Model Context Protocol Server: Deploy & Orchestrate AI Workflows

Model Context Protocol Server: Streamline AI workflows with seamless model deployment, context orchestration, and enterprise-grade scalability.

Visit Repository

✨ Cloud Platforms

4.8(77 reviews)

115 saves

53 comments

This tool saved users approximately 10187 hours last month!

About Model Context Protocol Server

What is Model Context Protocol Server: Deploy & Orchestrate AI Workflows?

The Model Context Protocol (MCP) Server is a Kubernetes-based deployment framework designed to streamline AI workflow orchestration. Built for Azure Kubernetes Service (AKS), it provides a structured approach to manage AI models and services through containerized deployments, environment configuration, and scalable infrastructure. The solution uses Docker, Node.js 18+, and Kubernetes manifests to ensure seamless integration with cloud environments.

How to Use Model Context Protocol Server: Deploy & Orchestrate AI Workflows?

Follow these core steps to deploy the MCP Server:
1. Build a Docker image with docker build -t mcp-server:latest .
2. Push the image to Azure Container Registry using Azure CLI commands
3. Deploy Kubernetes resources via kubectl apply -f k8s/
4. Validate the setup with pod and service status checks

Model Context Protocol Server Features

Key Features of Model Context Protocol Server: Deploy & Orchestrate AI Workflows?

Environment variables managed through ConfigMap configuration
Automated health checks via exposed /health endpoints
Integrated Azure Monitor for resource usage tracking
Security features including HTTPS, CORS protection, rate limiting, and Helmet.js headers
Horizontal scaling capabilities with default 3 replicas

Use Cases of Model Context Protocol Server: Deploy & Orchestrate AI Workflows?

Typical applications include:
• Coordinating multi-service AI pipelines in production environments
• Managing model versioning and context dependencies at scale
• Automating failover processes through Kubernetes health monitoring
• Securing API access while maintaining cross-origin compatibility

Model Context Protocol Server FAQ

FAQ from Model Context Protocol Server: Deploy & Orchestrate AI Workflows?

How do I check deployment status?
Use kubectl get pods and kubectl get services
Can I customize security settings?
Edit the ConfigMap to adjust rate limits or CORS policies
What logging options are available?
Application logs are accessible via kubectl logs and Azure Monitor
How do I scale instances?
Run kubectl scale deployment mcp-server --replicas=X where X is your desired count

Content

Model Context Protocol (MCP) Server

This repository contains the Kubernetes deployment configuration for the MCP server on Azure Kubernetes Service (AKS).

Prerequisites

Azure CLI
kubectl
Docker
Node.js 18+

Project Structure

.
├── k8s/
│   ├── deployment.yaml    # Kubernetes deployment configuration
│   ├── service.yaml      # Kubernetes service configuration
│   └── configmap.yaml    # Kubernetes configmap for environment variables
├── src/                  # Source code directory
├── Dockerfile           # Container build configuration
├── package.json         # Node.js dependencies
└── tsconfig.json        # TypeScript configuration

Deployment Steps

Build the Docker image:

docker build -t mcp-server:latest .

Push the image to Azure Container Registry (ACR):

az acr login --name <your-acr-name>
docker tag mcp-server:latest <your-acr-name>.azurecr.io/mcp-server:latest
docker push <your-acr-name>.azurecr.io/mcp-server:latest

Apply Kubernetes manifests:

kubectl apply -f k8s/

Verify deployment:

kubectl get pods
kubectl get services

Configuration

The application can be configured through environment variables defined in the ConfigMap (k8s/configmap.yaml).

Health Checks

The application exposes a /health endpoint for Kubernetes health checks.

Monitoring

Resource usage can be monitored through Azure Monitor
Application logs are available through kubectl logs

Security

The application uses HTTPS
CORS is configured for secure cross-origin requests
Rate limiting is implemented to prevent abuse
Helmet.js is used for security headers

Scaling

The deployment is configured with 3 replicas by default. You can scale up or down using:

kubectl scale deployment mcp-server --replicas=<number>