weatherstack-mcp-server 0.1.1

An MCP server for interacting with the Weatherstack API via Model Context Protocol tools, providing access to current, historical, forecast, and marine weather data.

⛅ Weatherstack MCP Server

This is an MCP (Model Context Protocol) server for integrating with the Weatherstack API, enabling AI agents to access real-time, historical, forecast, and marine weather data using natural language input.

The goal of this project is to expose Weatherstack's functionality through MCP-compatible tools that can be used seamlessly by large language models and agent frameworks.

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🧠 What is MCP?

MCP (Model Context Protocol) is a lightweight protocol designed to let AI agents interact with external tools and APIs in a structured and modular way. Think of it like USB for AI — this server acts as a "driver" for the Weatherstack platform.

With this MCP server, AI models can:

• 🌤️ Get current weather conditions for any location
• 🕰️ Access historical weather data by date and hour
• 📅 Retrieve multi-day weather forecasts
• 🌊 Get marine/sailing weather and tide information

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🚀 How to Run

To use this MCP server, you'll need:

✅ Prerequisites

• Python 3.11+
• uv – a modern Python package manager
• A supported LLM (e.g., Claude)
• A Weatherstack API key – get it at weatherstack.com

Add this to Claude Desktop config

{
  "mcpServers": {
    "weatherstack-mcp": {
      "command": "uvx",
      "args": [
        "weatherstack-mcp-server",
        "--api-key",
        "YOUR WEATHERSTACK API KEY"
      ]
    }
  }
}

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🤝 Contributions Welcome!

Whether you're passionate about weather tech, AI agent development, or robust API integrations — we’d love your help improving this project. You can contribute by:

• Adding support for additional Weatherstack endpoints
• Improving the structure of tool responses
• Writing better tests and usage examples
• Sharing feedback or ideas via Issues or Discussions

Feel free to fork, explore, and open a PR. Let’s empower agents with better environmental awareness — one forecast at a time. 🌍🛰️

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