The Model Context Protocol (MCP) is changing how we build software. It provides the “API” for large language models (LLMs) to interact with the real world. This lets an AI agent query a database, read a file, or call a third-party service. This new capability brings new challenges. MCP servers, the back-end tools the AI uses, are not traditional microservices. Their user is a non-deterministic AI, and they often need access to sensitive systems. 

How do we build, deploy, and secure these servers reliably? The clear answer is Docker. The entire MCP ecosystem, including Docker’s own MCP Toolkit and Catalog, is built around containerization. Running your MCP servers in Docker is not just a good idea; it is a necessary best practice. This article covers five key principles for building production-ready, Dockerized MCP servers.