Azure AI MCP Server
A mission-critical Model Context Protocol (MCP) server providing comprehensive Azure AI services integration with enterprise-grade reliability, observability, and chaos engineering capabilities.
🚀 Features
Core Azure AI Services
- Azure OpenAI: Chat completions, embeddings, and text generation
- Cognitive Services Text Analytics: Sentiment analysis, entity recognition, key phrase extraction
- Computer Vision: Image analysis, object detection, OCR
- Face API: Face detection, recognition, and analysis
- Azure Storage: Blob storage integration for data persistence
Mission-Critical Capabilities
- High Availability: Multi-region deployment with automatic failover
- Observability: Comprehensive logging, metrics, and distributed tracing
- Security: Azure AD integration, API key management, and encryption at rest/transit
- Rate Limiting: Intelligent throttling and backpressure handling
- Retry Logic: Exponential backoff with circuit breaker patterns
- Chaos Engineering: Built-in chaos testing with Azure Chaos Studio
DevOps & CI/CD
- Infrastructure as Code: Terraform modules for all environments
- Multi-Environment: Integration, E2E, and Production pipelines
- Container Support: Docker containerization with health checks
- Monitoring: Azure Monitor, Application Insights integration
- Security Scanning: Automated vulnerability assessments
📋 Prerequisites
- Node.js 18+
- Azure subscription with appropriate permissions
- Azure CLI installed and configured
- Terraform 1.5+
- Docker (optional, for containerized deployment)
🔧 Installation
1. Clone and Setup
git clone https://github.com/caiotk/nexguideai-azure-ai-mcp-server.git
cd azure-ai-mcp-server
npm install
2. Environment Configuration
Copy the environment template and configure your Azure credentials:
cp .env.example .env
Required environment variables:
# Azure OpenAI
AZURE_OPENAI_ENDPOINT=https://your-openai.openai.azure.com/
AZURE_OPENAI_API_KEY=your-api-key
# Azure Cognitive Services
AZURE_COGNITIVE_SERVICES_ENDPOINT=https://your-region.api.cognitive.microsoft.com/
AZURE_COGNITIVE_SERVICES_KEY=your-key
# Azure Storage
AZURE_STORAGE_CONNECTION_STRING=your-connection-string
# Azure AD (for production)
AZURE_TENANT_ID=your-tenant-id
AZURE_CLIENT_ID=your-client-id
AZURE_CLIENT_SECRET=your-client-secret
# Monitoring
AZURE_APPLICATION_INSIGHTS_CONNECTION_STRING=your-connection-string
LOG_LEVEL=info
3. Build and Run
npm run build
npm start
🏗️ Architecture
System Overview
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ MCP Client │────│ Azure AI MCP │────│ Azure Services │
│ │ │ Server │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
▼
┌─────────────────┐
│ Observability │
│ & Monitoring │
└─────────────────┘
Component Architecture
- API Layer: MCP protocol implementation with request validation
- Service Layer: Azure AI service integrations with retry logic
- Infrastructure Layer: Terraform modules for cloud resources
- Observability Layer: Logging, metrics, and distributed tracing
🔄 CI/CD Pipeline
Environments
- Integration (INT): Feature testing and integration validation
- End-to-End (E2E): Full system testing with chaos engineering
- Production (PROD): Live environment with blue-green deployment
Pipeline Stages
Build → Test → Security Scan → Deploy INT → E2E Tests → Chaos Tests → Deploy PROD
Deployment Strategy
- Blue-Green Deployment: Zero-downtime deployments
- Canary Releases: Gradual traffic shifting for risk mitigation
- Automated Rollback: Automatic rollback on health check failures
🧪 Testing Strategy
Test Pyramid
- Unit Tests: Individual component testing (Jest)
- Integration Tests: Service integration validation
- E2E Tests: Full workflow testing
- Chaos Tests: Resilience and failure scenario testing
Chaos Engineering
Integrated with Azure Chaos Studio for:
- Service Disruption: Simulated Azure service outages
- Network Latency: Increased response times
- Resource Exhaustion: CPU/Memory pressure testing
- Dependency Failures: External service failures
📊 Monitoring & Observability
Metrics
- Performance: Response times, throughput, error rates
- Business: API usage, feature adoption, cost optimization
- Infrastructure: Resource utilization, availability
Logging
- Structured Logging: JSON format with correlation IDs
- Log Levels: ERROR, WARN, INFO, DEBUG
- Centralized: Azure Log Analytics integration
Alerting
- SLA Monitoring: 99.9% availability target
- Error Rate Thresholds: >1% error rate alerts
- Performance Degradation: Response time anomalies
🔒 Security
Authentication & Authorization
- Azure AD Integration: Enterprise identity management
- API Key Management: Secure key rotation and storage
- RBAC: Role-based access control
Data Protection
- Encryption at Rest: Azure Storage encryption
- Encryption in Transit: TLS 1.3 for all communications
- Data Residency: Configurable data location compliance
Security Scanning
- Dependency Scanning: Automated vulnerability detection
- SAST: Static application security testing
- Container Scanning: Docker image vulnerability assessment
🚀 Deployment
Local Development
npm run dev
Docker Deployment
npm run docker:build
npm run docker:run
Terraform Deployment
cd terraform/environments/prod
terraform init
terraform plan
terraform apply
📈 Performance
Benchmarks
- Latency: P95 < 500ms for chat completions
- Throughput: 1000+ requests/minute sustained
- Availability: 99.9% uptime SLA
Optimization
- Connection Pooling: Efficient Azure service connections
- Caching: Intelligent response caching strategies
- Rate Limiting: Adaptive throttling based on service limits
🤝 Contributing
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Development Guidelines
- Follow TypeScript strict mode
- Maintain 90%+ test coverage
- Use conventional commits
- Update documentation for new features
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🆘 Support
- Documentation: docs/
- Issues: GitHub Issues
- Discussions: GitHub Discussions
🗺️ Roadmap
- Multi-model support (GPT-4, Claude, Gemini)
- Advanced caching strategies
- GraphQL API support
- Kubernetes deployment manifests
- Advanced chaos engineering scenarios
- Cost optimization recommendations
Built with ❤️ by NexGuide AI