Member-only story
Model Context Protocol (MCP) Explained
How to build a custom MCP server with Python
This is the 5th article in a larger series on AI agents. LLMs, tools, and context are the key ingredients of agentic systems. However, gluing these components together becomes increasingly cumbersome as more models, apps, and libraries are released. In this article, we’ll discuss how Model Context Protocol (MCP) solves this problem by providing us a standard way to connect LLMs to tools and context. I’ll start with an overview of the key concepts, then share Python code that implements an MCP server.
MCP is a standard way to connect models to tools and context. Anthropic describes it as the “USB-C port of AI apps”. Rather than every AI app developer creating their own custom way of plugging in tools and data, MCP provides a standard that everyone can use [1].
This is valuable because it unlocks the power of community-driven development. Now, apps and tools from different groups can easily talk to one another using a standard process.
