Introduction Model Context Protocol, usually called MCP, has quickly become one of the most important ideas in AI application development. It gives AI tools and agents a standard way to connect to external systems such as filesystems, GitHub, databases, browsers, documentation, and internal APIs. The protocol is useful because it gives agents a common tool interface. Instead of every AI application inventing its own way to call tools, MCP creates a shared pattern for exposing capabilities. However, the protocol is only one part of the story. The real pain starts when developers need to run multiple MCP servers locally. One server may need Node.js, another may need Python, another may need browser dependencies, and another may need OAuth or API keys. Suddenly, your agent is not just an AI workflow. It is a small distributed system running on your laptop. Docker MCP Toolkit tries to solve that operational problem. It does not replace MCP, and it does not make your agent intelligent by itself.…