Deploying an MCP server requires three main components. You need a host environment to run the server process. You need the MCP server code that exposes your tools. You need a client connection from your AI model to the server.
MCP servers run as standalone processes. The server listens for JSON-RPC messages from MCP clients. When a request arrives, the server executes the requested tool and returns results.
Start by choosing your MCP server implementation. Anthropic provides reference servers in Python and TypeScript. Community developers have created servers in Go, Rust, and other languages. Select the implementation that matches your infrastructure and expertise.
Install the required runtime for your chosen server. Python servers need Python 3.8 or higher. TypeScript servers need Node.js 18 or higher. Verify your environment meets these requirements before proceeding.
Clone the MCP server repository to your deployment location. Install dependencies using pip for Python or npm for Node.js. Configure environment variables for authentication, data sources, and tool settings.
Test the server locally before deploying to production. Run the server process and verify it starts without errors. Connect an MCP client to confirm tool discovery works correctly.
Configure your MCP client to connect to the server. Claude Desktop uses a configuration file at ~/.config/claude/claude_desktop_config.json. Add your server details to this file with the correct command and arguments.
The client spawns the server process when needed. For remote deployments, configure the client to connect via network sockets instead of stdio. The server must accept incoming connections on the specified port.
Azure deployment options include Virtual Machines, Container Instances, and App Service. Each option provides different tradeoffs for cost, scalability, and management overhead.
Azure Virtual Machines offer full control over the server environment. Create a VM with Ubuntu 22.04 or your preferred Linux distribution. Choose a VM size based on your workload. B-series VMs work well for development and testing.
Connect to your VM via SSH. Install the required runtime for your MCP server. Copy your server code to the VM using git clone or secure file transfer.
Configure the MCP server to listen on a network port instead of stdio. Azure requires network-based communication for client connections. Update your server configuration to bind to 0.0.0.0 on your chosen port.
Create a Network Security Group rule to allow inbound traffic on your server port. Restrict access to specific IP addresses for security. Your MCP client needs network access to reach the server.
Set up systemd to run your MCP server as a service. Create a service file in /etc/systemd/system/. Configure automatic restarts and logging. Enable the service to start on boot.
Azure Container Instances provide a simpler deployment path. Create a Dockerfile for your MCP server. Build and push the image to Azure Container Registry. Deploy the container with the required port mappings.
Container deployments simplify updates and scaling. Push a new image to update your server. Azure handles container orchestration and networking.
Azure App Service supports Docker containers and direct code deployment. For Node.js MCP servers, deploy directly from a Git repository. App Service handles runtime provisioning and scaling automatically.
Configure application settings in the Azure portal. Set environment variables for your MCP server configuration. Enable continuous deployment for automated updates from your source repository.
AWS provides multiple deployment options including EC2, ECS, Lambda, and Lightsail. The right choice depends on your traffic patterns and operational preferences.
AWS EC2 provides traditional VM deployment. Launch an EC2 instance with Amazon Linux 2023 or Ubuntu. Select an instance type based on your workload. t3.micro works for light usage.
Connect to your EC2 instance using SSH or AWS Systems Manager Session Manager. Install your MCP server runtime and dependencies. Configure security groups to allow inbound connections from your MCP client.
Store your MCP server configuration in AWS Systems Manager Parameter Store. Reference these parameters in your startup script. This approach keeps sensitive data out of your code repository.
Use an Elastic IP for stable connectivity. Associate the Elastic IP with your instance. Update your MCP client configuration with this address.
AWS ECS provides container orchestration. Create a task definition that specifies your container image and resources. Define port mappings for client connections.
Push your MCP server Docker image to Amazon ECR. Create an ECS service that runs your task definition. Configure an Application Load Balancer if you need multiple server instances.
ECS Fargate removes server management entirely. Define your container requirements and AWS provisions the environment. Fargate handles scaling and availability.
AWS Lambda supports MCP servers for event-driven workloads. Package your server code as a Lambda function. Configure a Lambda function URL for HTTP access. Lambda works best for intermittent tool execution.
Lambda functions have a 15-minute limit. Long-running MCP operations may timeout. Use Lambda for lightweight tasks and EC2 or ECS for continuous workloads.
AWS Lightsail offers simplified VPS hosting. Create a Lightsail instance with your preferred blueprint. Install your MCP server using the same process as EC2. Lightsail provides predictable monthly pricing.
noBGP eliminates traditional cloud deployment complexity. The noBGP MCP server provisions infrastructure through natural language instructions. Your AI model creates compute nodes without manual setup.
Tell your AI model to deploy an MCP server. The model uses noBGP to provision a node with your required specifications. The noBGP agent installs automatically on the node.
The model installs your MCP server code on the node. Dependencies install automatically based on your configuration. The server starts and becomes accessible through a secure proxy URL.
No public IPs. No security groups. No VPNs. noBGP handles all networking through encrypted tunnels.
Share your MCP server using generated URLs. Control access through authentication requirements. Enable or disable services through quick instructions to your AI model.
Update your MCP server by telling your AI model to deploy a new version. The model updates the server while maintaining connectivity. Zero-downtime updates occur through automated orchestration.
Scale your MCP infrastructure by requesting additional nodes. The model provisions new compute resources and configures distribution. Reduce resources with a simple command when demand drops.
Traditional MCP deployment requires networking skills, security expertise, and ongoing maintenance. noBGP deployment only requires describing what you need. The system manages everything else.
