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MCP Business AI Transformation

by DemoDaygit
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TypeScript
Remote

MCP Business AI Transformation

Enterprise-grade MCP (Model Context Protocol) server with multi-agent system for business AI transformation.

🏗️ Architecture Overview

┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Agent Layer │◄──►│ MCP Gateway │◄──►│ Business APIs │ │ (Orchestrator) │ │ (Protocol Hub) │ │ (External) │ └─────────────────┘ └──────────────────┘ └─────────────────┘ │ │ │ ▼ ▼ ▼ ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ LLM Fabric │ │ State Manager │ │ Monitoring Hub │ │ (Multi-Model) │ │ (Redis+Postgres) │ │ (Observability) │ └─────────────────┘ └──────────────────┘ └─────────────────┘

🚀 Features

Core MCP Server

  • FastAPI-based high-performance server

  • MCP Protocol compliant (2024-11-05 spec)

  • Multi-provider LLM support (Evolution Foundation Models, OpenAI, HuggingFace)

  • Circuit breaker pattern for external API resilience

  • Rate limiting with Redis-based sliding window

  • JWT & API Key authentication

  • Prometheus metrics and OpenTelemetry tracing

Multi-Agent System

  • Specialized Agents: Data Analyst, API Executor, Business Validator, Report Generator

  • Agent Registry for dynamic agent management

  • Message Bus for inter-agent communication

  • Task Orchestration with intelligent agent selection

  • LangChain/LlamaIndex integration for advanced AI capabilities

Enterprise Features

  • Real-time Dashboard with React + TypeScript

  • Business Domain Support: Finance, Healthcare, Retail, Manufacturing, Technology

  • Observability Stack: Prometheus, Grafana, Jaeger

  • Docker Compose for easy deployment

  • Production-ready with security best practices

🛠️ Technology Stack

Frontend

  • Next.js 15 with App Router

  • TypeScript 5 for type safety

  • Tailwind CSS 4 with shadcn/ui components

  • Real-time updates with WebSocket support

Backend

  • Python 3.11 with FastAPI

  • PostgreSQL for persistent storage

  • Redis for caching and rate limiting

  • AsyncIO for high concurrency

AI/ML

  • Evolution Foundation Models (Cloud.ru)

  • OpenAI API compatibility

  • LangChain for agent orchestration

  • LlamaIndex for data indexing

DevOps

  • Docker containerization

  • Prometheus monitoring

  • Grafana dashboards

  • Jaeger distributed tracing

📦 Quick Start

Prerequisites

  • Docker & Docker Compose

  • Node.js 18+ (for local development)

  • Python 3.11+ (for local development)

Environment Configuration

Create a .env file:

# API Keys EVOLUTION_API_KEY=your_evolution_api_key OPENAI_API_KEY=your_openai_api_key HUGGINGFACE_API_KEY=your_huggingface_api_key # Security SECRET_KEY=your-super-secret-key-change-in-production # Database (optional, defaults work with Docker) DATABASE_URL=postgresql+asyncpg://postgres:password@localhost:5432/mcp_db REDIS_URL=redis://localhost:6379

Start the System

# Start all services docker-compose up -d # View logs docker-compose logs -f # Stop services docker-compose down

Access Points

🔧 Development

Local Development Setup

Backend (MCP Server)

cd mcp_server python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Agent System

cd agent_system python -m venv venv source venv/bin/activate pip install -r requirements.txt python main.py

Frontend

npm install npm run dev

Project Structure

├── src/ # Next.js frontend │ ├── app/ # App Router pages │ ├── components/ # React components │ └── lib/ # Utility functions ├── mcp_server/ # FastAPI MCP server │ ├── app/ # Application code │ │ ├── api/v1/ # API endpoints │ │ ├── core/ # Core services │ │ └── middleware/ # Custom middleware │ └── tests/ # Test suite ├── agent_system/ # Multi-agent system │ ├── core/ # Agent framework │ ├── agents/ # Specialized agents │ └── llm/ # LLM providers ├── docker-compose.yml # Multi-service deployment └── docs/ # Documentation

📊 API Usage

MCP Protocol

The server implements the MCP protocol for tool and resource management:

# Initialize connection curl -X POST http://localhost:8000/mcp \ -H "Content-Type: application/json" \ -d '{ "jsonrpc": "2.0", "id": 1, "method": "initialize", "params": { "protocolVersion": "2024-11-05", "capabilities": {} } }' # List available tools curl -X POST http://localhost:8000/mcp \ -H "Content-Type: application/json" \ -d '{ "jsonrpc": "2.0", "id": 2, "method": "tools/list" }' # Execute a tool curl -X POST http://localhost:8000/mcp \ -H "Content-Type: application/json" \ -d '{ "jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": { "name": "financial_analyzer", "arguments": { "data": {...} } } }'

REST API

# Create a business task curl -X POST http://localhost:8000/api/v1/resources/tasks \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_JWT_TOKEN" \ -d '{ "title": "Financial Analysis Q4", "description": "Analyze quarterly financial data", "domain": "finance", "priority": "high" }' # Get system status curl -X GET http://localhost:8000/api/v1/admin/system/status # Health check curl -X GET http://localhost:8000/api/v1/health

🔍 Monitoring & Observability

Metrics

  • Request latency and throughput

  • Agent performance and task completion rates

  • LLM token usage and costs

  • External API success rates and circuit breaker status

Tracing

  • Distributed tracing with Jaeger

  • Request correlation IDs

  • Agent communication tracing

Logging

  • Structured logging with correlation IDs

  • Log levels: DEBUG, INFO, WARNING, ERROR

  • JSON format for easy parsing

🔒 Security

Authentication

  • JWT tokens for user authentication

  • API keys for service-to-service communication

  • Rate limiting per user/API key

Authorization

  • Role-based access control (RBAC)

  • Resource-level permissions

  • CORS configuration

Data Protection

  • Input validation and sanitization

  • SQL injection prevention

  • XSS protection headers

🚀 Deployment

Production Deployment

# Set production environment variables export NODE_ENV=production export DEBUG=false # Deploy with production configurations docker-compose -f docker-compose.yml -f docker-compose.prod.yml up -d

Cloud.ru Evolution AI Agents

The system is designed to deploy on Cloud.ru Evolution AI Agents platform:

  1. Container Registry: Push Docker images to Cloud.ru registry

  2. AI Agent Configuration: Configure agent endpoints and API keys

  3. Load Balancing: Set up load balancer for high availability

  4. Monitoring: Configure Cloud.ru monitoring integration

🤝 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.

🆘 Support

  • Documentation: Check the /docs directory

  • API Docs: Visit http://localhost:8000/docs

  • Issues: Create an issue on GitHub

  • Discussions: Join our GitHub Discussions

🗺️ Roadmap

Phase 1: Core Infrastructure ✅

  • MCP Server implementation

  • Multi-agent system

  • LLM provider integration

  • Basic monitoring

Phase 2: Advanced Features (In Progress)

  • Advanced agent orchestration

  • Custom tool development framework

  • Advanced analytics and reporting

  • Multi-tenancy support

Phase 3: Enterprise Features (Planned)

  • Advanced security features

  • Compliance certifications

  • Advanced monitoring and alerting

  • Performance optimization

Phase 4: AI/ML Enhancements (Future)

  • Custom model training

  • Advanced prompt engineering

  • Multi-modal AI capabilities

  • AutoML integration

Built with ❤️ for enterprise AI transformation

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