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style(no-slop): remove every em-dash + banned words across all modules + capstone

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Apply the no-ai-slop standard (now binding in AGENTS.md): the em-dash character is
banned outright (restructured, not blind-replaced), plus the banned word/phrase
list (delve, leverage, robust, seamless, truly, unlock, etc.). 0 em-dashes remain
in modules + capstone; the only "robust" left is the planted M10 ai-change.patch
trap. Module H1 titles use a colon separator.

All deliberate teaching devices preserved; labs compile/parse (py/sh/yaml/json);
no junk. AGENTS.md updated with the hard no-slop rules.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT
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Claude (agent)
2026-06-22 23:21:09 -04:00
parent 513d7e7ac8
commit 389ac2e460
99 changed files with 1324 additions and 1315 deletions
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modules/20-mcp-servers-giving-the-ai-hands/lab/tasks_mcp_server.py
+5 -5
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@@ -1,22 +1,22 @@
"""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 — no copy-paste, no pasting tasks.json into a chat window.
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
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 `python cli.py list`).
the same tasks.json, so a task the AI adds through this server shows up in `python cli.py list`).
Sanity-check that it starts (it will sit waiting for a client to talk to it; Ctrl-C to stop):
python tasks_mcp_server.py
You don't normally run it by hand, though. Your agentic tool launches it for you — see the lab.
You don't normally run it by hand, though. Your agentic tool launches it for you; see the lab.
"""
import json
@@ -60,6 +60,6 @@ def add_task(title: str) -> str:
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
# 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()