klydo-mcp

Klydo MCP Server

Fashion discovery MCP server for Indian Gen Z.

Enables AI assistants like Claude to search and discover fashion products from Klydo — India's Gen-Z quick tech fashion commerce platform based in Bangalore.

✨ Features

• 🔍 Search Products — Search fashion items with filters (category, gender, price range)
• 📦 Product Details — Get complete product info including images, sizes, colors, ratings
• 🔥 Trending Products — Discover what's popular right now
• 📝 Structured Logging — Debug-friendly logs with Loguru
• ⚡ Fast & Cached — In-memory caching for quick responses

🚀 Quick Start

Installation

Option 1: Install from PyPI (Recommended)

# Using pip
pip install klydo-mcp

# Or using pipx (isolated environment)
pipx install klydo-mcp

# Or using uvx (no installation needed)
uvx --from klydo-mcp klydo

Option 2: Install from Source

# Clone the repository
git clone https://github.com/myselfshravan/klydo-mcp.git
cd klydo-mcp

# Install dependencies with uv
uv sync

Usage with Claude Desktop

If installed via PyPI (pip/pipx)

Add to your Claude Desktop configuration:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "klydo": {
      "command": "klydo"
    }
  }
}

If using uvx (recommended for easy updates)

{
  "mcpServers": {
    "klydo": {
      "command": "uvx",
      "args": ["--from", "klydo-mcp", "klydo"]
    }
  }
}

If installed from source

{
  "mcpServers": {
    "klydo": {
      "command": "uv",
      "args": ["--directory", "/path/to/klydo-mcp", "run", "klydo"]
    }
  }
}

Then restart Claude Desktop.

Run Standalone

uv run klydo

🛠️ MCP Tools

search_products

Search for fashion products.

Parameter	Type	Description
query	string	required — Search terms (e.g., "black dress", "nike shoes")
category	string	Filter by category (e.g., "dresses", "shoes")
gender	string	Filter by gender ("men" or "women")
min_price	int	Minimum price in INR
max_price	int	Maximum price in INR
limit	int	Max results (default 10, max 50)

get_product_details

Get complete product information.

Parameter	Type	Description
product_id	string	required — Product ID from search results

Returns: Full details — images, sizes, colors, ratings, and purchase link.

get_trending

Discover what's hot rn 🔥

Parameter	Type	Description
category	string	Category filter
limit	int	Max results (default 10, max 50)

⚙️ Configuration

Copy .env.example to .env and customize:

# Request settings
KLYDO_REQUEST_TIMEOUT=30
KLYDO_CACHE_TTL=3600

# Debug mode (set to false in production)
KLYDO_DEBUG=false

# API token for klydo.in (required)
KLYDO_KLYDO_API_TOKEN=your-token

📁 Project Structure

klydo-mcp/
├── src/klydo/
│   ├── __init__.py
│   ├── server.py          # MCP server entry point
│   ├── config.py          # Configuration (Pydantic Settings)
│   ├── logging.py         # Loguru configuration
│   ├── models/
│   │   └── product.py     # Product, Price models
│   └── scrapers/
│       ├── base.py        # Scraper protocol (interface)
│       ├── cache.py       # In-memory cache with TTL
│       └── klydo_store.py # Klydo.in API client
├── tests/                 # Test suite
├── .github/workflows/     # CI/CD pipelines
├── pyproject.toml
└── README.md

🧪 Testing

# Run all tests
uv run pytest

# Run with verbose output
uv run pytest -v

# Run specific test file
uv run pytest tests/test_models.py

🔧 Development

# Install dev dependencies
uv sync --dev

# Run linting
uv run ruff check src/

# Format code
uv run ruff format src/

# Run the server locally
uv run klydo

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

🔐 Security

For security issues, please see our Security Policy.

📄 License

MIT License — see LICENSE for details.

🏢 About Klydo

Klydo is a Bangalore-based startup building quick tech fashion commerce for Gen-Z (18-32 age group). We're making fashion discovery seamless, fast, and accessible. This MCP server extends our platform to AI assistants, enabling natural language fashion search.

Backed by innovation. Built for Gen-Z. Made in India. 🇮🇳

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Made with ❤️ in Bangalore, India