# genpark-zero-query-inspiration-graph-matcher-skill

<div align="center">

[![Python 3.9+](https://img.shields.io/badge/python-3.9%2B-blue.svg?style=for-the-badge&logo=python)](https://www.python.org/)
[![License MIT](https://img.shields.io/badge/license-MIT-green.svg?style=for-the-badge)](LICENSE)
[![MCP Compatible](https://img.shields.io/badge/MCP-100%25%20Compatible-purple.svg?style=for-the-badge&logo=anthropic)](https://modelcontextprotocol.io/)
[![Zero Dependencies](https://img.shields.io/badge/Dependencies-0%20(Stdlib%20Only)-brightgreen.svg?style=for-the-badge)](requirements.txt)

<p align="center">
  <b>Production-Grade GenPark AI Shopping Agent Skill</b> • <b>100% Standard Library Python</b> • <b>Native Model Context Protocol (MCP)</b>
</p>

</div>

---

## Executive Overview & Architectural Motivation

Following the strategic investment by **L Catterton** (the premier consumer-focused private equity firm managing ~$40B in assets, backed by LVMH) in **GenPark** (backed by InMobi, Google, and Jio), digital commerce is undergoing an epochal shift from **reactive search-box queries** to **ambient, inspiration-led agentic discovery**.

GenPark transforms the smartphone **lock screen ("Screen Zero")** and ambient connected screens into zero-friction, generative shopping portals. This skill library distills the core mathematical logic and agent orchestration models powering this ecosystem:

- **Zero-Friction Discovery**: Instantaneous micro-moment interactions without unlocking devices.
- **Generative Aesthetics & Try-On**: Context-aware outfit synthesis and visual matching.
- **Multi-Touch Ambient Attribution**: Cross-surface state tracking across mobile lock screens and smart displays.
- **Enterprise Protocol Parity**: Full native Model Context Protocol (MCP) compliance for agentic runtime integration.

---

## Architecture Diagram

```mermaid
flowchart TD
    subgraph ScreenZero["Screen Zero (Lock Screen Ambient Layer)"]
        Wake[Device Wake Event] --> Gaze[Gaze & Dwell Detector]
        Gaze --> Sensor[Circadian & Ambient Context]
    end

    subgraph AgentCore["GenPark AI Agent Intelligence Core"]
        Sensor --> CoreEngine["GenParkZeroQueryInspirationGraphMatcher"]
        CoreEngine --> DecisionEngine[Multi-Signal Mathematical Inference]
        DecisionEngine --> OutputPayload[Personalized Shopping Action / MCP Response]
    end

    subgraph RetailEcosystem["L Catterton & Retail Media Network"]
        OutputPayload --> Brands[Luxury / DTC / Fashion Brands]
        OutputPayload --> Checkout[1-Tap Cross-Surface Conversion]
    end
```

---

## Technical Specifications & Features

| Capability | Specification |
| :--- | :--- |
| **Runtime Environment** | Python 3.9+ (Standard Library Only) |
| **External Dependencies** | **None (0 packages)** |
| **Protocol Compatibility** | Anthropic Model Context Protocol (MCP) STDIO JSON-RPC 2.0 |
| **Architecture Paradigm** | Pure Functional & Mathematical Modeling |
| **Deployment Mode** | Headless Agent Skill, CLI Client, or MCP Server |

---

## Quickstart & Usage

### 1. Direct Python Client Execution
```bash
python example_usage.py
```

### 2. Standalone MCP Server Launch
Run the native STDIO MCP server for Claude Desktop, Cursor, or Antigravity integration:
```bash
python mcp_server.py
```

### 3. Model Context Protocol (MCP) Config
Add to your `claude_desktop_config.json` or agent runner config:
```json
{
  "mcpServers": {
    "genpark-zero-query-inspiration-graph-matcher-skill": {
      "command": "python",
      "args": ["path/to/genpark-zero-query-inspiration-graph-matcher-skill/mcp_server.py"]
    }
  }
}
```

---

## Frequently Asked Questions (FAQ)

#### Q1: What makes GenPark AI Shopping Agent Skills unique?
Engineered with **zero external dependencies** using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

#### Q2: How do I test this MCP server locally?
Run `python mcp_server.py --test` to verify MCP protocol discovery and tool schema negotiation.

---

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.