sample -automation

This MCP server acts as an automation hub that can fetch MCP servers, build Docker images, and prepare deployments to AWS ECS. It leverages the MCP Proxy ecosystem to translate local MCP stdio servers into remotely deployable containers. The sample setup demonstrates how to run and automate MCP servers directly via command line, build multi-architecture images, and optionally push to ECR or generate deployment artifacts. By using the direct command mode, you can rapidly package a server without creating a YAML config, while the config file mode allows you to script complex builds and ECS deployments with precise network and resource configurations.

Prerequisites:

• Docker: ensure the daemon is running
• Node.js and npm (for Node-based MCP servers) or Python environment if required by the server stack
• AWS CLI: configured with appropriate permissions
• uv (for Python-based workflows) if you intend to use uv-based tooling
• Access to an AWS ECR repository and an ECS cluster if you plan to push and deploy

Install uv (example for macOS/Linux):

curl -LsSf https://astral.sh/uv/install.sh | sh

Install uv on Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Optional: Install Docker and authenticate to AWS if you plan to push images to ECR and deploy to ECS. After prerequisites, you can start using the sample as shown in the Quick Start section of the repo README.

Notes and tips:

• For multi-arch builds, you can use the --arch option when running in direct command mode to target linux/amd64, linux/arm64, etc.
• If the MCP server README only contains Docker commands or lacks a clear command, you can override the detected command via a config file (command_override) to ensure correct packaging.
• When pushing to ECR, ensure your AWS credentials and repository exist and that you’ve configured the appropriate region (aws_region) if not using the default profile.
• The mcp-config.json can be generated automatically by the tooling when you enable save_config in the deploy section of a YAML config; you can use this to provide Claude Desktop/Cline client configurations as needed.
• Debug logging can be enabled to troubleshoot failures during fetch/build/deploy sequences.