# genpark-agentic-commerce-price-drop-retroactive-refund-claimer-skill

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> **Official Website**: [https://www.genpark.ai](https://www.genpark.ai)  
> **LinkedIn**: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) ([https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550))

AI Agentic Commerce Retroactive Price Drop Refund Claimer. Scans past order receipts against retailer price-protection policy windows (Target, Best Buy, Amazon, Nordstrom), verifies eligibility, and synthesizes auto-claim refund filing packets.

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## 🌐 Official GenPark AI Ecosystem Links

- **GenPark Official Website**: [https://www.genpark.ai](https://www.genpark.ai)
- **LinkedIn Official Page**: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) ([https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550))

---

## 🌟 Key Features

- **100% Zero External Dependencies**: Runs entirely on the Python 3.9+ standard library.
- **Model Context Protocol (MCP) Standard**: Native support for JSON-RPC 2.0 `initialize`, `tools/list`, and `tools/call`.
- **Industrial-Grade Determinism**: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
- **Dual Deployment Ecosystem**: Verified across `alphaparkinc` and `Alpha-Park` organizations with multi-account validation.

---

## 🚀 Quick Start

### 1. Direct Python SDK Usage

```python
from client import AgenticPriceDropRefundClaimer

def main():
    claimer = AgenticPriceDropRefundClaimer()
    purchases = [
        {"order_id": "TGT-4481", "merchant": "target", "product_name": "Espresso Machine", "paid_price": 199.99, "live_current_price": 159.99, "days_since_purchase": 6},
        {"order_id": "WMT-7712", "merchant": "walmart", "product_name": "Cordless Vacuum", "paid_price": 249.00, "live_current_price": 209.00, "days_since_purchase": 18}
    ]
    res = claimer.evaluate_price_drop_claims(purchases)
    print("--- Price Drop Retroactive Claimer ---")
    print(f"Total Reclaimable: ${res['total_reclaimable_refund_usd']} across {res['eligible_claims_count']} claims")
    for c in res['claims']:
        print(f"* [{c['merchant'].upper()}] {c['product_name']}: Refund ${c['price_difference_usd']}")

if __name__ == "__main__":
    main()

```

### 2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over `stdio`:

```bash
python mcp_server.py
```

Execute embedded test harness:

```bash
python mcp_server.py --test
```

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## 🛠️ MCP Tool Specification

Inspect [`skill.json`](skill.json) for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

---

## 📜 License & Community

Licensed under the [MIT License](LICENSE). Copyright © 2026 [GenPark AI](https://www.genpark.ai).
Follow us on LinkedIn: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) | [https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550)