De-slop: remove every em-dash + banned words across all modules + capstone (#94)

Co-authored-by: claude <[email protected]>
Co-committed-by: claude <[email protected]>

modules/20-mcp-servers-giving-the-ai-hands/lab/tasks_mcp_server.py

@@ -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()