🤖 Model Context Protocol (MCP)

Overview

The Model Context Protocol (MCP) is an open standard that enables Artificial Intelligence (AI) models to securely communicate with external tools, applications, databases, APIs, and services through a standardized interface. It allows Large Language Models (LLMs) to access real-time information, execute functions, and interact with external systems beyond their built-in knowledge.

MCP separates AI reasoning from tool execution by providing a consistent protocol for exposing capabilities such as tools, resources, and prompts. This makes AI applications more modular, reusable, and easier to integrate with different services.

Key Components

🔹 Tools

Tools are executable functions that allow AI models to perform specific tasks such as mathematical calculations, weather retrieval, database queries, or API calls.

🔹 Resources

Resources provide structured information that AI models can read, including server metadata, documentation, configuration files, or datasets.

🔹 Prompts

Prompts are reusable templates that guide AI models in generating consistent and context-aware responses for various tasks.

Features

• Standardized communication between AI models and external systems
• Easy integration with APIs and third-party services
• Support for synchronous and asynchronous tools
• Modular architecture for scalable AI applications
• Secure interaction with external resources
• Extensible design for building custom AI assistants

🧮 Math MCP Server

Description

The Math MCP Server is an MCP-based application that provides mathematical operations as AI-accessible tools. It demonstrates how mathematical functions can be exposed through the Model Context Protocol, allowing AI assistants to perform calculations accurately and efficiently.

Features

• Addition
• Subtraction
• Multiplication
• Division
• Power calculation
• Square root
• Factorial
• Percentage calculation
• Average calculation
• Prime number checking
• Quadratic equation solver
• Math resource information
• Step-by-step mathematical explanation prompt

Example Use Cases

• Basic arithmetic calculations
• Engineering computations
• Student learning assistance
• Scientific calculations
• Mathematical problem solving
• Educational AI assistants

🌦️ Weather MCP Server

Description

The Weather MCP Server is an MCP-based application that exposes weather-related services as AI tools. It enables AI assistants to provide weather information, forecasts, rain probability, wind details, and travel recommendations through a standardized MCP interface.

The current implementation uses simulated weather data to demonstrate MCP functionality and can easily be extended to integrate with real-time weather APIs.

Features

• Current weather information
• Multi-day weather forecast
• Rain probability prediction
• Celsius ↔ Fahrenheit conversion
• Wind speed information
• Weather server metadata
• AI-powered travel advice prompt

Example Use Cases

• Weather assistant applications
• Travel planning
• Outdoor activity recommendations
• Educational demonstrations of MCP
• AI-powered virtual assistants
• Smart weather information systems

🚀 Technologies Used

• Python
• Model Context Protocol (MCP)
• FastMCP
• Async Programming
• Standard Python Libraries
• REST API Integration (Extensible)

📌 Purpose

This repository demonstrates how to build MCP servers using Python and FastMCP. It showcases how AI models can interact with external tools through a standardized protocol by implementing practical examples such as a Math MCP Server and a Weather MCP Server. These projects serve as learning resources for developers exploring AI tool integration, agent development, and the Model Context Protocol ecosystem.

This project includes tools for retrieving weather information, generating weather forecasts, checking rain probability, converting temperatures between Celsius and Fahrenheit, and obtaining wind speed information. It also provides MCP resources and prompts to support AI-powered travel recommendations.

Features

• 🌤️ Get current weather information for any location
• 📅 Generate multi-day weather forecasts
• 🌧️ Check rain probability for a location
• 🌡️ Convert temperatures between Celsius and Fahrenheit
• 💨 Retrieve wind speed information
• 📚 Expose server metadata using MCP resources
• 🤖 Generate travel advice using MCP prompts
• 🔗 Communicate using the Model Context Protocol (MCP)

Technologies Used

• Python
• Model Context Protocol (MCP)
• FastMCP
• Async Programming
• Random (for simulated weather data)
• Datetime

Project Structure

• Tools – Perform weather-related operations.
• Resources – Provide server metadata and available capabilities.
• Prompts – Generate AI-friendly travel advice based on weather conditions.

Purpose

This project demonstrates how to build an MCP server that exposes reusable tools for AI assistants. It serves as a learning example for integrating Python applications with the Model Context Protocol and showcases how AI agents can invoke external functionality through standardized tool calls.

Future Improvements

• Integrate with real-time weather APIs such as OpenWeatherMap or Open-Meteo.
• Add air quality and UV index information.
• Support severe weather alerts.
• Provide hourly forecasts.
• Include location detection using GPS or IP geo location.
• Add weather history and climate analytics.