Weather-MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Weather-MCP What's the weather forecast in Tel Aviv?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
š¤ļø ā Israel Weather Forecast
A learning project in MCP (Model Context Protocol) development: an LLM-based agent that can fetch weather forecasts for cities in Israel and the US, and answer in free language in the terminal chat.
šÆ Project Goal
Build a dedicated MCP server that "teaches" the LLM to perform a task it can't do on its own ā fetch a weather forecast from an Israeli site that has no API.
The agent does this in two ways:
Israel server (
weather-Israel) ā automates a real browser with Playwright: opens weather2day.co.il/forecast, searches for a city, selects it, and then extracts the forecast page content and feeds it to the LLM to answer on its own (RAG approach).US server (
weather-USA) ā fetches forecast and alerts via the official NWS (National Weather Service) API.
The Host connects all servers to the LLM (Gemini, via the OpenAI-compatible API), automatically collects all the Tools from them, and runs a chat loop where the LLM chooses which tool to use and activates them one by one until an answer is received.
Related MCP server: Israeli Weather MCP
šļø Project Structure
project-template/
āāā host.py # צ'×× ××ר××× × ×©×××ר ××Ŗ ×-LLM ××× ×©×Ø×Ŗ× ×-MCP
āāā client.py # MCP Client ×× ×Ø× (××Ŗ××ר ×שרת, ×ר×ׄ Tools)
āāā weather_USA.py # שרת MCP ××ר×"× ā ××Ø× NWS API
āāā weather_Israel.py # שרת MCP ××שר×× ā ××Ø× ××××× ×פ××¤× (Playwright)
āāā pyproject.toml # ×Ŗ×××××Ŗ ×פר×××§×
āāā README.mdIsrael Server Tools
# | Tool | Role |
1 |
| Opens a Chromium browser and navigates to the forecast site |
2 |
| Types a city name in the search field |
3 |
| Selects the first city from the suggestions list |
4 |
| Extracts the forecast page content (including iframes) and returns it to the LLM |
š” The first three share the same browser via a global state (
_page), because they all run in the same server process. The fourth iterates over all frames in the page, because the forecast itself loads inside an<iframe>that the mainbodydoesn't see.
āļø Installation and Running
Prerequisites
Python ā„ 3.13
uv (environment and dependency manager)
Gemini API key (get a free key)
Steps
# 1. ××Ŗ×§× ×Ŗ ××Ŗ×××××Ŗ ×××§××Ŗ ×”××××Ŗ ××¢××××
uv sync
# 2. ××Ŗ×§× ×Ŗ ×פ××¤× Chromium ×¢××ר Playwright
uv run playwright install chromiumSetting Environment Variables
Create a .env file in the project directory:
GEMINI_API_KEY=your_api_key_here
GEMINI_MODEL=gemini-2.0-flashRunning
uv run host.pyAfter launching, a Query: prompt will appear ā type a question (or quit to exit).
š¬ Example Questions the Agent Can Answer
Israel Forecast (Playwright)
A browser window opens showing the forecast page, and the LLM also answers in the chat.
"What's the forecast in Tel Aviv?"
"How many degrees are expected tomorrow in Jerusalem?"
"How's the weather in Haifa today?"
"Open the forecast for Be'er Sheva for me"
US Forecast and Alerts (NWS API)
"What's the weather forecast for San Francisco?" (latitude/longitude)
"Are there any weather alerts in California?"
"Show me active weather alerts for NY"
š§© How It Works (Flowchart)
×שת×ש ā "×× ××Ŗ××××Ŗ ××Ŗ× ××××?"
ā
ā¼
Host āāāŗ LLM (Gemini) ×××ר Tools ××פע××
ā
ā¼ (שרת ×שר××, ××× ××ר ××)
open ā enter("×Ŗ× ××××") ā select ā get_content
ā
ā¼
×Ŗ××× ××Ŗ××××Ŗ ×××ר ×-LLM (RAG)
ā
ā¼
LLM ×××Ŗ× ×Ŗ×©××× ××©×¤× ××פש××Ŗ ×צ'×× āØš Notes
The LLM is the one that decides which Tools to run and in what order ā we didn't write hard-coded logic.
Two server files = two separate processes, so any shared state (like the open browser) must live inside only one server.
Available Tools
4 toolsenter_weather_forecast_city_israelB
Type a city name into the search field on the Israeli forecast page.
Args: city: The name of the city to search for (in Hebrew)
Inputs: city (required). Returns: result
get_weather_forecast_content_israelA
Extract the weather forecast text from the currently open forecast page.
Returns the cleaned textual content so the LLM can answer the user directly.
Inputs: no named parameters. Returns: result
open_weather_forecast_israelA
Open a browser and navigate to the Israeli weather forecast website.
Inputs: no named parameters. Returns: result
select_weather_forecast_city_israelB
Select the first city from the autocomplete suggestion list.
Inputs: no named parameters. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogā¦
TDQS
Scored across 4 tools
Each tool covers a distinct workflow step: opening the page, typing a city, selecting an autocomplete result, and extracting content. The main ambiguity is between open_weather_forecast_israel and get_weather_forecast_content_israel, since both could sound like they return forecast data without reading the descriptions carefully.
All tool names follow the same verb-first snake_case pattern and consistently include weather_forecast and israel context. This makes the order and purpose of the tools predictable.
Four tools is appropriate for the narrow browser-automation workflow this server provides. Each tool contributes one essential step, and the set is neither bloated nor too thin.
The tool surface covers the full workflow from opening the forecast site to retrieving the forecast text for a chosen city. It lacks fallback or reset tools, but those are not essential for the core task.
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