🤖 MCP Enterprise AI Assistant System

A comprehensive Model Context Protocol (MCP) based AI application featuring intelligent server selection, specialized AI tools, and a beautiful web interface for enterprise-grade text analysis, code review, sentiment analysis, and knowledge management.

🌟 Workshop Ready Features

🏗️ True MCP (Model Context Protocol) Implementation

📋 JSON-RPC 2.0 Protocol: Full compliance with JSON-RPC 2.0 specification
� MCP Initialize Handshake: Proper server capability negotiation
🔄 Standard MCP Methods: initialize, tools/list, tools/call, resources/list
⚡ WebSocket Transport: Persistent connections as per MCP specification
🎯 Tool Schema Compliance: Proper inputSchema format for tool definitions

🚀 AI-Powered Enterprise Features

🧠 Intelligent Server Selection: AI routing to appropriate servers based on context
📝 Text Analysis: AI summarization, entity extraction, and classification
🔍 Code Review: Automated quality analysis, bug detection, improvements
😊 Sentiment Analysis: Advanced emotion detection and sentiment scoring
📚 Knowledge Management: Document Q&A and information retrieval
🎨 Beautiful Web UI: Professional interface with structured result display
🔧 Configurable AI Models: Support for Ollama (local) and Azure OpenAI

🏗️ System Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Web Frontend  │────│   Flask App     │────│   MCP Host      │
│   (HTML/JS/CSS) │    │   (Web Server)  │    │ (AI Coordinator)│
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                        │
                                               ┌─────────────────┐
                                               │   MCP Client    │
                                               │ (Communication) │
                                               └─────────────────┘
                                                        │
        ┌───────────────────┬───────────────────┬───────────────────┬───────────────────┐
        │                   │                   │                   │                   │
┌───────▼────────┐ ┌────────▼────────┐ ┌───────▼────────┐ ┌────────▼────────┐
│ Text Analysis  │ │  Code Review    │ │ Sentiment      │ │   Knowledge     │
│    Server      │ │    Server       │ │   Analysis     │ │    Server       │
│   Port: 8001   │ │   Port: 8002    │ │   Port: 8003   │ │   Port: 8004    │
└────────────────┘ └─────────────────┘ └────────────────┘ └─────────────────┘

📋 Prerequisites

1. Python Installation

Python 3.8 or higher is required
Download from: https://python.org/downloads/
Verify installation: python --version or py --version

2. Ollama Installation & Setup

Install Ollama

Download Ollama: Visit https://ollama.ai/download
Install for your OS:
- Windows: Download and run the installer
- macOS: brew install ollama
- Linux: curl -fsSL https://ollama.ai/install.sh | sh

Download Required AI Model

# Download the Llama 3.2 3B model (recommended for this project)
ollama pull llama3.2:3b

# Verify the model is installed
ollama list

Start Ollama Service

# Start Ollama service (keep this running)
ollama serve

3. Git Installation (for cloning the repository)

Download from: https://git-scm.com/downloads
Verify: git --version

🚀 Installation & Setup

Step 1: Clone the Repository

git clone https://github.com/harunraseed07/ADC_MCP_Project.git
cd ADC_MCP_Project

Step 2: Install Python Dependencies

# Install required packages
pip install -r requirements.txt

Step 3: Verify Ollama Model

# Make sure llama3.2:3b is available
ollama list

# If not installed, download it
ollama pull llama3.2:3b

Step 4: Configure the System

The system is pre-configured to use:

AI Provider: Ollama (local)
Model: llama3.2:3b
Ports: 8001-8004 for MCP servers, 5000 for web interface

Configuration files are in the config/ directory.

🎯 Quick Start

Method 1: Automated Start (Recommended)

# Start all servers and web application (Windows)
start_demo_system.bat

# For PowerShell
./start_demo_system.bat

Method 2: Manual Start

# Terminal 1: Start Text Analysis Server
python -m mcp_servers.text_analysis_server

# Terminal 2: Start Code Review Server  
python -m mcp_servers.code_review_server

# Terminal 3: Start Sentiment Analysis Server
python -m mcp_servers.sentiment_analysis_server

# Terminal 4: Start Knowledge Server
python -m mcp_servers.knowledge_server

# Terminal 5: Start Web Application
python web_app/app.py

Step 5: Access the Application

Open your browser
Navigate to: http://localhost:5000
Start interacting with the AI assistant!

💡 Usage Examples

Text Analysis

"Summarize this text: [your text here]"
"Extract entities from: [your text]"
"Classify this content: [your content]"

Code Review

"Review this Python code: def function_name():"
"Check this JavaScript for bugs: [your code]"
"Analyze code quality: [your code]"

Sentiment Analysis

"Analyze sentiment: I love this product!"
"What's the emotion in: [your text]"
"Sentiment of customer feedback: [feedback]"

Knowledge Management

"Search for information about: [topic]"
"What do you know about: [subject]"
"Find documents related to: [query]"

� MCP Protocol Implementation

JSON-RPC 2.0 Compliance

All communication follows JSON-RPC 2.0 specification:

{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": "summarize_text",
        "arguments": {"text": "Your text here"}
    }
}

MCP Standard Methods

initialize: Server capability handshake
tools/list: Get available tools with schemas
tools/call: Execute tool with arguments
resources/list: List available resources
resources/read: Read resource content

Tool Schema Format

{
    "name": "summarize_text",
    "description": "AI-powered text summarization",
    "inputSchema": {
        "type": "object",
        "properties": {
            "text": {
                "type": "string",
                "description": "Text to summarize"
            }
        },
        "required": ["text"]
    }
}

�🔧 Configuration

AI Model Configuration

Edit config/mcp_config.json:

{
    "ai_provider": "ollama",
    "ai_model": "llama3.2:3b",
    "ai_base_url": "http://localhost:11434"
}

Server Ports

Text Analysis: 8001
Code Review: 8002
Sentiment Analysis: 8003
Knowledge: 8004
Web Interface: 5000

🛠️ Troubleshooting

Common Issues

"Ollama not found" Error

# Make sure Ollama is running
ollama serve

"Model not found" Error

# Download the required model
ollama pull llama3.2:3b

Port Already in Use

# Check what's using the ports
netstat -ano | findstr :8001
# Kill processes if needed
kill_all_servers.bat

Python Module Not Found

# Reinstall dependencies
pip install -r requirements.txt

Utility Scripts

check_ports.bat - Check which ports are in use
kill_all_servers.bat - Stop all running servers
start_demo_system.bat - Start entire system

🎓 Workshop Activities

Activity 1: Basic Setup (15 minutes)

Install prerequisites (Python, Ollama)
Download the llama3.2:3b model
Clone and setup the project
Start the system and verify it's working

Activity 2: Understanding MCP (20 minutes)

Explore the system architecture
Examine how AI routing works
Test different types of queries
Observe server selection logic

Activity 3: Customization (25 minutes)

Modify server responses
Add new AI tools
Customize the web interface
Experiment with different AI models

Activity 4: Advanced Features (20 minutes)

Implement custom server logic
Add new MCP servers
Integrate external APIs
Deploy to production environment

📁 Project Structure

ADC_MCP_Project/
├── mcp_servers/           # MCP server implementations
│   ├── text_analysis_server.py
│   ├── code_review_server.py
│   ├── sentiment_analysis_server.py
│   └── knowledge_server.py
├── mcp_client/            # MCP client for communication
│   └── client.py
├── mcp_host/              # AI-powered MCP host
│   ├── host.py
│   └── ai_models.py
├── web_app/               # Flask web application
│   ├── app.py
│   ├── templates/
│   └── static/
├── config/                # Configuration files
│   └── mcp_config.json
├── scripts/               # Utility scripts
└── requirements.txt       # Python dependencies

🤝 Contributing

Fork the repository
Create a feature branch: git checkout -b feature-name
Make your changes
Test thoroughly
Submit a pull request

📄 License

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

🆘 Support

Issues: Report bugs and request features via GitHub Issues
Documentation: Check the project files for detailed guides
Community: Join discussions for help and collaboration

🎉 Acknowledgments

Built with the Model Context Protocol (MCP) framework
Powered by Ollama and Llama 3.2 AI models
Inspired by enterprise AI automation needs

Happy Coding! 🚀 Ready to explore the future of AI-powered enterprise applications!

Verify port configurations

4. Web UI not loading:

Check if Flask is running on the correct port
Verify template files exist
Check browser console for errors

Logging

Enable debug logging by setting:

FLASK_DEBUG=True
LOG_LEVEL=DEBUG

View logs in the terminal where you started the application.

📊 Performance

Resource Usage

Memory: ~200-500MB (depending on AI model)
CPU: Low (spikes during AI inference)
Network: Minimal (local WebSocket communication)

Scalability

Each server can handle multiple concurrent connections
AI model responses are cached for common queries
WebSocket connections are persistent and efficient

🔒 Security

Current Implementation

Local communication only (localhost)
No authentication required
Mock data for demonstration

Production Considerations

Add authentication and authorization
Use HTTPS/WSS for encrypted communication
Implement rate limiting
Add input validation and sanitization
Use real databases with proper security

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

Code Style

Follow PEP 8 for Python code
Use meaningful variable and function names
Add docstrings for classes and functions
Comment complex logic

📝 License

This project is for demonstration purposes. See LICENSE file for details.

🆘 Support

For issues and questions:

Check the troubleshooting section
Review server logs
Open an issue with detailed information

🎯 Future Enhancements

Built with ❤️ using the Model Context Protocol (MCP)

ADC Enterprise Orchestration

🤖 MCP Enterprise AI Assistant System

🌟 Workshop Ready Features

🏗️ True MCP (Model Context Protocol) Implementation

🚀 AI-Powered Enterprise Features

🏗️ System Architecture

📋 Prerequisites

1. Python Installation

2. Ollama Installation & Setup

Install Ollama

Download Required AI Model

Start Ollama Service

3. Git Installation (for cloning the repository)

🚀 Installation & Setup

Step 1: Clone the Repository

Step 2: Install Python Dependencies

Step 3: Verify Ollama Model

Step 4: Configure the System

🎯 Quick Start

Method 1: Automated Start (Recommended)

Method 2: Manual Start

Step 5: Access the Application

💡 Usage Examples

Text Analysis

Code Review

Sentiment Analysis

Knowledge Management

� MCP Protocol Implementation

JSON-RPC 2.0 Compliance

MCP Standard Methods

Tool Schema Format

�🔧 Configuration

AI Model Configuration

Server Ports

🛠️ Troubleshooting

Common Issues

Utility Scripts

🎓 Workshop Activities

Activity 1: Basic Setup (15 minutes)

Activity 2: Understanding MCP (20 minutes)

Activity 3: Customization (25 minutes)

Activity 4: Advanced Features (20 minutes)

📁 Project Structure

🤝 Contributing

📄 License

🆘 Support

🎉 Acknowledgments

Logging

📊 Performance

Resource Usage

Scalability

🔒 Security

Current Implementation

Production Considerations

🤝 Contributing

Code Style

📝 License

🆘 Support

🎯 Future Enhancements

Reviews