MCP

The authors demonstrate how a tightly coupled Python data‑processing pipeline can be refactored into a set of independently deployable Model Context Protocol (MCP) services, enabling each component to run in isolation while preserving the overall workflow. By exposing each stage as an MCP service, the architecture gains modularity, allowing teams to scale, update, or replace parts of the pipeline without disrupting the entire system. The approach highlights the trade‑off between the simplicity of a single monolithic process and the operational benefits of distributed services, such as improved fault isolation and easier continuous deployment.