ποΈ MCP - MCP for Commerce Platforms - Universal E-commerce Customer Support Assistant
About
It is a platform-agnostic customer support assistant that connects to any e-commerce platform through pluggable strategies, using the MCP (Model Context Protocol) server and Gradio for the user interface. It is built for developers and support teams who need a singleβ¦
Details
- License
- MIT license
Explore
- π¦ Order Management: Track orders, check status, view history
- π Returns & Refunds: Initiate returns, process refunds seamlessly
- β Cancellations: Quick and easy order cancellations
- π¬ Natural Language: Conversational interface for customer support
- π Platform Agnostic: Extensible to any e-commerce platform
- π Auto-Deploy: Continuous deployment to Hugging Face Spaces
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
ποΈ MCP - MCP for Commerce Platforms - Universal E-commerce Customer Support AssistantCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
- Python 3.10 or higher
- Git
- Virtual environment tool (venv)
python -m venv venv
pip install -r requirements-dev.txt
pre-commit install
3. Configure environment variables
bashcp .env.example .env
4. Run the application
bashpython app.py
The application will be available at http://localhost:7860
Create a .env file based on .env.example:
env
HF_TOKEN=your_token_here
HF_USERNAME=your_username
HF_SPACE_NAME=your_space_name
This project is configured for automatic deployment to Hugging Face Spaces:
1. Push to the main branch
2. GitHub Actions will run tests and quality checks
3. If all checks pass, the app deploys to Hugging Face Spaces
To deploy manually to Hugging Face Spaces:
git push space main
```bash
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"\ud83d\udecd\ufe0f mcp - mcp for commerce platforms - universal e-commerce customer support assistant": {
"mcp-slavpilus": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}
McpServers
{
"mcp-slavpilus": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
One Chat, Every Platform - A platform-agnostic customer support assistant that
connects to any e-commerce platform through pluggable strategies, using MCP (Model
Context Protocol) server and Gradio for the interface.
π― Features
- π¦ Order Management: Track orders, check status, view history
- π Returns & Refunds: Initiate returns, process refunds seamlessly
- β Cancellations: Quick and easy order cancellations
- π¬ Natural Language: Conversational interface for customer support
- π Platform Agnostic: Extensible to any e-commerce platform
- π Auto-Deploy: Continuous deployment to Hugging Face Spaces
ποΈ Architecture
The system uses a Strategy Pattern to abstract different e-commerce platforms:
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β Gradio UI Layer β
β (Customer Support Conversational Interface) β
βββββββββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββ
β
βββββββββββββββββββββββββββββββΌββββββββββββββββββββββββββββ
β MCP Server Core β
β (Order Management, NLP Processing, Context Engine) β
βββββββββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββ
β
βββββββββββββββββββββββββββββββΌββββββββββββββββββββββββββββ
β E-commerce Strategy Interface β
β (MockData, Shopify, Magento, WooCommerce, etc.) β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
π Quick Start
Prerequisites
- Python 3.10 or higher
- Git
- Virtual environment tool (venv)
Installation
1. Clone the repository
git clone https://github.com/slavpilus/mcp.git
cd mcp
2. Run the setup script (macOS/Linux)
chmod +x scripts/setup_dev.sh
./scripts/setup_dev.sh
Or manually:
# Create virtual environment
python -m venv venv
# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate
# Install dependencies
pip install -r requirements-dev.txt
# Install pre-commit hooks
pre-commit install
3. Configure environment variables
cp .env.example .env
# Edit .env with your configuration
4. Run the application
python app.py
The application will be available at http://localhost:7860
π οΈ Development
Project Structure
mcp/
βββ .github/workflows/ # CI/CD pipelines
βββ mcp_server/ # Core MCP server implementation
β βββ strategies/ # E-commerce platform strategies
β βββ models/ # Data models
β βββ utils/ # Utility functions
βββ ui/ # Gradio UI components
βββ tests/ # Test suite
βββ app.py # Main Gradio application
βββ requirements.txt # Python dependencies
Development Workflow
1. Activate virtual environment
source venv/bin/activate # macOS/Linux
# or
venv\Scripts\activate # Windows
2. Make your changes
- Follow the existing code style
- Add tests for new functionality
- Update documentation as needed
3. Run code quality checks
# Format code
black .
isort .
# Run linter
ruff check .
# Type checking
mypy mcp_server ui
4. Run tests
# Run all tests with coverage
pytest
# Run specific test file
pytest tests/unit/test_strategies.py
# Run with verbose output
pytest -v
5. Commit changes
git add .
git commit -m "feat: your feature description"
Pre-commit hooks will automatically run code quality checks.
Code Style
This project uses:
- Black for code formatting (line length: 88)
- isort for import sorting
- Ruff for linting
- MyPy for type checking
All code must pass these checks before merging.
Testing
- Minimum test coverage: 80%
- Write unit tests for all new functionality
- Integration tests for critical workflows
- Use pytest fixtures for test data
Adding a New E-commerce Platform
1. Create a new strategy in mcp_server/strategies/:
from .base import EcommerceStrategy
class YourPlatformStrategy(EcommerceStrategy):
async def get_order(self, order_id: str) -> Optional[Order]:
# Implementation here
pass
2. Add tests in tests/unit/test_your_platform_strategy.py
3. Update the strategy factory to include your platform
π Environment Variables
Create a .env file based on .env.example:
```env
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