My recent journey into agentic developer systems has been driven by a desire to understand how AI moves from passive assistance to active participation in software workflows. In an earlier article, AI Co-creation in Developer Debugging Workflows, I explored how developers and AI systems collaboratively reason about code. As I went deeper into this space, I came across the Model Context Protocol (MCP) and became keen to understand what this component is and why it is important. I noticed that MCP was frequently referenced in discussions about agentic systems, yet rarely explained in a concrete, developer-centric way. This article is a direct outcome of that learning process, using a practical Git workflow example to clarify the role and value of MCP in intent-driven developer tooling.
What Is an MCP Server?
At a conceptual level, an MCP server acts as a control plane between an AI assistant and external systems. Rather than allowing an LLM to issue arbitrary API calls, the MCP server implements the Model Context Protocol and exposes a constrained, well-defined set of capabilities that the model can invoke.