AMMS46/Pokemon_MCP_Server

Pokémon MCP Server 🎮⚡

app link: https://pokemonmcpserver.streamlit.app/

demolink: https://drive.google.com/file/d/1kA3IO0a1GzDYc20y3DSLGLCvIBvvqKUY/view?usp=sharing

A modular AI-powered middleware server for Pokémon data retrieval, team building, and strategic analysis.

✨ Features

• 🔍 Pokémon Search: Detailed information retrieval from PokeAPI
• ⚖️ Pokémon Comparison: Side-by-side stat and attribute analysis
• 🤖 AI Team Builder: Generate balanced teams from natural language descriptions
• 🎯 Strategic Counters: AI-powered counter-Pokémon suggestions
• 🌐 Web Interface: Interactive Streamlit frontend
• 📊 Comprehensive Logging: Full request tracking and error monitoring

🚀 Quick Start

Prerequisites

• Python 3.8+
• Google Gemini API Key

Installation

1. Clone & Setup

git clone <your-repo-url>
cd pokemon-mcp-server
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install Dependencies

pip install fastapi uvicorn requests python-dotenv langchain-google-genai langchain-core streamlit

3. Environment Configuration

# Create .env file
echo "GEMINI_API_KEY=your_api_key_here" > .env

4. Run the Application

# Start backend (Terminal 1)
uvicorn main:app --reload

# Start frontend (Terminal 2)
streamlit run streamlit_app.py

5. Access the Application

• API Documentation: http://localhost:8000/docs
• Web Interface: http://localhost:8501

🛠️ API Endpoints

| /pokemon/{name} | GET | Get detailed Pokémon information | | /compare/{pokemon1}/{pokemon2} | GET | Compare two Pokémon | | /team/generate | POST | Generate team from description | | /counters/{pokemon_name} | GET | Get counter suggestions |

📋 Example Usage

Get Pokémon Data

curl http://localhost:8000/pokemon/pikachu

Generate Team

curl -X POST http://localhost:8000/team/generate \
  -H "Content-Type: application/json" \
  -d '{"description": "balanced team with strong defense"}'

🏗️ Architecture

Web Frontend (Streamlit) ←→ MCP Server (FastAPI) ←→ PokeAPI
                                    ↓
                            Google Gemini AI

Modular Components

• Information Retrieval: PokeAPI data fetching and normalization
• Comparison Module: Multi-Pokémon analysis
• Strategy Module: AI-powered counter suggestions
• Team Composition: Natural language team generation

## 🚢 Deployment

### Docker
```dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Environment Variables

GEMINI_API_KEY=your_gemini_api_key
ENVIRONMENT=production
LOG_LEVEL=INFO

📁 Project Structure

pokemon-mcp-server/
├── main.py              # FastAPI backend
├── streamlit_app.py     # Web interface
├── .env                 # Environment variables
├── requirements.txt     # Dependencies
├── tests/              # Test files
└── docs/               # Documentation

🔧 Tech Stack

• Backend: FastAPI, Python
• AI: Google Gemini (LangChain)
• Frontend: Streamlit
• External API: PokeAPI
• Environment: python-dotenv

📝 License

MIT License - see LICENSE file for details.

🤝 Contributing

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

📞 Support

For issues and questions:

• Create an issue on GitHub
• Check the

Built with ❤️ for Pokémon trainers and AI enthusiasts