🌤️ MCP Agent

A sophisticated Model Context Protocol (MCP) agent that provides advanced weather forecasting capabilities using machine learning models, real-time weather data, and multiple AI tools integrated through the MCP framework.

🚀 Overview

This project implements a powerful weather forecasting system that combines:

• Real-time weather data from WeatherAPI
• Machine learning models for advanced predictions
• Multiple AI tools (crypto, web search, Wikipedia, etc.)
• MCP (Model Context Protocol) for seamless tool integration
• Groq AI for natural language processing

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Client Side   │◄──►│   MCP Server    │◄──►│   AI Services   │
│  (Groq + MCP)   │    │   (FastMCP)     │    │ (APIs & Models) │
└─────────────────┘    └─────────────────┘    └─────────────────┘
        │                       │                       │
        ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ User Interface  │    │   Tool Manager  │    │  External APIs  │
│    & Queries    │    │   & Registry    │    │ & ML Models     │
└─────────────────┘    └─────────────────┘    └─────────────────┘

📁 Project Structure

MCPAgent/
├── 📄 ClientSide-SSE-MCP.py      # Main client application
├── 📄 ServerSideMCP.py           # MCP server entry point
├── 📁 MCPTools/                  # Core tools and services
│   ├── 📄 server.py              # FastMCP server configuration
│   ├── 📁 get_weather/           # Weather data processing
│   │   └── 📄 getweather.py      # Weather API integration
│   ├── 📁 loader/                # ML models and API clients
│   │   ├── 📄 loader.py          # Resource loader
│   │   └── 📁 ML-models/         # Pre-trained ML models
│   │       ├── 🤖 voting_regressor.joblib
│   │       ├── 🔧 feature_scaler.joblib
│   │       └── 🔧 target_scaler.joblib
│   └── 📁 tools/                 # Available MCP tools
│       ├── 🌤️ weather.py         # Weather forecasting
│       ├── 💰 trade.py           # Cryptocurrency data
│       ├── 🔍 web_search.py      # Web search capabilities
│       ├── 📚 wiki.py            # Wikipedia integration
│       ├── 📖 manga.py           # Manga information
│       ├── 🏓 ping.py            # System utilities
│       └── 🛠️ utils.py           # Helper functions
├── 📄 .gitignore                 # Git ignore rules
└── 📄 .env.example              # Environment variables template

🛠️ Installation & Setup

Prerequisites

• Python 3.8+
• pip package manager
• API keys for various services

1. Clone & Install Dependencies

git clone <repository-url>
cd MCPAgent
pip install -r requirements.txt  # You may need to create this

2. Required Dependencies

pip install mcp
pip install groq
pip install pandas
pip install requests
pip install joblib
pip install python-dotenv
pip install tavily-python
pip install firecrawl-py
pip install openai

3. Environment Configuration

Create a .env file with the following API keys:

# AI Services
GROQ_API_KEY_2=your_groq_api_key_here
OPENROUTER=your_openrouter_api_key

# Weather Service
WEATHER_API_KEY=your_weatherapi_key

# Search Services
TAVILY_API_KEY=your_tavily_key
GOOGLE_SEARCH_API_KEY=your_google_search_key
PROGRAMMABLE_SEARCH_ENGINE_ID=your_search_engine_id
FIRECRAWL_SANE_API=your_firecrawl_key

# Financial Data
ALPHAVANTAGE_API=your_alphavantage_key

4. API Key Setup Guide

Service	Description	How to Get
Groq	Fast AI inference	groq.com → Console → API Keys
WeatherAPI	Weather data	weatherapi.com → Free tier available
Tavily	AI search	tavily.com → Sign up for API
Firecrawl	Web scraping	firecrawl.dev → Get API key
Google Search	Custom search	Google Cloud Console → Custom Search API
AlphaVantage	Financial data	alphavantage.co → Free API key

🚀 Usage

Quick Start

# Run the MCP server
python run.py

Example Queries

🌤️ Weather Forecasting

"What's the weather like in London?"
"Give me a 5-day forecast for New York"
"Show me hourly weather for tomorrow in Paris"
"What's the air quality in Tokyo right now?"

💰 Cryptocurrency Data

"What's the current Bitcoin price?"
"Show me Ethereum trading data"

🔍 Research & Information

"Search for recent climate change news"
"Find information about machine learning in weather prediction"
"What's happening with renewable energy stocks?"

📚 Knowledge Queries

"Tell me about meteorology from Wikipedia"
"Search for the latest manga releases"

🛠️ Available Tools

Core Weather Tools

• get_weather() - Current weather and forecasts
  • Supports current conditions, daily/hourly forecasts
  • Air quality monitoring
  • Severe weather alerts
  • Multiple forecast types (daily/hourly)

Financial Tools

• get_bitcoin_price() - Real-time Bitcoin pricing
• get_crypto_data() - Comprehensive cryptocurrency data

Search & Research Tools

• deep_research() - AI-powered research assistant
• internet_search() - Google search integration
• search_firecrawl() - Advanced web scraping
• wikipedia_search() - Wikipedia knowledge base

Utility Tools

• get_datetime() - Date and time utilities
• get_project_structure() - Project analysis
• get_credits() - System information
• get_summarized_manga_info() - Manga database queries

🔧 Advanced Configuration

Custom Model Usage

# Initialize with different AI models
client = MCPGroqClient(model="deepseek-ai/DeepSeek-V3")
# or
client = MCPGroqClient(model="llama3-8b-8192")

Weather Forecast Types

# Daily forecast
get_weather("London", forecast=True, days=5, forecast_type="daily")

# Hourly forecast
get_weather("Paris", forecast=True, days=3, forecast_type="hourly", day_for_hourly=1)

# Current weather with air quality
get_weather("Tokyo", current=True)

🧠 Machine Learning Models

The project includes pre-trained ML models for enhanced weather prediction:

• voting_regressor.joblib - Ensemble model for weather prediction
• feature_scaler.joblib - Input feature normalization
• target_scaler.joblib - Output scaling for predictions

These models work in conjunction with real-time API data to provide more accurate forecasts.

🔌 MCP (Model Context Protocol) Integration

This project leverages MCP for:

• Tool Registration - Automatic discovery of available functions
• Type Safety - Structured parameter validation
• Scalable Architecture - Easy addition of new tools
• Client-Server Communication - Robust messaging protocol

🌐 API Integrations

Service	Purpose	Documentation
WeatherAPI	Weather data & forecasts	docs.weatherapi.com
Groq	Fast AI inference	docs.groq.com
Tavily	AI-powered search	docs.tavily.com
Firecrawl	Web scraping	docs.firecrawl.dev
AlphaVantage	Financial data	alphavantage.co/documentation

🎯 Key Features

• ✅ Real-time Weather Data - Current conditions and forecasts
• ✅ ML-Enhanced Predictions - Machine learning model integration
• ✅ Multi-tool Integration - Crypto, search, research capabilities
• ✅ Natural Language Interface - Conversational AI interactions
• ✅ Air Quality Monitoring - Environmental health data
• ✅ Severe Weather Alerts - Safety notifications
• ✅ Flexible Forecast Types - Daily, hourly, and custom ranges
• ✅ Multiple AI Models - Support for various LLMs
• ✅ Extensible Architecture - Easy to add new tools

🚨 Error Handling

The system includes robust error handling for:

• Invalid API keys
• Network connectivity issues
• Malformed queries
• API rate limiting
• Missing environment variables

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• MCP (Model Context Protocol) for the protocol framework
• Groq for lightning-fast AI inference
• WeatherAPI for comprehensive weather data
• FastMCP for the MCP server implementation

🐛 Troubleshooting

Common Issues

1. API Key Errors

   Error: Failed to get weather data
   Solution: Check your .env file and ensure all API keys are valid
   

2. Module Import Errors

   Error: ModuleNotFoundError
   Solution: Install missing dependencies with pip install -r requirements.txt
   

3. MCP Connection Issues

   Error: Failed to connect to MCP server
   Solution: Ensure ServerSideMCP.py is accessible and Python path is correct
   

Getting Help

• 📧 Create an issue in the repository
• 📖 Check the API documentation for individual services
• 🔍 Review error logs for specific error messages

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Made with ❤️ for better weather forecasting and AI tool integration