Azure AI MCP Server

# 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 ```bash 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: ```bash cp .env.example .env ``` Required environment variables: ```env # 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 ```bash 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 1. **Integration (INT)**: Feature testing and integration validation 2. **End-to-End (E2E)**: Full system testing with chaos engineering 3. **Production (PROD)**: Live environment with blue-green deployment ### Pipeline Stages ```yaml 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 ```bash npm run dev ``` ### Docker Deployment ```bash npm run docker:build npm run docker:run ``` ### Terraform Deployment ```bash 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 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 ### 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](LICENSE) file for details. ## 🆘 Support - **Documentation**: [docs/](./docs/) - **Issues**: [GitHub Issues](https://github.com/caiotk/nexguideai-azure-ai-mcp-server/issues) - **Discussions**: [GitHub Discussions](https://github.com/caiotk/nexguideai-azure-ai-mcp-server/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**