AI Agents with MCP

Appendix. A Refresher on Decorators

Decorators are a bit of syntactic sugar in Python that allow you to modify the behavior of a function without changing the code of the function itself. They work because functions are first-class objects in Python, which means, among other things, that you can pass functions as arguments to other functions. So when you decorate a function, the decorated function is passed as an argument to the decorator function, which then will call the decorated function at some point during the execution of the rest of its logic.

This is done by defining a wrapper() or handler() function within the body of the decorator function definition that does its logic and calls the function that it was passed, and that inner function is returned by the outer (decorator) function. (The name of the inner function doesn’t matter; wrapper() and handler() are just common conventions.)

In the MCP Python SDK, the MCPServer API’s tool() decorator is a great example of this. Prior to v2, the low-level server also used decorators like @server.call_tool() to wrap your tool functions in nested handler functions and add them to a handler dictionary. Now, the low-level server constructor takes handler functions directly as parameters.

Since the v2 SDK update, the high-level server API’s tool decorator has only a single inner function called decorator(). All it does is take a function as an argument and then call add_tool() on the server instance, which adds the tool definition ...