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

2. Environment Configuration

Copy the environment template and configure your Azure credentials:

Required environment variables:

3. Build and Run

🏗️ Architecture

System Overview

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

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

Docker Deployment

Terraform Deployment

📈 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 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