Weather Playwright

An MCP (Model Context Protocol) project demonstrating two ways to extend an LLM's context with live weather data:

• weather_USA.py — a "classic" MCP server: fetches forecasts and alerts from the US National Weather Service API (api.weather.gov).
• weather_Israel.py — an MCP server that puts the LLM's hand on the mouse 🖱️: it launches a real Chromium browser with Playwright, navigates to weather2day.co.il, types a city name into the search box, picks it from the autocomplete list — and extracts the page content so the LLM can answer the question itself (RAG).

🧩 Project Structure

├── client.py           # Generic MCP client — connects to any MCP server over stdio
├── host.py             # Terminal chat: connects Gemini to all MCP servers
├── weather_USA.py      # MCP server for US forecasts (API)
├── weather_Israel.py   # MCP server for Israeli forecasts (Playwright)
└── test_israel_flow.py # Smoke test for the full Israeli flow

The Israeli Server's Tools

🚀 Setup & Run

Prerequisites: Python 3.11+, uv.

# 1. Install dependencies
uv sync

# 2. Install Chromium for Playwright
uv run playwright install chromium

# 3. Gemini API key (free, no credit card) — https://aistudio.google.com/apikey
copy .env.example .env    # then edit: GEMINI_API_KEY=...

# 4. Run the chat
uv run host.py

Quick check of the Israeli server without an LLM:

uv run test_israel_flow.py

💬 Example Questions

• מה התחזית להיום בתל אביב? (What's today's forecast in Tel Aviv?)
• כדאי לקחת מטריה מחר בירושלים? (Should I take an umbrella tomorrow in Jerusalem?)
• מה מזג האוויר בחיפה בסוף השבוע? (What's the weather in Haifa this weekend?)
• What's the forecast in Chicago? (routed to the US server)
• Are there weather alerts in California?

While the question is being processed you'll see the browser open, type the city name, and select it from the list — then the model answers based on the page content.

⚙️ How It Works

1. The Host (host.py) launches each MCP server as a child process and opens a stdio session with it (via the generic Client in client.py).
2. The Host discovers each server's tools and attaches them to every LLM (Gemini) call.
3. When the model detects a question about weather in Israel, it invokes the four tools one after another: open browser → type city → select from list → extract content.
4. The page content comes back to the model as a tool result, and it composes an answer from it — RAG over a live web page.

🔗 Connecting to Other Hosts (e.g. ChatBox)

The MCP servers are host-agnostic. Connect one to any MCP-capable app with a command like:

uv --directory C:\path\to\project run weather_Israel.py