Weather And Tasks Server

Two real, runnable MCP servers for the video — one that reads the world and one that writes to it — plus a boilerplate template to build your own. No API keys. Runs on Windows, macOS, and Linux.

▶️ Video: (link at publish) · 📬 Newsletter: (link)

What's here

File	Does	Shows
weather_server.py	live weather for any city (Open-Meteo, keyless)	a read tool — your AI reaching the real world
tasks_server.py	a to-do list it can add to / complete / list	write tools — your AI taking action and changing state
server_template.py	boilerplate to copy	how to build your own
client.py	tests both servers without Claude

1. Test it in 2 minutes (no Claude needed)

Install uv (one line, any OS), then from this folder:

uv run python client.py

You'll see a read demo and a write demo:

weather_server.py  tools: ['get_weather']
  get_weather({'city': 'Tokyo'}) -> Tokyo, Japan: 21.7°C, mainly clear, wind 4.4 km/h.

tasks_server.py  tools: ['add_task', 'complete_task', 'list_tasks']
  add_task({'task': 'record episode 2'}) -> Added: 'record episode 2'  (you now have 1 task(s)).
  complete_task({'number': 1}) -> Completed: 'record episode 2'.
  list_tasks({}) -> 1. [x] record episode 2

The tasks server writes to a tasks.json right next to it — open the file and you'll see exactly what your AI changed.

2. Connect them to Claude

• macOS — ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows — %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "weather": { "command": "uv", "args": ["--directory", "ABSOLUTE/PATH/TO/THIS/FOLDER", "run", "python", "weather_server.py"] },
    "tasks":   { "command": "uv", "args": ["--directory", "ABSOLUTE/PATH/TO/THIS/FOLDER", "run", "python", "tasks_server.py"] }
  }
}

Restart Claude, then try:

• Read: "What's the weather in Tokyo right now?"
• Write: "Add 'finish the thumbnail' to my tasks." → then "What's on my list?"

3. Build your own

Open server_template.py, rename the server, and replace do_something with your tool — read a file, hit an API, write to a database. Uncomment the resource/prompt examples for those too, then point Claude at it the same way.

Read vs. write — and the one safety note

weather_server only reads. tasks_server writes (it changes tasks.json). That write power — letting your AI actually do things — is the whole point of MCP. It's also exactly why you only connect servers you trust, and why real tools add confirmations and permissions before destructive actions.

Why it's cross-platform

Pure Python + the standard library for HTTP (urllib), local files via pathlib, and sys.executable to launch servers — no OS-specific paths or shells. Only dependency: the mcp SDK.

MIT licensed. Built for the AI Makes Sense channel.