WeatherForcast Using Playwright

This project is an advanced portfolio piece that demonstrates a professional-grade workflow for integrating LLMs with custom MCP servers and browser automation.

It showcases how to build a multi-agent system in Python that uses:

• Anthropic MCP SDK for exposing custom tools to a language model.
• Playwright for automated browser control and web interaction.
• Groq for LLM orchestration and tool selection.

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Project Overview

The solution includes three main components:

• host.py — the chat host that connects to multiple MCP clients, discovers available tools, and forwards user requests to the LLM.
• client.py — a generic MCP client wrapper that launches MCP server scripts and manages STDIO communication.
• weather_USA.py — a classic MCP server that fetches weather data from the NWS API for the USA.
• weather_Israel.py — a new MCP server that performs browser automation to retrieve Israeli weather forecast data from a website.

The architecture intentionally separates concerns: one MCP handles API-based weather data, while another MCP handles browser-driven Israeli weather retrieval.

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Why this project is portfolio-worthy

This project is designed to reflect both technical depth and learning progress:

• Building a custom MCP Server from scratch.
• Integrating browser automation with an LLM workflow.
• Enabling the model to choose and orchestrate tools dynamically.
• Solving a real-world problem with non-conventional data access.

It demonstrates the ability to design and implement advanced AI tooling, not just consume existing APIs.

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Learning Goals

This project highlights the key professional and educational outcomes:

• Build an independent MCP Server that exposes custom tools to an LLM.
• Use Playwright to extend the LLM with browser automation capabilities.
• Design a multi-step flow that answers real user queries through tool orchestration.
• Apply RAG-style contextual enrichment by extracting and using page content.

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Technical Stack

• MCP SDK — Anthropic's official toolkit for defining the MCP protocol and exposing tools to the model.
• Playwright — Microsoft's modern browser automation framework, used here to automate a real website and perform human-like interactions.

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Project Requirements

The main goal is to build a new MCP Server that handles Israeli weather forecast requests by opening a browser and navigating to a specific site, rather than using a conventional API.

Recruiter-worthy highlights:

• A custom MCP Server focused on browser-based data retrieval.
• Playwright-driven automation for searching and selecting a city in a live website.
• A multi-step tool chain that simulates manual browsing from within an AI agent.
• Page extraction and contextualization for the LLM.

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Development Stages

Stage 0 — Project Structure

project-template/
├── client.py          # generic MCP Client
├── host.py            # terminal chat host that ties everything together
├── weather_USA.py     # MCP Server for USA using API
└── weather_Israel.py  # ← your core Israel-focused MCP

Stage A — Implement Tools with Playwright

Goal: implement MCP tools that allow the LLM to fetch Israeli weather forecast directly from the browser.

The new MCP should load the forecast page from: https://www.weather2day.co.il/forecast

The MCP exposes three Playwright-based tools that automate the complete flow:

1. open_weather_forecast_israel — open the browser and navigate to the weather page.
2. enter_weather_forecast_city_israel — enter the city name into the search field.
3. select_weather_forecast_city_israel — select the first city suggestion from the list.

To make this work professionally:

• define each function as an MCP tool using the decorator.
• add the new MCP to the host's MCP client list.
• run the system and verify the host selects the new MCP and executes the functions sequentially.

Example command:

uv run host.py

Then ask the system for a forecast in a given city and confirm that the host opened the forecast page in the browser.

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Stage B — Let the LLM Finish the Answer

Goal: move beyond tool execution and give the LLM page content so it can answer directly in chat.

Add another tool that extracts the page content, cleans it, and returns a useful context snippet for the LLM. This is the RAG-style enhancement: the model receives actual page text and can incorporate it into the response.

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How it works

1. host.py connects to the MCP clients defined in client.py.
2. It asks Groq to decide which tool should run for a given user query.
3. The selected MCP tool runs and returns real data.
4. If the browser-based MCP is used, Playwright drives the website and extracts context from the page.
5. The result is returned to the user as a fully formed chat answer.

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Why this project matters for AI recruiting

This project shows that I can:

• architect multi-agent systems.
• implement custom MCP tools for business logic.
• use browser automation to access data behind interactive websites.
• enrich LLM responses with real-world context.
• solve engineering problems that involve both AI and automation.

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Run Instructions

1. Install dependencies:

python -m pip install -U pip
python -m pip install groq>=0.4.0 mcp>=1.27.0 python-dotenv>=1.2.2 playwright>=1.44.0
python -m playwright install

2. Create a .env file with:

GROQ_API_KEY=your_groq_api_key_here
GROQ_MODEL=mixtral-8x7b-32768

3. Run the host:

python host.py

4. Ask a query about weather forecast in Israel or the USA.

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Sample Questions

• What is the weather forecast for Jerusalem today?
• Open the Israeli weather site and get the forecast for Tel Aviv.
• Are there any weather alerts in California?
• Use the Israel browser tool and answer from the page content.
• How do I get a 5-day forecast for a US location?

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Notes

• The Israeli MCP is intentionally browser-driven rather than API-driven.
• The project emphasizes autonomous tool orchestration and dynamic LLM-driven execution.
• This is a strong portfolio example for AI engineering, agent design, and browser automation skills.