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ai-workflow-course/modules/20-mcp-servers-giving-the-ai-hands/lab/tasks_mcp_server.py
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Use python3 as the canonical command name course-wide (#104) (#105)
2026-06-23 20:25:05 -04:00

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Python

"""A tiny MCP server that gives an AI client hands on the tasks-app.
It exposes the tasks-app over the Model Context Protocol (MCP) so an agentic tool can read and
change your real task list directly, with no copy-paste and no pasting tasks.json into a chat window.
The whole server is the decorated functions below. FastMCP (from the official Python SDK) turns
each `@mcp.tool()` function into a tool the AI client can discover and call. That's it: a tool is
a normal Python function plus a docstring the client reads to know what it does.
Setup (once):
pip install "mcp[cli]"
Drop this file into your tasks-app folder, next to tasks.py and cli.py (it reuses them, and shares
the same tasks.json, so a task the AI adds through this server shows up in `python3 cli.py list`).
Sanity-check that it starts (it will sit waiting for a client to talk to it; Ctrl-C to stop):
python3 tasks_mcp_server.py
You don't normally run it by hand, though. Your agentic tool launches it for you; see the lab.
"""
import json
from pathlib import Path
from mcp.server.fastmcp import FastMCP
from tasks import Task, TaskList
STATE = Path(__file__).parent / "tasks.json"
# The name is how the server identifies itself to the client.
mcp = FastMCP("tasks")
def _load() -> TaskList:
if not STATE.exists():
return TaskList()
raw = json.loads(STATE.read_text())
return TaskList(tasks=[Task(**t) for t in raw])
def _save(tlist: TaskList) -> None:
STATE.write_text(json.dumps([t.__dict__ for t in tlist.tasks], indent=2))
@mcp.tool()
def list_tasks() -> str:
"""List every task in the tasks-app, with its index and whether it's done."""
return _load().render()
@mcp.tool()
def add_task(title: str) -> str:
"""Add a new task to the tasks-app. `title` is the text of the task to add."""
tlist = _load()
tlist.add(title)
_save(tlist)
return f"added: {title}"
if __name__ == "__main__":
# stdio transport by default: the client launches this process and talks to it over
# stdin/stdout. That's why the server "just sits there" when you run it by hand: it's
# waiting for a client on the other end of the pipe.
mcp.run()