MCP with Playwright — Israeli Weather Forecasts

An MCP server that gives an LLM a hand on the mouse.

There is no API here. The server drives a real Chromium the way a person would: it opens weather2day.co.il/forecast, types a city name into the search box, picks a city from the autocomplete list, and reads the forecast page that loads.

The host is a terminal chat backed by Google Gemini. Nothing routes by keyword — Gemini itself decides which server to use. A question about Haifa opens a browser; a question about San Francisco goes to an API.

you> מה מזג האוויר עכשיו בחיפה? כמה מעלות ומה הלחות?

  🔧 open_weather_forecast_israel()
     ↳ Opened https://www.weather2day.co.il/forecast …
  🔧 enter_weather_forecast_city_israel(city='חיפה')
     ↳ Typed 'חיפה'. 4 suggestion(s): [0] חוף הסטודנטים, חיפה  [1] חיפה  …
  🔧 select_weather_forecast_city_israel(index=1)
     ↳ Selected [1] 'חיפה'. Forecast page: https://www.weather2day.co.il/haifa …
  🔧 extract_weather_forecast_israel()
     ↳ # מזג אוויר בחיפה … Current conditions …

🤖 נכון לעכשיו בחיפה, הטמפרטורה היא 23.3 מעלות והלחות עומדת על 80%.

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Quick start

uv sync                              # dependencies and virtualenv
uv run playwright install chromium   # the browser Playwright drives

# Paste your key into .env:
#   GEMINI_API_KEY=...               https://aistudio.google.com/apikey

uv run host.py                       # terminal chat

Tests — 27 of them, seven of which open a real browser against the live site:

uv run pytest                # everything
uv run pytest -m "not e2e"   # fast tests only, no network

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Layout

mcp-playwright-weather-israel/
├── host.py              # terminal chat: Gemini + the agent loop
├── client.py            # generic MCP client, holds several servers at once
├── weather_Israel.py    # MCP server: Playwright on weather2day.co.il
├── weather_USA.py       # MCP server: api.weather.gov, for contrast
├── .env                 # your API key goes here
└── tests/
    ├── test_tools_e2e.py           # real browser, real site, real DOM
    ├── test_schema_translation.py  # MCP JSON Schema → Gemini Schema
    └── test_retry.py               # backoff on 429 / 503

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The tools

Tool	What it does
open_weather_forecast_israel()	Opens the browser on the forecast page
enter_weather_forecast_city_israel(city)	Types a city name, returns the suggestion list
select_weather_forecast_city_israel(index=0)	Clicks a suggestion, loads its forecast page
extract_weather_forecast_israel()	RAG. Reads the loaded page back as clean markdown
close_weather_browser_israel()	Closes the browser

The browser stays open between calls. That is deliberate: the model calls the tools in sequence and watches the page take shape, which is the entire point of the exercise.

A note on "select the first item"

The assignment says the third tool selects the first item in the list. On the live site, typing חיפה returns:

[0] חוף הסטודנטים, חיפה   ← the first item. It is a beach.
[1] חיפה                  ← the city
[2] חיפה, אוניברסיטה
[3] חיפה, טכניון

So index=0 is the default, exactly as specified — but enter_… returns the list as numbered text, and the system prompt instructs the model to pick the index matching the city the user asked about. In testing, Gemini chooses index=1 for Haifa and index=0 for Beer Sheva, where the city genuinely is first.

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Stage 2: RAG

extract_weather_forecast_israel is the retrieval step. It does not return the page; it reads what matters out of it.

The forecast page is mostly not forecast. It has a cookie banner, ad slots, a Highcharts widget whose accessible text reads "Combination chart with 3 data series", and roughly 1,500 words of SEO prose about snowfall on Mount Carmel in 1950. Feeding all of that to the model buries the three numbers that matter.

The extractor therefore reads specific nodes and ignores the rest of the document:

• .current-weather → temperature, last update, wind, gust, wind direction, humidity, sunrise, sunset
• .hourly_forecast_container details → the hourly forecast, day by day, as a markdown table
• Two sources (the European model and the Israel Meteorological Service) render the same days twice; the duplicate is filtered out

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Questions the agent can answer

Question	What happens
מה מזג האוויר בחיפה?	browser → search → select → read page → answer in Hebrew
כמה מעלות עכשיו בתל אביב ומה הלחות?	same path, answers from the extracted values
מה התחזית השעתית להיום בתל אביב?	returns an hour-by-hour table
האם צפוי גשם מחר בירושלים?	reads the precipitation column and answers yes/no
ומה בבאר שבע?	the browser is already open, so it skips open_…
what's the forecast for 37.77,-122.42 ?	routes to weather-usa, no browser involved
are there any weather alerts in California?	get_alerts("CA")

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Configuration (.env)

Variable	Default	Purpose
GEMINI_API_KEY	—	Required. aistudio.google.com/apikey
GEMINI_MODEL	gemini-3.1-flash-lite	See the quota table below
GEMINI_TEMPERATURE	0.2	Not 0: the model must pick an index from a list whose order shifts
MAX_TOOL_ROUNDS	10	open → enter → select → extract is four rounds; leave room
WEATHER_HEADLESS	false	false lets you watch the browser work, which is the point
WEATHER_SLOW_MO_MS	0	300–500 makes the browser legible to a human watching it
WEATHER_TIMEOUT_MS	45000	Navigation and selector timeout

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When something goes wrong

What you see	What it means	What to do
⏳ 503 …; retrying in 2s	The model is busy. The host absorbs it, up to 5 attempts	Nothing. It works
⚠️ Gemini error 429 … RequestsPerDay	Free-tier daily quota exhausted for that model	Switch model in .env
Typed 'X' but no suggestions appeared	The city is not on the site, or a typo	Try another Hebrew city name
404 … is not found	The model id no longer exists	Use one from the table below
CERTIFICATE_VERIFY_FAILED … CA cert does not include key usage extension	You are behind a TLS-terminating filter, on Python 3.13	See below

Why .python-version pins 3.12

Python 3.13 turned on VERIFY_X509_STRICT in ssl.create_default_context(). Strict verification rejects any CA certificate that omits the keyUsage extension — and the root CA that a TLS-terminating filter such as Netfree installs omits exactly that. Every HTTPS call then dies before it leaves the machine, Gemini's included.

The fix is not to disable verification. It is to run the interpreter whose defaults the filter's certificate satisfies, so .python-version pins 3.12. Certificates are still verified, against the filter's CA, which the system already points at through SSL_CERT_FILE. weather_USA.py has a VERIFY_SSL escape hatch for the same situation; on 3.12 you do not need it, and it stays off.

Free-tier quota is per model, per project, per day. Measured against this key on 2026-07-10:

Model	Requests/day (free tier)	Notes
gemini-3.1-flash-lite	500	Fast and cheap. The default; a chat turn costs 4–6 requests
gemini-3-flash-preview	higher	Stronger; returns 503 when busy — the host retries
gemini-2.5-flash	—	404. Google closed it to projects created after ~2026-06

Both surviving models drive the tools correctly.

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Five things that broke, and what fixed them

1. There are two search boxes. The page ships #city_search in the sticky header (hidden on the forecast page) and #city_search_forecast in the body (visible). Typing into the wrong one fills an invisible box and the autocomplete never opens. The selectors in weather_Israel.py were read off the live DOM, not guessed.

2. The suggestion list was read too early. The autocomplete is rebuilt on every keystroke, so "at least one suggestion exists" is true long before the list is complete. Reading it then returned one item out of four — and the tool quietly picked the beach instead of the city. _wait_for_stable_suggestions waits for the count to hold steady across two polls, not merely to become non-zero.

3. The input is visible before its event handler is bound. Typing into it produces a filled box and an empty list, permanently: the handler missed every keystroke and nothing will fire it again. _type_city_and_wait clears the box and types again, up to three times, rather than guessing how long the page's JavaScript takes to load.

4. AsyncExitStack was closed in a different task than it was opened in. stdio_client and ClientSession are anyio context managers, and anyio requires a cancel scope to be exited by the task that entered it — which is not true under pytest-asyncio, nor in a host shutting down from a signal handler. In client.py, each connection now runs a supervisor task that enters the contexts, publishes the session, waits to be told to stop, and unwinds them itself. Closing is just an event, and it is safe to call from anywhere.

5. A single 503 killed the chat. MCP errors were handled; the Gemini call had no retry at all. Host._generate now retries up to five times and honours the retryDelay the server asks for instead of guessing. In the last verification run it absorbed three consecutive 503s and still produced the answer.

Also worth knowing: the site's ad and analytics scripts are blocked (_BLOCKED_HOSTS), but not via context.route("**/*"). A catch-all route round-trips every request through Python, on the same event loop that serves MCP over stdio — which starves the autocomplete's own XHR, so the suggestion list never arrives. One narrow pattern per blocked host keeps the matching inside Playwright, where it belongs.

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MCP JSON Schema → Gemini Schema

FastMCP emits JSON Schema with title, default, additionalProperties and lowercase type names. Gemini's types.Schema accepts none of them. _convert() in host.py strips the unsupported keys, maps integer → INTEGER, collapses Optional[int] (which arrives as anyOf: [integer, null]) into nullable, and returns None for a tool with no arguments — because Gemini rejects an OBJECT with no properties.

Each of the six tests in test_schema_translation.py is a 400 that happened before the test was written.

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Another library the AI era pulled onto the stage

Playwright predates GenAI and got a second life when agents started needing a browser.

Pydantic is another. Written in 2017 as an unglamorous validation library for type hints, it is now the substrate almost all tool calling stands on: FastMCP derives each tool's JSON Schema from it, and from there — through the _convert() above — comes the function declaration sent to the model. Type validation became the language in which an LLM is told what it is allowed to call.

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Note on the project template

The assignment links to github.com/malbruk/dovrot-ai-projects/tree/master/08-mcp/project-template. That URL returns 404: the repository is not publicly accessible, so its code could not be downloaded. The four files here (client.py, host.py, weather_USA.py, weather_Israel.py) were written from scratch against the structure and filenames the assignment specifies.

Note also that the template's client and host are built on Anthropic's SDK while this project targets Gemini, so both files would have had to be rewritten regardless.