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genpark-sierra-enterprise-customer-agent-empathic-resolver-skill

mcp
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SUMMARY

Sierra-inspired Conversational Enterprise Customer Agent & Empathic Resolver. Conducts sentiment de-escalation, enforces strict enterprise policy guardrails, and executes two-phase commit transactional workflows (refunds, order updates, boo | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-sierra-enterprise-customer-agent-empathic-resolver-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

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

Sierra-inspired Conversational Enterprise Customer Agent & Empathic Resolver. Conducts sentiment de-escalation, enforces strict enterprise policy guardrails, and executes two-phase commit transactional workflows (refunds, order updates, booking changes) without conversational hallucinations.

🌐 Official GenPark AI Ecosystem Links

🌟 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 SierraEnterpriseCustomerAgentEmpathicResolver

def main():
    resolver = SierraEnterpriseCustomerAgentEmpathicResolver()
    msg = "I am furious that my order was delayed. Can I change my delivery address?"
    order = {"order_id": "ORD-5541", "order_status": "SHIPPED", "days_since_delivery": 0, "order_amount_usd": 120.0}
    res = resolver.resolve_customer_inquiry(msg, "address_change", order)
    print("--- Sierra Enterprise Customer Agent Output ---")
    print(f"Sentiment: {res['sentiment_audit']['sentiment']} (Triggers: {res['sentiment_audit']['detected_triggers']})")
    print(f"Policy Check: {res['policy_check']['reason']}")
    print(f"Final Status: {res['final_status']}")
    print(f"Response:\n{res['agent_response']}")

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

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