MCP in 45 Minutes — A Classroom Walkthrough

You've already given the theory. This is the "now let's see it" session. Two files, ~90 lines of Python total, nothing exotic.

Setup (5 min, do this before class starts)

pip install "mcp[cli]==1.29.0"

That's the only dependency. Both files run with plain python3.

Related MCP server: Demo MCP Server

The three files

About interactive_client.py

Run it with:

python3 interactive_client.py

It connects to the server, calls list_tools(), and prints whatever tools come back as a numbered menu — nothing is hardcoded, so if a student adds a new tool to study_buddy_server.py, it just shows up next time this runs. Pick a number, and for each argument the tool needs it prompts you for a value (reading the schema FastMCP built from the function's type hints — this is a nice moment to point out that the schema is not hand-written). Type q to quit.

This is the best one to leave running on a projector: it's the closest students will get to "talking to the server" without wiring up a real LLM, and it makes the discovery step (menu built live from list_tools()) visually obvious in a way the scripted client doesn't.

Suggested flow in class

1. Read the server first, run nothing yet (10 min)

Open study_buddy_server.py on the projector. Walk through it top to bottom:

• mcp = FastMCP("StudyBuddy") — creating the server and naming it.

• @mcp.tool() — point out this is the entire difference between "a Python function" and "a function an AI can discover and call." Nothing else changes.

• The docstring under each function ("""Roll a dice...""") is not just a comment — the client/model reads this text to decide when to use the tool. This is worth emphasizing: MCP tool descriptions are part of the protocol, not decoration.

• mcp.run() at the bottom — starts the server listening for a connection.

Ask students: "If I wanted to add a tool that looks up tomorrow's weather, what three things would I need to write?" (a function, a docstring, the decorator) — this checks they got the pattern.

2. Run the server alone (2 min)

python3 study_buddy_server.py

It will appear to hang / do nothing. That's the point — a server on its own is inert. It's just sitting there waiting for something to talk to it over stdin/stdout. Ctrl+C to stop.

3. Read and run the client (15 min)

Open simple_client.py. This is the file that shows the protocol steps explicitly, because normally an AI app hides them from you:

1. Launch — stdio_client(server_params) starts the server as a subprocess and opens a pipe to it.

2. Handshake — session.initialize(). Client and server introduce themselves and agree on what they support.

3. Discover — session.list_tools(). The client asks "what can you do?" and gets back the tool names + docstrings + expected inputs.

4. Call — session.call_tool("roll_dice", {"sides": 20}). The client asks the server to actually run one specific tool with specific arguments, and gets a result back.

Run it:

python3 simple_client.py

Point out: no AI model was involved anywhere in this script. We (the humans) hardcoded which tool to call. That's deliberate — it isolates "what MCP does" from "what an LLM adds on top," which is normally where the confusion is. The LLM's job in a real app is just step 4: deciding which tool to call and what arguments to pass, based on the tool descriptions from step 3 and the user's message.

4. Live-code a new tool together (10 min)

Add a fourth tool to study_buddy_server.py as a class, e.g.:

@mcp.tool()
def word_of_the_day() -> str:
    """Return a random vocabulary word with its definition."""
    words = {
        "ephemeral": "lasting for a very short time",
        "resilient": "able to recover quickly from difficulty",
        "lucid": "clear and easy to understand",
    }
    word, definition = random.choice(list(words.items()))
    return f"{word}: {definition}"

Then add one line to simple_client.py to call it, and re-run. This is the moment that usually makes it click: adding a capability took one decorator and zero protocol code.

5. Bonus, if time allows: connect it to Claude Desktop

If any students have Claude Desktop installed, show them that the same study_buddy_server.py file can be registered in Claude Desktop's config (claude_desktop_config.json) instead of simple_client.py, and then Claude itself — not a hardcoded script — decides when to roll a dice or quiz them, based on what they type in chat. This is the "aha": the server code never changes, only who's driving the client side.

Key takeaway to write on the board

MCP doesn't make the AI smarter. It gives the AI a standard way to discover and call tools it doesn't know about in advance — so anyone can plug new capabilities into any AI app without custom integration code.