🛍️ PriceHunt

A Model Context Protocol (MCP) implementation that finds the lowest-priced products with good ratings (4+ stars) across major Pakistani e-commerce platforms including Daraz, Telemart, and iShopping.

MCP

Model Context Protocol (MCP) is a standardized protocol that enables AI applications to securely connect to external data sources and tools. It acts as a bridge between AI models (like Gemini) and various services, databases, APIs, and applications.

MCP Architecture Components:

• MCP Servers - Provide specific tools, resources, or data to clients
• MCP Clients - AI applications that want to access external resources
• Transport Layer - Communication mechanism between clients and servers

🎯 Project Overview

This project demonstrates MCP implementation by creating:

1. MCP Server: Provides three tools for scraping Pakistani e-commerce sites
2. MCP Client: Uses LangChain + Google Gemini to orchestrate tool calls
3. Streamlit Frontend: User-friendly web interface for product searches

Note: In this project both server and client run on the same host for learning purposes.

✨ Features

• 🔍 Multi-Platform Search: Scrapes Daraz, Telemart, and iShopping simultaneously
• ⭐ Quality Filtering: Prioritizes products with 4+ star ratings
• 💰 Price Search: Finds the lowest-priced genuine products
• 🤖 AI-Powered: Uses Google Gemini for intelligent product matching
• 💬 Chat Interface: Conversational UI with memory
• 🚀 Async Processing: Non-blocking operations for better performance

<img width="1366" height="671" alt="PriceHunt" src="https://github.com/user-attachments/assets/19b36d89-77cc-4295-b9aa-44d51d098e0c" />

🏛️ MCP Architecture

This Project's MCP Implementation:

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Streamlit     │    │   MCP Client    │    │   MCP Server    │
│   Frontend      │◄──►│  (LangChain +   │◄──►│   (FastMCP)     │
│   (app.py)      │    │   Gemini)       │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                        │
                                                        ▼
                                               ┌─────────────────┐
                                               │   E-commerce    │
                                               │   Websites      │
                                               │ • Daraz.pk      │
                                               │ • Telemart.pk   │
                                               │ • iShopping.pk  │
                                               └─────────────────┘

MCP Tools Defined:

1. `get_daraz_products(query)` - Scrapes Daraz with 4+ rating filter
2. `get_telemart_products(query)` - Scrapes Telemart search results
3. `get_ishopping_products(query)` - Scrapes iShopping catalog

📁 Project Structure

PriceHunt-MCP/
├── project/             # Client-side code
│   └── app.py              # Streamlit web interface
|   └── mcp_client.py          # MCP Client with LangChain integration
|   └── mcp_server.py          # MCP Server with 3 e-commerce tools
├── python-version         # Python version specification
├── pyproject.toml         # Python project configuration
├── README.md             # This file
└── uv.lock               # UV dependency lock file

🚀 Installation & Setup

1. Clone the Repository

git clone https://github.com/FassihShah/PriceHunt-MCP.git
cd PriceHunt-MCP

2. Create Virtual Environment

# Create virtual environment
python -m venv venv

# Activate virtual environment
venv\Scripts\activate

3. Install Dependencies

Since we're using uv, install dependencies with:

# If using uv (recommended)
uv install

# Or using pip with requirements.txt
pip install -r requirements.txt

If you don't have `uv` installed:

# Install uv first
pip install uv
# Then install dependencies
uv install

4. Set Up Environment Variables

Create a .env file in the project root:

GOOGLE_API_KEY=your_google_gemini_api_key_here

🖥️ Using with Claude Desktop

This MCP server can also be integrated directly with Claude Desktop application, allowing to use the e-commerce tools directly in your conversations with Claude!

Setup for Claude Desktop:

1. Install Claude Desktop:

• Download from Claude Desktop
• Make sure you have the latest version

2. Configure Claude Desktop: Open the Claude Desktop configuration file:

Windows:

code %APPDATA%\Claude\claude_desktop_config.json

3. Add Your MCP Server: Create or update the claude_desktop_config.json file:

{
  "mcpServers": {
    "ecommerce-scraper": {
      "command": "python",
      "args": ["/path/to/your/project/mcp_server.py"],
      "env": {
        "PYTHONPATH": "/path/to/your/project"
      }
    }
  }
}

Once configured, you can directly ask Claude things like:

• "Find me the cheapest Ronin Earbuds"

Claude will automatically use these MCP tools to scrape the websites and provide results!

🎮 Usage

Method 1: Claude Desktop Integration

After setting up Claude Desktop configuration (see section above)

Method 2: Streamlit Web Interface

streamlit run app.py

Method 3: MCP Inspector (Development & Testing)

Use the official MCP Inspector to test and debug your server:

uv run mcp dev mcp_server.py

This will:

• Launch a web interface
• Test all your tools interactively
• View tool schemas and parameters

📚 Learning Outcomes

This project demonstrates:

• MCP Protocol: Understanding of server/client architecture
• AI Integration: LangChain + LLM tool orchestration
• Async Programming: Non-blocking operations