Theta-MCP: AI-Powered Sales Assistant

A comprehensive sales automation platform combining Model Context Protocol (MCP) server with Gemini AI voice interface, featuring 13+ integrated sales tools and AWS deployment capabilities.

🚀 Features

Core Capabilities

• Voice Interface: Gemini AI-powered speech-to-text and text-to-speech
• MCP Server: Model Context Protocol server with extensive tool integration
• Real-time Processing: WebSocket-based voice communication
• AWS Deployment: Production-ready with ECS Fargate and auto-scaling

Integrated Sales Tools

• CRM: HubSpot, Salesforce integration
• Communication: Gmail, Google Meet, Twilio SMS
• Lead Generation: LinkedIn Sales Navigator, Apollo
• Data Management: Google Sheets, Google Drive
• Payments: Stripe integration
• Scheduling: Calendly automation
• Search: Google Search API

🏗️ Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Voice Client  │◄──►│  Gemini AI TTS   │◄──►│   MCP Server    │
│   (WebSocket)   │    │    Interface     │    │  (13+ Tools)    │
└─────────────────┘    └──────────────────┘    └─────────────────┘

🛠️ Quick Start

Local Development

# Clone repository
git clone <repository-url>
cd Theta-MCP

# Setup environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

# Configure settings
cp config/settings.example.json config/settings.json
# Add your API keys to config/settings.json

# Run locally
python ./deployment/test_local.py

Production Deployment

# Setup AWS deployment
./deployment/setup_aws.sh

# Deploy to AWS ECS
./deployment/aws/deploy.sh

📁 Project Structure

Theta-MCP/
├── sales_mcp_server.py          # Main MCP server
├── gemini_tts_interface.py      # Voice interface with Gemini AI
├── health_check.py              # Health monitoring
├── refresh_google_token.py      # Token management
├── config/                      # Configuration files
│   ├── google_auth.py          # Google authentication
│   ├── settings.py             # Settings loader
│   └── settings.example.json   # Configuration template
├── tools/                       # Sales automation tools
│   ├── hubspot_tool.py         # HubSpot CRM integration
│   ├── salesforce_tool.py      # Salesforce integration
│   ├── gmail_tool.py           # Gmail automation
│   ├── linkedin_tool.py        # LinkedIn Sales Navigator
│   ├── apollo_tool.py          # Lead generation
│   ├── stripe_tool.py          # Payment processing
│   ├── calendly_tool.py        # Scheduling automation
│   └── ... (13+ tools total)
├── deployment/                  # Deployment configurations
│   ├── aws/                    # AWS-specific files
│   ├── docker/                 # Docker configurations
│   ├── setup_aws.sh           # AWS setup script
│   └── test_local.py          # Local testing
└── tests/                      # Test suite

🔧 Configuration

Required API Keys

• Google Cloud (Speech-to-Text, Text-to-Speech, Calendar, Gmail)
• Gemini AI API key
• HubSpot, Salesforce, LinkedIn, Apollo (as needed)
• AWS credentials (for deployment)

Environment Variables

Copy .env.example to .env and configure:

GOOGLE_CLOUD_PROJECT=your-project
GEMINI_API_KEY=your-gemini-key
HUBSPOT_API_KEY=your-hubspot-key
# ... additional API keys

🚀 AWS Deployment

Infrastructure

• ECS Fargate: Serverless container orchestration
• Application Load Balancer: Traffic distribution
• Auto Scaling: 2-10 instances based on demand
• EFS Storage: Persistent token and log storage
• Secrets Manager: Secure API key management
• CloudWatch: Monitoring and logging

Deployment Process

1. Configure AWS credentials
2. Run ./deployment/setup_aws.sh
3. Execute ./deployment/aws/deploy.sh
4. Access via provided ALB endpoint

🧪 Testing

# Run test suite
python -m pytest tests/

# Test local deployment
python ./deployment/test_local.py

# Health check
curl http://localhost:8000/health

📚 API Documentation

MCP Server Endpoints

• GET /health - Health check
• POST /tools/{tool_name} - Execute tool
• WebSocket /voice - Voice interface

Voice Interface

• Real-time speech-to-text processing
• Gemini AI conversation handling
• Text-to-speech response generation
• WebSocket-based communication

🤝 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

📄 License

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

🆘 Support

For issues and questions:

• Create an issue in this repository
• Check the deployment guide: ./deployment/README.md
• Review test configurations: ./tests/README.md

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Built with ❤️ using Python, FastAPI, Gemini AI, and AWS