Weather MCP Server

What does “Weather” MCP Server do?

The Weather MCP Server is a Model Context Protocol (MCP) server designed to provide AI assistants with seamless access to comprehensive weather data and related services. By acting as an intermediary between AI clients and the WeatherAPI, this server enables AI-driven workflows to retrieve current weather conditions, forecasts (up to 14 days), historical weather data, air quality indices, astronomy data, location-based searches, timezone information, and even details on sports events. The server is built with FastAPI and the MCP framework, facilitating easy integration into AI development environments. This enhances the ability of AI agents to answer user queries, automate weather-dependent workflows, and enrich context for language model interactions.

List of Prompts

No explicit prompt templates were found in the repository files.

List of Resources

No explicit resources are described in the available documentation or code listings.

List of Tools

• Current weather conditions: Provides real-time data about temperature, humidity, wind speed, etc., for a specified location.
• Weather forecasts (1-14 days): Retrieves weather predictions for upcoming days, allowing planning based on forecasted conditions.
• Historical weather data: Accesses past weather data for analytics or retrospective queries.
• Weather alerts: Supplies warnings about severe weather events.
• Air quality information: Fetches information about the air pollution level and air quality index for a given location.
• Astronomy data: Delivers details such as sunrise, sunset, and moon phases.
• Location search: Enables searching and resolving of locations for weather queries.
• Timezone information: Provides local timezone information for specified locations.
• Sports events: Returns weather conditions relevant to sports events.

Use Cases of this MCP Server

• Personal Assistant Integration: AI assistants can leverage the server to answer user queries about weather, sunrise/sunset times, and air quality, enhancing the user experience.
• Travel Planning: Developers can automate itinerary planning by integrating weather forecasts and alerts for destinations, allowing users to adjust plans based on weather conditions.
• Environmental Monitoring Dashboards: The server can power dashboards that monitor air quality and weather trends, supporting health advisories and urban planning.
• Event Scheduling: Teams organizing sports or outdoor events can use the server to check historical and forecasted weather conditions, optimizing event timing.
• Smart Home Automation: Integrate weather data to automate home devices—e.g., adjusting thermostats, closing windows, or sending alerts based on upcoming weather changes.

How to set it up

Windsurf

1. Ensure Python 3.13+ and the uv package manager are installed.
2. Add the Weather MCP Server to your configuration.
3. Insert the server in your mcpServers object with the command and arguments.
4. Save the configuration and restart Windsurf.
5. Verify connectivity to the server.

JSON configuration example

"mcpServers": {
  "weather-mcp": {
    "command": "python",
    "args": ["main.py"]
  }
}

Securing API Keys

Set your WeatherAPI key using environment variables:

"env": {
  "WEATHER_API_KEY": "your_api_key_here"
},
"inputs": {
  // Other config options
}

Claude

1. Ensure Python 3.13+ and the uv package manager are installed.
2. Add the Weather MCP Server to Claude’s configuration.
3. Edit the mcpServers object as shown below.
4. Save and restart Claude.
5. Test by prompting Claude for weather data.

JSON configuration example

"mcpServers": {
  "weather-mcp": {
    "command": "python",
    "args": ["main.py"]
  }
}

Securing API Keys

"env": {
  "WEATHER_API_KEY": "your_api_key_here"
}

Cursor

1. Install Python 3.13+ and uv.
2. Add the Weather MCP Server in Cursor’s setup.
3. Edit the configuration file to include the server.
4. Save and restart Cursor.
5. Verify that weather queries are functioning.

JSON configuration example

"mcpServers": {
  "weather-mcp": {
    "command": "python",
    "args": ["main.py"]
  }
}

Securing API Keys

"env": {
  "WEATHER_API_KEY": "your_api_key_here"
}

Cline

1. Make sure Python 3.13+ and uv are installed.
2. Edit Cline’s configuration to add the Weather MCP Server.
3. Add the appropriate entry to the mcpServers object.
4. Save changes and restart Cline.
5. Confirm the server is operational.

JSON configuration example

"mcpServers": {
  "weather-mcp": {
    "command": "python",
    "args": ["main.py"]
  }
}

Securing API Keys

"env": {
  "WEATHER_API_KEY": "your_api_key_here"
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "weather-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “weather-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available information, the Weather MCP Server provides solid tool coverage and easy setup, but lacks explicit documentation for prompts, resources, or support for roots and sampling. Its primary focus is on weather-related tools, with clear instructions for API key security. For a focused weather MCP, it’s effective but could be improved with more MCP-standard documentation and resource definitions.

MCP Score