AgenticCartAbandonmentRecoveryOrchestrator
Monitors abandoned checkout sessions in Meta Muse, diagnosing friction causes and orchestrating cart abandonment recovery.
Monitors abandoned checkout sessions in Shopify, diagnosing friction causes and orchestrating cart abandonment recovery.
Generates WeChat Work recovery message copy for multi-channel cart abandonment win-back campaigns.
Integrates with WhatsApp Commerce to monitor abandoned checkout sessions and generate channel-native win-back recovery messages.
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., "@AgenticCartAbandonmentRecoveryOrchestratorDiagnose cart CART-MUSE-89410 abandonment and send Sarah a WhatsApp win-back voucher."
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.
genpark-agentic-cart-abandonment-recovery-orchestrator-skill
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.
🌟 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, andtools/call.Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
Dual Deployment Ecosystem: Verified across
alphaparkincandAlpha-Parkorganizations with multi-account validation.
Related MCP server: Maple Cart MCP
🚀 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.pyExecute 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
Licensed under the MIT License. Copyright © 2026 GenPark AI.
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