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Shopping_Agent_Using_MCP_Server

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An intelligent, conversational shopping assistant built to revolutionize the online shopping experience. Powered by the Groq AI model and seamlessly integrated with the Model Context Protocol (MCP), this assistant offers smart, multi-context product discovery tailored to your needs. It engages in natural, context-aware conversations to help you

2 stars PythonAI & Machine Learning Updated Aug 8, 2025

Documentation


🛍️ AI Shopping Assistant

An intelligent, conversational shopping assistant powered by the Groq AI model and Model Context Protocol (MCP) for smart web-based product discovery and decision-making.


✨ Overview

The AI Shopping Assistant is an interactive, AI-powered chatbot that helps users make smarter shopping decisions. Backed by xAI’s Groq LLM and the Model Context Protocol (MCP), it can:

  • 🧠 Understand natural language queries
  • 🔎 Conduct real-time searches on shopping platforms
  • 🛒 Compare products, services, and features
  • 💸 Provide price guidance and recommendations

Whether you're choosing between phones, comparing streaming services, or searching for the best air purifier under a budget—this assistant is your ultimate shopping buddy.


🧩 Features

FeatureDescription
🔄 Product ComparisonCompare products (e.g., *iPhone 15 vs. Galaxy S24*)
🎯 Smart RecommendationsGet suggestions based on your needs and budget
📊 Feature AnalysisUnderstand specs, pros, cons, and more
💵 Price GuidanceDetermine best value options
🌐 Service ComparisonCompare services like *Netflix vs. Prime Video*
🔍 Web Search (via MCP)Searches shopping platforms like Amazon, Flipkart, Best Buy
💬 Context-Aware ChatMaintains conversation context and provides summaries
🔁 Retries & FallbacksSmart handling of failed searches with category advice
💡 Chat Commands`/exit`, `/clear`, `/context`, `/status` supported

🚀 Installation

1. Clone the Repository

bash
git clone 
cd ai-shopping-assistant

2. Set Up a Virtual Environment (Optional)

bash
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install Python Dependencies

bash
pip install -r requirements.txt

4. Install MCP Node.js Dependencies

Make sure Node.js and npm are installed:

bash
npm install -g @playwright/mcp @openbnb/mcp-server-airbnb duckduckgo-mcp-server

5. Environment Setup

Create a `.env` file in the root directory:

bash
echo "GROQ_API_KEY=your-api-key-here" > .env

6. MCP Configuration

Ensure you have a valid `browser_mcp.json` in your MCP directory (e.g., `D:\mcp\mcpdemo\`):

json
{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"]
    },
    "airbnb": {
      "command": "npx",
      "args": ["-y", "@openbnb/mcp-server-airbnb"]
    },
    "duckduckgo-search": {
      "command": "npx",
      "args": ["-y", "duckduckgo-mcp-server"]
    }
  }
}

In `shopping_assistant.py`, set:

python
self.config_file = r"path/to/your/browser_mcp.json"

🧠 Usage

Start the Assistant:

bash
python shopping_assistant.py

Example Queries:

text
🛒 You: Best laptop for programming under $1000  
🤖 Assistant: 🔍 Searching... (attempt 1/3)  
✅ Successfully retrieved current information  
[Laptop recommendations with specs and prices]

Commands You Can Use:

  • `exit` or `quit` – End the session
  • `clear` – Reset chat history
  • `context` – View recent conversation summary
  • `status` – Check last search time and rate limits

🗂️ Project Structure

code
ai-shopping-assistant/
├── shopping_assistant.py      # Main assistant logic
├── requirements.txt           # Python dependencies
├── .env                       # Environment variables
├── browser_mcp.json           # MCP config for search engines
└── README.md                  # You're reading it!

📦 Requirements

Add the following to your `requirements.txt`:

code
langchain-grok==0.1.0
python-dotenv==1.0.0
requests==2.31.0
mcp-use==

⚙️ Configuration Details

SettingDescription
🔑 GROQ\_API\_KEYSet in `.env` for xAI’s Grok access
🕒 Rate Limiting3-second delay between API searches
🔁 RetriesUp to 3 search retries with 5s backoff
📁 MCP FileJSON config for search integration
📦 ModelDefault: `qwen-qwq-32b` (Grok model)
🛍️ CategoriesElectronics, appliances, services, clothing, home

⚠️ Limitations

  • Requires internet connection for API and MCP search
  • Prices may vary—verify with retailers
  • Only predefined categories supported
  • MCP setup requires proper Node.js configuration
  • Offline fallbacks may offer limited depth

🔮 Future Enhancements

  • 🛒 Real-time price scraping from major e-retailers
  • 🧬 Personalized recommendations via user profiles
  • 🖥️ Web-based UI for a seamless UX
  • 🛠️ Enhanced MCP integration with more shopping portals

🤝 Contributing

Contributions are welcome!

To contribute:

1. Fork the repository

2. Create your feature branch

bash
git checkout -b feature/your-feature

3. Commit your changes

bash
git commit -m "Add your feature"

4. Push and open a PR

bash
git push origin feature/your-feature

Images:

app1
app2
app3
shop1
shop2
shop3

> 🧠 AI + Shopping = Smarter Choices

> Start your intelligent shopping journey now with the AI Shopping Assistant.

Frequently asked questions

What is Shopping_Agent_Using_MCP_Server?

Shopping_Agent_Using_MCP_Server is An intelligent, conversational shopping assistant built to revolutionize the online shopping experience. Powered by the Groq AI model and seamlessly integrated with the Model Context Protocol (MCP), this assistant offers smart, multi-context product discovery tailored to your needs. It engages in natural, context-aware conversations to help you

How do I install Shopping_Agent_Using_MCP_Server?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is Shopping_Agent_Using_MCP_Server open source?

Yes — it is hosted on GitHub at https://github.com/sakshirajeshirke/Shopping_Agent_Using_MCP_Server and has 2 stars.

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