De-slop: remove every em-dash + banned words across all modules + capstone (#94)
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Co-authored-by: claude <claude@jpaul.io> Co-committed-by: claude <claude@jpaul.io>
This commit was merged in pull request #94.
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@@ -1,22 +1,22 @@
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"""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 — no copy-paste, no pasting tasks.json into a chat window.
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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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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 `python cli.py list`).
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the same tasks.json, so a task the AI adds through this server shows up in `python 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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python 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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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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@@ -60,6 +60,6 @@ def add_task(title: str) -> str:
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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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# 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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