Mcp Weather Stdio
What is Mcp Weather Stdio
The Model Context Protocol (MCP) is a framework that facilitates standardized communication between AI models and external tools or services. It acts as an intermediary, allowing AI models to access external data or perform actions seamlessly, akin to a universal translator.
Use cases
MCP is applicable in various scenarios where AI models need to interact with external systems, such as checking weather data, conducting data analysis, or any other functionality that requires external input or services. This is exemplified in the weather service implementation, showcasing real-world applications of MCP.
How to use
To set up an MCP server, start by installing the server using the recommended command with UV. Run the server in a specified directory and test its functionality using the MCP inspector. Users can also modify the server to add new features or capabilities, building on the provided examples.
Key features
Key features of the MCP server include structured implementation using Python, asynchronous communication capabilities, integration with various AI platforms, and packaging for easy distribution. The weather service example offers a practical illustration of these concepts.
Where to use
MCP can be used in any context where AI models require interaction with external services. This includes applications in weather forecasting, data analytics, and other domains where AI-driven insights are enhanced through real-time data access and processing.
Overview
What is Mcp Weather Stdio
The Model Context Protocol (MCP) is a framework that facilitates standardized communication between AI models and external tools or services. It acts as an intermediary, allowing AI models to access external data or perform actions seamlessly, akin to a universal translator.
Use cases
MCP is applicable in various scenarios where AI models need to interact with external systems, such as checking weather data, conducting data analysis, or any other functionality that requires external input or services. This is exemplified in the weather service implementation, showcasing real-world applications of MCP.
How to use
To set up an MCP server, start by installing the server using the recommended command with UV. Run the server in a specified directory and test its functionality using the MCP inspector. Users can also modify the server to add new features or capabilities, building on the provided examples.
Key features
Key features of the MCP server include structured implementation using Python, asynchronous communication capabilities, integration with various AI platforms, and packaging for easy distribution. The weather service example offers a practical illustration of these concepts.
Where to use
MCP can be used in any context where AI models require interaction with external services. This includes applications in weather forecasting, data analytics, and other domains where AI-driven insights are enhanced through real-time data access and processing.
Content
Building a Model Context Protocol (MCP) Server: A Practical Guide
This repository contains a working example of a Model Context Protocol (MCP) server implementation. We’ve created a weather service to demonstrate how to build, package, and deploy an MCP server that can integrate with AI models and applications.
What is the Model Context Protocol?
The Model Context Protocol (MCP) enables AI models to interact with external tools and services in a standardized way. Think of it as a universal translator between AI models and the tools they need to use. When an AI model needs to check the weather, analyze data, or perform any external action, MCP provides the framework for this communication.
Why This Example Matters
Building an MCP server might seem daunting at first, but this weather service example breaks down the process into understandable components. We’ve chosen weather data because it’s:
- Familiar to everyone
- Complex enough to show real-world patterns
- Simple enough to understand quickly
Core Concepts Demonstrated
Through this example, you’ll learn how to:
- Structure an MCP server using Python
- Define and implement tools that AI models can use
- Handle asynchronous communication via STDIO
- Package your server for distribution
- Integrate with different AI platforms
Getting Started
We’ve organized the documentation into several guides:
- Understanding MCP Server Structure - Core components and their roles
- Implementation Guide - Step-by-step walkthrough of the code
- Installation and Running - Different ways to deploy your server
- Integration Guide - Connecting with Claude Desktop and other platforms
Quick Start
If you’re eager to see the server in action, follow these steps:
- Install using UV (recommended):
uv pip install -e .
- Run the server:
uv --directory path/to/project run mcp_weather_service_stdio
- Test with the MCP inspector:
npx @modelcontextprotocol/inspector uv --directory path/to/project run mcp_weather_service_stdio
Learning Path
If you’re new to MCP, we recommend following this learning path:
- Start by reading the Understanding MCP Server Structure guide
- Experiment with running the server using the Quick Start instructions
- Study the implementation details in Implementation Guide
- Try modifying the server to add new capabilities
Contributing
We welcome contributions that help make this example more educational. Whether it’s better documentation, new features that demonstrate additional MCP capabilities, or improved explanations, please feel free to submit a pull request.
Next Steps
Ready to dive in? Head to Understanding MCP Server Structure to begin your journey into MCP server development.