In my last post, I walked through a RAG pipeline that answers questions from a company policy document. The next question I wanted to answer: what happens when I want other AI systems to use that same capability, without hardcoding a Python import?
That's what pulled me into building an MCP server. In this article, I will explain how I built a custom MCP server that exposes tools to AI agents and how this architecture enables more powerful enterprise AI applications.
What is MCP?
Model Context Protocol is an open protocol that standardizes how AI applications communicate with external tools and data sources.
Instead of creating custom integrations for every AI application, MCP provides a common interface where servers expose tools that AI clients can discover and invoke.
