Weather MCP Server

What does “Weather” MCP Server do?

The Weather MCP Server is a Model Context Protocol (MCP) server that connects AI assistants with real-time and historical weather data by interfacing with the Open-Meteo API. Designed to enhance AI-driven development workflows, it allows AI agents to query current weather, retrieve weather information for specific date ranges, and obtain the current time in any specified timezone. By exposing these capabilities as tools, the Weather MCP Server enables seamless integration of external weather data into large language model (LLM) interactions, supporting use cases like travel planning, context-aware assistants, and data-driven automation—all without requiring API keys or credentials.

List of Prompts

No prompt templates are listed or mentioned in the repository.

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List of Resources

No explicit MCP resources are documented in the repository.

List of Tools

• get_weather
  Retrieves the current weather information for a specified city. Requires the city name as input.

• get_weather_by_datetime_range
  Fetches weather data for a given city between a start and end date (in YYYY-MM-DD format).

• get_current_datetime
  Returns the current time in a specified IANA timezone (e.g., “America/New_York”). Defaults to UTC if not specified.

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Use Cases of this MCP Server

• Travel Planning Assistants
  Use real-time and forecasted weather data to help users plan trips, suggest optimal travel dates, or pack appropriately.

• Event Scheduling
  Integrate weather predictions to recommend suitable dates or locations for outdoor events, meetings, or activities.

• Contextual AI Conversations
  Enable chatbots or virtual assistants to provide context-aware responses based on current or historical weather conditions in a user’s location.

• Data Analysis and Visualization
  Retrieve historical weather data for analytical tools or dashboards to uncover trends or inform business decisions.

• Smart Home Automation
  Trigger actions (e.g., adjust thermostats, close windows) based on current or upcoming weather conditions provided by the server.

How to set it up

Windsurf

1. Ensure you have Python and pip installed.

2. Install the MCP Weather Server:
   pip install mcp_weather_server

3. Locate your Windsurf MCP configuration file (typically windsurf_mcp_settings.json).

4. Add the Weather MCP Server configuration:

   {
     "mcpServers": {
       "weather": {
         "command": "python",
         "args": [
           "-m",
           "mcp_weather_server"
         ],
         "disabled": false,
         "autoApprove": []
       }
     }
   }
   

5. Save the file and restart Windsurf.

6. Verify that the “weather” server appears in the MCP server list.

Claude

1. Install Python and pip if not already present.

2. Run pip install mcp_weather_server.

3. Edit your Claude MCP settings file (e.g., claude_mcp_settings.json).

4. Insert the following under the mcpServers key:

   {
     "mcpServers": {
       "weather": {
         "command": "python",
         "args": [
           "-m",
           "mcp_weather_server"
         ],
         "disabled": false,
         "autoApprove": []
       }
     }
   }
   

5. Save changes and restart Claude.

6. Check that the Weather MCP Server is available.

Cursor

1. Make sure Python and pip are available.

2. Execute pip install mcp_weather_server.

3. Open your Cursor configuration file (cursor_mcp_settings.json).

4. Add the Weather MCP Server entry:

   {
     "mcpServers": {
       "weather": {
         "command": "python",
         "args": [
           "-m",
           "mcp_weather_server"
         ],
         "disabled": false,
         "autoApprove": []
       }
     }
   }
   

5. Save and restart Cursor.

6. Confirm the server is running in the MCP integrations panel.

Cline

1. Ensure Python and pip are installed.

2. Install the server using:
   pip install mcp_weather_server

3. Locate your cline_mcp_settings.json configuration file.

4. Add the following block:

   {
     "mcpServers": {
       "weather": {
         "command": "python",
         "args": [
           "-m",
           "mcp_weather_server"
         ],
         "disabled": false,
         "autoApprove": []
       }
     }
   }
   

5. Save the configuration file.

6. Restart Cline and verify the Weather MCP Server is active.

Securing API Keys

This server does not require API keys, as it uses the free and open-source Open-Meteo API. If API keys were needed, you would use environment variables in your configuration like this:

{
  "mcpServers": {
    "weather": {
      "env": {
        "API_KEY": "<YOUR_KEY>"
      },
      "inputs": {
        "api_key": "<YOUR_KEY>"
      }
    }
  }
}

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": {
    "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” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above, the Weather MCP Server offers solid core functionality (tools), clear setup, and is open source, but lacks advanced MCP features like resources, prompt templates, or sampling. Its utility is straightforward and easy to use. I would rate this MCP server a 6/10 for general-purpose integration—great for weather, but limited in MCP extensibility.

MCP Score