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

Related MCP server: Azure AI Agent Service MCP Server

📋 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