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

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.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.

🌐 Official GenPark AI Ecosystem Links

• GenPark Official Website: https://www.genpark.ai

• LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Related MCP server: AgenticPriceDropRefundArbiter

🌟 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

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:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect 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. Copyright © 2026 GenPark AI. Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550