Enables monitoring of Shopify purchases for price drops within the merchant's price-match guarantee window, eligibility verification, and automated refund claim synthesis.
Enables monitoring of Target purchases for price drops within the merchant's price-match guarantee window, eligibility verification, and automated refund claim synthesis.
Enables monitoring of Walmart purchases for price drops within the merchant's price-match guarantee window, eligibility verification, and automated refund claim synthesis.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genpark-agentic-price-drop-refund-arbiter-skill Register my Best Buy MacBook receipt for $1999 and alert me if price drops for a refund."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Autonomous Post-Purchase Price Drop Monitor & Retailer Refund Claim Arbiter. Tracks online purchases across Shopify, Walmart, Best Buy, and Target, detects price drops within the merchant price-match guarantee window, verifies eligibility conditions, and synthesizes automated refund claims.
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.
Related MCP server: restrackit-pantry-mcp
"""Example usage for AgenticPriceDropRefundArbiter."""
import sys
import json
from client import AgenticPriceDropRefundArbiter
sys.stdout.reconfigure(encoding='utf-8')
def main():
print("=== Agentic Commerce Price Drop & Refund Arbiter Demo ===")
arbiter = AgenticPriceDropRefundArbiter()
# 1. Register post-purchase transaction
print("\n--- 1. Registering Best Buy Purchase Receipt ---")
receipt = arbiter.register_receipt(
receipt_id="BB-2026-90412",
merchant="BESTBUY",
sku="APPLE-MBP-14",
product_name="MacBook Pro 14-inch M4",
purchase_price=1999.00
)
print(f"Registered Receipt #{receipt['receipt_id']}, Window: {receipt['window_days']} Days")
# 2. Evaluate sudden retailer price drop
print("\n--- 2. Evaluating Price Drop from $1999 to $1799 ---")
evaluation = arbiter.evaluate_price_drop("BB-2026-90412", 1799.00)
print(f"Eligible for Claim: {evaluation['eligible_for_claim']} (Savings: ${evaluation['potential_refund_usd']:.2f})")
# 3. Synthesize autonomous refund claim letter
print("\n--- 3. Synthesizing Retailer Price Protection Claim ---")
claim = arbiter.synthesize_refund_claim("BB-2026-90412", 1799.00, "https://bestbuy.com/deal/APPLE-MBP-14")
print(f"Claim ID: {claim['claim_id']}")
print(claim["claim_letter"])
if __name__ == "__main__":
main()
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute embedded test harness:
python mcp_server.py --testInspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.
Licensed under the MIT License. Copyright © 2026 GenPark AI.
This server cannot be deployed
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