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