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AgenticCartAbandonmentRecoveryOrchestrator

genpark-agentic-cart-abandonment-recovery-orchestrator-skill

GenPark AI License: MIT Dependencies MCP Compliant

Autonomous Agentic Cart Abandonment Recovery Orchestrator. Monitors abandoned checkout sessions in Meta Muse, Shopify, and WhatsApp Commerce, diagnoses friction causes (shipping cost shock, size doubt, payment hesitation), crafts margin-preserving dynamic incentive vouchers, and synthesizes multi-channel win-back messages.


๐ŸŒŸ 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.


Related MCP server: Merchant Web MCP

๐Ÿš€ Quick Start

1. Direct Python SDK Usage

"""Example usage for AgenticCartAbandonmentRecoveryOrchestrator."""
import sys
import json
from client import AgenticCartAbandonmentRecoveryOrchestrator

sys.stdout.reconfigure(encoding='utf-8')

def main():
    print("=== Agentic Commerce Cart Abandonment Win-Back Orchestrator Demo ===")
    orchestrator = AgenticCartAbandonmentRecoveryOrchestrator()

    abandoned_session = {
        "cart_id": "CART-MUSE-89410",
        "customer_name": "Sarah Connor",
        "cart_total_usd": 150.00,
        "shipping_cost_usd": 30.00,
        "exit_step": "SHIPPING",
        "dwell_time_seconds": 75.0,
        "items": ["Smart Noise-Canceling Earbuds", "Protective Leather Case"]
    }

    # 1. Diagnose abandonment friction cause
    print("\n--- 1. Diagnosing Drop-off Friction ---")
    diagnosis = orchestrator.diagnose_abandonment_friction(abandoned_session)
    print(f"Friction Code: {diagnosis['friction_code']}")
    print(f"Recommended Strategy: {diagnosis['recommended_strategy']}")

    # 2. Synthesize profit-margin preserving win-back voucher
    print("\n--- 2. Synthesizing Dynamic Incentive Voucher ---")
    offer = orchestrator.synthesize_winback_offer(abandoned_session, profit_margin_pct=45.0)
    print(f"Voucher Code: {offer['voucher_code']} (Discount: {offer['discount_percentage']}%, Free Shipping: {offer['free_shipping_granted']})")
    print(f"Total Customer Savings: ${offer['customer_savings_usd']:.2f}")

    # 3. Generate WhatsApp and WeChat Work recovery message copy
    print("\n--- 3. Generating Channel-Native Outreach Copy ---")
    wa_msg = orchestrator.generate_recovery_message(abandoned_session, channel="WHATSAPP", offer=offer)
    print(f"[WhatsApp]:\n{wa_msg['recovery_message_text']}")

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

๐Ÿ› ๏ธ MCP Tool Specification

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


๐Ÿ“œ License

Licensed under the MIT License. Copyright ยฉ 2026 GenPark AI.

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