MCP Weather Server
A simple MCP server demonstrating tools, resources, and prompts for weather data, built during an Agentic AI Internship.
README
MCP Weather Server
A simple MCP (Model Context Protocol) server built during an Agentic AI Internship. This server demonstrates how MCP can be used to provide tools, resources, and prompts to AI applications.
Features
MCP servers can provide the following functionalities:
Resources
File-like data that can be read by clients, such as API responses or file contents.
Tools
Functions that can be called by Large Language Models (LLMs) with user approval.
Prompts
Pre-written templates that help users accomplish specific tasks efficiently.
Requirements
- Python 3.10 or higher
- uv package manager
- Python MCP SDK 1.2.0 or higher
- httpx
Project Setup
1. Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
2. Initialize the Project
uv init .
3. Install Dependencies
uv add "mcp[cli]" httpx
4. Create the Server File
Create a file named weather.py and add the MCP server implementation.
5. Run the Server
uv run weather.py
Project Structure
.
├── weather.py
├── pyproject.toml
├── uv.lock
├── README.md
└── .gitignore
Learning Outcomes
- Understanding MCP architecture
- Building custom MCP tools
- Integrating APIs using Python
- Running MCP servers with uv
- Working with Agentic AI applications
Author
Vishal M K B.E. CSE (AI & ML)
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