Create and Deploy a FastMCP Server on macOS

The Model Context Protocol (MCP) lets AI clients (Claude Desktop, Claude Code, or your own agent) call tools, read resources, and load prompts from an external server. FastMCP is a Python framework that turns ordinary typed functions into a compliant MCP server with a couple of decorators. Most FastMCP tutorials stop at “run it over stdio.” This one goes further: you build a small server, then deploy it as a persistent background service on your Mac using launchd, the same supervisor macOS uses for its own daemons. The result is an HTTP MCP server that starts at login, restarts if it crashes, and writes logs you can tail — managed entirely through make. ...

15 min

Build an MCP Client with FastMCP and Python

A Model Context Protocol (MCP) server exposes tools, resources, and prompts; an MCP client is what connects to that server, discovers those capabilities, and calls them. Clients are usually embedded in an AI application (Claude Desktop, Claude Code, or your own agent), but writing a standalone client is the clearest way to understand the protocol and to script, test, or debug a server. In this tutorial you will build a command-line MCP client with FastMCP. It connects to any MCP server over stdio, prints the server’s advertised tools, resources, and prompts, and calls a tool with JSON arguments. You will run it against a small demo server, then cover it with pytest using FastMCP’s in-memory transport — no subprocess required. ...

13 min

Build an MCP Server with FastMCP and Python

The Model Context Protocol (MCP) is an open standard that lets AI clients (such as Claude Desktop, Claude Code, or your own agent) call tools, read resources, and load prompts from an external server over a well-defined wire protocol. FastMCP is a Python framework that hides the protocol plumbing behind plain decorators, so you write ordinary typed functions and FastMCP turns them into a compliant server. In this tutorial you will build a small but realistic “notes” MCP server: it exposes tools to create and search notes, a resource to read a single note by URI, and a prompt that asks the model to summarize a note. You will run it over stdio, inspect it interactively, and cover it with pytest using FastMCP’s in-memory client. ...

13 min