the data source. Applications using Weather MCP Server should implement appropriate error handling for such scenarios.

Is there a limit to how often I can use this service?

The documentation doesn’t mention specific usage limits. However, since Weather MCP Server relies on the National Weather Service API, it would inherit any rate limits or usage restrictions imposed by NWS. The MIT license suggests the server itself doesn’t impose additional restrictions.

How current is the weather data?

The documentation indicates the service provides “real-time” weather alerts and forecasts, suggesting data is updated as the National Weather Service issues new information. Weather forecasts typically update every few hours, while alerts are issued as conditions develop.

Can I modify the server for my specific needs?

Yes. Weather MCP Server is released under the MIT License, which permits modification and redistribution. The open-source nature allows users to adapt the server to their specific requirements while benefiting from the core functionality.

What programming knowledge do I need to use this?

Basic Python knowledge is required to install and run the server. Understanding command-line interfaces and fundamental programming concepts will help with integration into larger systems. The documentation assumes familiarity with these technical areas.

How does this compare to commercial weather APIs?

Unlike many commercial weather APIs that charge based on usage volume, Weather MCP Server is completely free. It focuses specifically on US weather data from the authoritative National Weather Service source rather than offering global coverage or additional features found in commercial services.

Best Practices for Implementation

To maximize the value of Weather MCP Server, consider these implementation strategies based on the documented capabilities.

Coordinate Precision Matters

When using get_forecast, precise coordinates yield more accurate results. Rather than using city center coordinates, consider:

  • Using coordinates for your exact location
  • Verifying coordinates with mapping services
  • Understanding that even small coordinate differences can affect local weather conditions

State Code Accuracy

For get_alerts, ensure you use the correct two-letter state abbreviation. Common mistakes include:

  • Using full state names instead of abbreviations
  • Confusing similar abbreviations (e.g., “MS” for Mississippi vs. “MO” for Missouri)
  • Forgetting that territories like Puerto Rico have different codes

Error Handling Implementation

While the documentation doesn’t specify error conditions, robust implementations should:

  • Verify server availability with health_check before critical operations
  • Handle potential connection issues gracefully
  • Provide user-friendly messages when weather data isn’t available

Integration Testing

Before deploying applications that depend on Weather MCP Server:

  • Test with multiple state codes to verify alert functionality
  • Validate forecast results against known weather conditions
  • Confirm the server starts reliably in your target environment

Understanding the Technology Stack

Weather MCP Server leverages several key technologies that work together to deliver weather information.

fastmcp (Version 2.11.0+)

This framework implements the Model Context Protocol, providing the foundation for tool definition and execution. It handles the communication protocol between client and server, ensuring consistent interaction patterns.

httpx (Version 0.28.1+)

As a modern HTTP client library for Python, httpx enables efficient communication with the National Weather Service API. Its asynchronous capabilities support responsive performance even when handling multiple requests.

mcp[cli] (Version 1.12.3+)

This component provides command-line interface functionality, making the server accessible through terminal commands and integrable with various development environments.

These technologies combine to create a robust system that abstracts the complexities of weather data access while maintaining reliability and performance.

License and Attribution

Weather MCP Server operates under the MIT License, one of the most permissive open-source licenses available. This means:

  • You can use the software for any purpose
  • You can modify the code to suit your needs
  • You can distribute original or modified versions
  • You can use it in commercial products

The only requirement is including the original license and copyright notice with any distribution. This generous licensing model encourages widespread adoption and community contribution.

Acknowledgments and Dependencies

The project properly acknowledges its dependencies and foundational technologies:

  • National Weather Service API: The authoritative source for US weather data
  • fastmcp: The framework enabling MCP implementation
  • httpx: The HTTP client library facilitating API communication
  • MCP Protocol: The underlying communication standard
  • Build an MCP Server: Reference documentation for server implementation

These acknowledgments highlight the collaborative nature of open-source development, where projects build upon each other’s work to create more valuable tools.

Getting Started: Your First Weather Query

Let’s walk through a complete example of using Weather MCP Server to retrieve weather information.

Step 1: Start the Server

Open your terminal and navigate to the project directory, then run:

uv run main.py

You should see confirmation that the server has started successfully.

Step 2: Check Server Status

Before proceeding, verify the server is operational:

health_check()

A successful response confirms everything is working correctly.

Step 3: Explore Available Tools

Discover what the server can do:

root()

This displays information about the server and lists the available tools (get_alerts, get_forecast, etc.).

Step 4: Retrieve Weather Alerts

Check for active weather alerts in your state. For example, to check California:

get_alerts("CA")

The response will show any active weather alerts for California.

Step 5: Get a Detailed Forecast

Obtain a forecast for your specific location. For New York City (40.7128, -74.0060):

get_forecast(40.7128, -74.0060)

This returns a detailed forecast for the next five time periods.

Troubleshooting Common Issues

While Weather MCP Server is designed for simplicity, you might encounter these common situations.

Python Version Compatibility

If you receive errors about incompatible Python features, verify your Python version:

python --version

Ensure you’re using Python 3.13 or newer as specified in the requirements.

Dependency Installation Problems

If dependency installation fails:

  • Confirm you’re using uv as recommended
  • Check your internet connection
  • Verify you have the necessary permissions to install packages

Coordinate Format Errors

When using get_forecast, ensure:

  • Latitude values are between -90 and 90
  • Longitude values are between -180 and 180
  • You’re using decimal degrees, not degrees-minutes-seconds

The Value of Authoritative Weather Information

In an era of climate change and increasingly extreme weather events, access to reliable weather information isn’t just convenient—it’s critical for safety and planning. Weather MCP Server provides a direct connection to the National Weather Service, the United States’ official weather authority.

Unlike commercial services that may prioritize profit over accuracy, or social media posts that spread unverified information, Weather MCP Server delivers factual weather data you can trust. When planning outdoor activities, managing agricultural operations, or making business decisions affected by weather conditions, this reliability makes all the difference.

Looking Ahead: Potential Applications

While the current implementation focuses on core weather data access, the foundation supports various potential applications:

  • Custom alert systems: Building notifications for specific weather conditions
  • Historical analysis: Extending the tool to access historical weather patterns
  • Integration with IoT devices: Providing weather data to smart home systems
  • Educational tools: Creating interactive weather learning experiences

The open-source nature of Weather MCP Server invites community contributions to expand its capabilities while maintaining its focus on reliable, authoritative weather information.

Conclusion: Weather Intelligence Made Accessible

Weather MCP Server represents a thoughtful approach to making authoritative weather information accessible without unnecessary complexity. By bridging the gap between the National Weather Service API and end users, it delivers real value through:

  • Simplicity: Straightforward tools that address specific weather information needs
  • Reliability: Direct connection to official weather data sources
  • Transparency: Open-source implementation anyone can verify
  • Accessibility: Clear documentation and sensible requirements

Whether you’re a developer integrating weather data into applications, a professional whose work depends on accurate weather information, or simply someone who values reliable weather forecasts, Weather MCP Server offers a practical solution grounded in authoritative data.

The beauty of this tool lies in what it doesn’t do—it avoids unnecessary features, resists the temptation to sensationalize weather events, and focuses squarely on delivering the weather information you need, when you need it. In a world of increasingly complex software, sometimes the most valuable tools are the ones that simply work, without fanfare or fuss.

As you implement Weather MCP Server in your projects or workflows, remember that its true value emerges not from technical specifications, but from how it helps you make better decisions based on accurate weather information. Whether planning tomorrow’s commute, preparing for severe weather, or building applications that depend on weather data, this tool provides the reliable foundation you need to navigate whatever weather comes your way.