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Most current systems (default Debian/Ubuntu, recent macOS) install Python only as `python3`, with no bare `python` on PATH, so learners who copied `python cli.py ...` into their host shell hit "command not found". Convert host-shell `python <cmd>` -> `python3 <cmd>` across module/lab READMEs, lab `.py` docstrings & usage strings, blog posts, lab prompt and instruction files, the M04 verify.sh message, and the M10/M24 lab patches. Module 01's convention note (and its blog/02 mirror) is rewritten so `python3` is canonical and `python` is the documented fallback. Stop-lines respected: Docker image tags (`python:3.12-slim`), `.venv/.../python` and `...\.venv\Scripts\python.exe` paths, the M20 `"command": "python"` teaching example and surrounding venv prose, container-internal invocations (M16/M18 Dockerfiles, M16 README `docker run` examples), and CI-workflow `run:` steps fed by `actions/setup-python` / `image: python:3.12` are left as `python` on purpose. pip was left out of scope: most occurrences are prose or CI/container-internal, and `pip3` does not fix the PEP 668 externally-managed-environment refusal that the course already addresses with venvs. The M01 note is worded to stay consistent with bare `pip` (use whichever pip pairs with your Python). Build (tools/build_wiki.py) and tools/check.sh both pass. Closes #104 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01GAEzanEoGJT5o1VizQar47
66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
"""A tiny MCP server that gives an AI client hands on the tasks-app.
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It exposes the tasks-app over the Model Context Protocol (MCP) so an agentic tool can read and
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change your real task list directly, with no copy-paste and no pasting tasks.json into a chat window.
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The whole server is the decorated functions below. FastMCP (from the official Python SDK) turns
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each `@mcp.tool()` function into a tool the AI client can discover and call. That's it: a tool is
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a normal Python function plus a docstring the client reads to know what it does.
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Setup (once):
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pip install "mcp[cli]"
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Drop this file into your tasks-app folder, next to tasks.py and cli.py (it reuses them, and shares
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the same tasks.json, so a task the AI adds through this server shows up in `python3 cli.py list`).
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Sanity-check that it starts (it will sit waiting for a client to talk to it; Ctrl-C to stop):
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python3 tasks_mcp_server.py
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You don't normally run it by hand, though. Your agentic tool launches it for you; see the lab.
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"""
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import json
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from pathlib import Path
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from mcp.server.fastmcp import FastMCP
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from tasks import Task, TaskList
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STATE = Path(__file__).parent / "tasks.json"
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# The name is how the server identifies itself to the client.
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mcp = FastMCP("tasks")
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def _load() -> TaskList:
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if not STATE.exists():
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return TaskList()
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raw = json.loads(STATE.read_text())
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return TaskList(tasks=[Task(**t) for t in raw])
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def _save(tlist: TaskList) -> None:
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STATE.write_text(json.dumps([t.__dict__ for t in tlist.tasks], indent=2))
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@mcp.tool()
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def list_tasks() -> str:
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"""List every task in the tasks-app, with its index and whether it's done."""
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return _load().render()
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@mcp.tool()
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def add_task(title: str) -> str:
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"""Add a new task to the tasks-app. `title` is the text of the task to add."""
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tlist = _load()
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tlist.add(title)
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_save(tlist)
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return f"added: {title}"
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if __name__ == "__main__":
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# stdio transport by default: the client launches this process and talks to it over
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# stdin/stdout. That's why the server "just sits there" when you run it by hand: it's
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# waiting for a client on the other end of the pipe.
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mcp.run()
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