Weather Israel MCP
Provides weather forecast data for cities in Israel by scraping weather2day.co.il via Playwright, and for the USA via NWS API, with the OpenAI model managing the conversation.
Click on "Install 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 Israel MCPwhat's the weather forecast for tomorrow 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.
๐ฆ๏ธ Weather Israel MCP
An MCP (Model Context Protocol) project that lets an LLM pull weather forecasts for cities in Israel โ directly from the browser, no API, using Playwright.
๐ฏ Project Goal
Instead of relying on a built-in API, the Agent opens a Chrome browser, navigates to the weather2day.co.il site, searches for a city, selects it from the list โ and then reads the page content and provides a full forecast.
Related MCP server: Israeli Weather MCP
๐ ๏ธ Tech Stack
Tool | Role |
Defining Tools and running the MCP server | |
Browser control โ navigation, typing, clicking | |
The language model that manages the conversation | |
Environment and dependency management |
๐ Project Structure
project-template/
โโโ client.py # MCP Client ืื ืจื โ ืืชืืืจ ืืฉืจืชื MCP
โโโ host.py # ืฆ'ืื ืืจืืื ื ืฉืืืืจ ืืช ืืื ืืื
โโโ weather_USA.py # MCP Server ืืชืืืืช ืืจื"ื (ืืจื NWS API)
โโโ weather_Israel.py # MCP Server ืืชืืืืช ืืฉืจืื (ืืจื Playwright)
โโโ .env # ืืคืชื API (ืื ืืืขืื ื-Git)
โโโ pyproject.toml # ืชืืืืืช ืืคืจืืืงื๐ Running the Project
1. Installing Dependencies
uv sync2. Installing Chrome Browser for Playwright
uv run playwright install chromium3. Setting Up the API Key
Create a .env file in the directory:
OPENAI_API_KEY=your_openai_api_key_here4. Running
uv run host.py๐ฌ Example Questions
Query: what is the weather in Jerusalem?
Query: ืื ืืชืืืืช ืืชื ืืืื?
Query: ืืื ืืฉืื ืืืื ืืืืคื?
Query: what is the weather forecast for Bnei Brak?
Query: what are the weather alerts in NY?
Query: what is the forecast for latitude 40.7, longitude -74.0?๐งฉ The weather_Israel MCP Tools
Tool | Description |
| Opens a browser and navigates to the weather site |
| Types a city name in the search field |
| Selects the matching city from the dropdown list |
| Reads the page content and returns the forecast to the LLM (RAG) |
๐งฉ The weather_USA MCP Tools
Tool | Description |
| Returns weather alerts by state code (e.g. |
| Returns a forecast by geographic coordinates |
Available Tools
4 toolsenter_weather_forecast_city_israelC
ืืื ืช ืฉื ืืขืืจ ืืฉืื ืืืืคืืฉ.
| Name | Required | Description | Default |
|---|---|---|---|
| city_name | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It reveals only that a city name is typed into a search field, but does not disclose whether the action submits, overwrites existing text, requires a loaded page, or is idempotent. This is a thin behavioral disclosure for an action tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The single sentence is short and front-loaded with no fluff, but it is under-specified. It is not as egregious as a one-word tautology, yet there is no room for waste while important behavioral and workflow context is missing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no annotations or output schema, the basic target is present, but the description fails to explain where this action fits in a workflow (before select? after open?) and what happens after entering the name. An agent cannot confidently sequence this tool among its siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description should compensate. It only repeats 'city name' (which equals the property name city_name) and adds the marginal context that the value goes into the search field. No format, example, language, or acceptable values are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The Hebrew description, 'Entering the city name in the search field,' clearly states a specific action and resource, making the core purpose understandable. It does not explicitly contrast with sibling tools like select_weather_forecast_city_israel or open_weather_forecast_israel, but the verb 'enter' differentiates it from 'select' and 'open' enough for a 4.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to call this tool versus the siblings: there is no mention of a preceding open action or a following select/extract step. The workflow context is only implied by the tool name, not stated in the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_weather_data_from_pageA
ืืืืฅ ืืช ืชืืื ืืืงืกื ืืืืฃ ืื ืืืื ืืื ืฉื-LLM ืืืื ืืงืจืื ืืช ืืชืืืืช. ืืื ืฉืื ื-RAG ืฉืืกืคืง ืงืื ืืงืกื ืืืืื.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the core behaviorโextracting text and providing contextโbut does not mention prerequisites, side effects, or limitations. For a zero-parameter read operation this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short, purposeful sentences that front-load the action and explain why the tool exists. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The definition is serviceable for a simple zero-parameter tool, but it omits the prerequisite that a weather page must already be open and does not describe the output format, which would help an agent sequence it correctly among the sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the baseline is 4. The description adds no parameter details, but none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('extracts') and resource ('text content from the current page'), with a clear purpose: letting the LLM read the forecast. It is distinguishable from the sibling navigation tools, though it does not explicitly name them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'RAG stage' phrase implies the tool is an intermediate step after a page is loaded and before the model uses the forecast, but there is no explicit when-to-use or when-not-to-use guidance relative to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
open_weather_forecast_israelA
ืคืชืืืช ืืืชืจ ืื ืืืื ืืืฃ ืืชืืืืช.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the core behavior: opening the site and navigating to the forecast page. However, with no annotations, it does not mention potential side effects, whether the page is displayed, or what success/failure looks like. For a simple navigation tool this is acceptable but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short, focused sentence with no filler or redundant information. It earns its place by clearly stating the action and target page.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter navigation tool with no output schema, the description is largely complete. It communicates the intended action, though it would benefit from a brief note about being the initial step in a multi-step weather lookup flow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so there are no parameter semantics to document. The description therefore does not need to add parameter-level detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The Hebrew description translates to 'Opening the website and navigating to the forecast page,' which states a clear action and destination. It is distinguishable from sibling tools like enter_weather_forecast_city_israel and extract_weather_data_from_page, though it does not explicitly name the website.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus the sibling tools, or any workflow context such as 'use this first before entering a city.' The description implies a navigation step but provides no explicit selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
select_weather_forecast_city_israelB
ืืืืจืช ืืขืืจ ืืืจืฉืืื ืื ืคืชืืช.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of explaining behavior. It only names the selection action and does not disclose side effects, prerequisites such as whether the dropdown must already be open, or what happens after selection.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short, front-loaded sentence with no filler or repetition. It is concise enough for a trivial UI action, though it is somewhat thin in substance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-input, no-output-schema UI action, the description is mostly adequate, but it omits the source of the target city value and prerequisites like an already-open dropdown. The action is identifiable, yet not fully contextualized among its siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so there is no parameter-level information for the description to add. This matches the baseline for a no-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The Hebrew description states a concrete action with a resource: selecting the city from a dropdown list. It is clear what the tool does and subtly differentiates this from the 'enter' sibling, though the distinction is not made explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'from the dropdown list' implies this is used when a list of cities is available and a choice must be made, but the description gives no explicit when-to-use or when-not-to-use guidance relative to open, enter, or extract siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
4 tool updates
v0.1.0- First observed
enter_weather_forecast_city_israel - First observed
extract_weather_data_from_page - First observed
open_weather_forecast_israel - First observed
select_weather_forecast_city_israel
TDQS
Scored across 4 tools
Each tool represents a distinct step in a clear browser workflow: open the site, enter a city, select a city, and extract text. Even though enter and select both relate to city input, their descriptions clearly distinguish typing into a search field from choosing from a dropdown.
All tool names use snake_case with a verb-noun structure, but the object naming is slightly inconsistent: three names end with 'city_israel' or 'israel', while the extract tool uses 'weather_data_from_page' and drops the location marker. Overall the pattern is readable and predictable.
With only 4 tools, the server is tightly scoped to a single weather-forecast retrieval workflow. Each tool earns its place as a necessary step, and the count is well within the ideal range for a focused server.
The tools cover the core lifecycle of opening the forecast page, entering and selecting a city, and extracting the forecast text for the model. Minor gaps exist, such as no explicit retry, reset, or alternate navigation tool, but the primary user journey is fully supported.
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Related MCP Servers
- FlicenseAqualityCmaintenanceMCP server enabling LLMs to fetch Israeli weather forecasts by automating a browser with Playwright to scrape weather2day.co.il.4-
- FlicenseNot gradedqualityCmaintenanceMCP server that lets users obtain current Israeli city weather forecasts. It uses Playwright to browse Weather2day, extract the forecast page, and feed the cleaned data back to the LLM for natural language responses.-
- FlicenseAqualityCmaintenanceAn MCP server that provides real-time weather data for Israel by automating browser searches via Playwright. It allows LLMs to get current forecasts for Israeli cities through natural language queries.4-
- FlicenseBqualityCmaintenanceMCP server that enables LLMs to retrieve weather forecasts for Israeli cities by automating a browser with Playwright, and also provides US weather alerts and forecasts via NWS API.4-