MCP Configuration and Tool Distribution
Explore the process of configuring Model Context Protocol (MCP) servers to distribute tools efficiently across multiple agents, applications, or users. Understand how the MCP architecture separates the server, transport, and client roles, and learn how to write a minimal MCP server using Python. Discover how to register and configure MCP servers within Claude Code and how this infrastructure supports consistent tool behavior without changing Claude's reasoning. This lesson prepares you to manage tool distribution, handle structured errors, and avoid tool name conflicts in production-ready AI systems.
In the previous two lessons, we defined tools inline: a TOOLS list passed directly to client.messages.create(). That pattern works well for a single agent in a single codebase. The Model Context Protocol (MCP) solves a different problem: how do you expose the same set of tools to Claude across multiple agents, applications, or users without duplicating the tool definitions in every caller?
MCP defines a standard protocol for a server process to advertise tools and for a Claude-compatible client to discover and invoke them. From Claude's perspective, an MCP-registered tool behaves identically to an inline tool. The behavioral contract principles from the previous lessons apply without change. What changes is where the tool lives and how the client learns about it. By the end of this lesson, we will be able to:
Explain the relationship between an MCP server, a transport, and the Claude client
Write a minimal MCP server that exposes one tool
Configure an MCP server in Claude Code settings
Identify what changes and what stays the same when moving from inline tools to MCP
The MCP architecture
An MCP setup has three parts: the server, the transport, and the ...
