genpark-agentic-post-purchase-dispute-arbiter-skill
OfficialClick 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-post-purchase-dispute-arbiter-skillReview this delayed delivery webhook and generate a compensation claim payload"
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-post-purchase-dispute-arbiter-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
🌟 Overview
genpark-agentic-post-purchase-dispute-arbiter-skill delivers robust, industrial-grade capabilities engineered for Multi-Agent Collaborative Swarms and Enterprise Workplace Execution. Built exclusively on the Python standard library with zero external runtime dependencies, it integrates seamlessly as a native Model Context Protocol (MCP) server or an importable Python module.
Autonomous Agentic Post-Purchase Tracking & Delivery Dispute Arbiter (inspired by Meta Muse, Shopify, Walmart, Instacart Post-Checkout Operations). Ingests carrier webhook events, tracks transit anomalies (stalled shipments, delivery failures), calculates compensation eligibility, and generates structured merchant claim payloads.
💡 Key Capabilities
Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without
pip installoverhead or supply-chain vulnerabilities.Model Context Protocol (MCP) First: Compatible with Claude Desktop, Cursor, GenPark Engine, Meta Muse, and Tencent WorkBuddy runtime frameworks.
Deterministic & Safe: Rigorous mathematical synchronization models, cryptographic hashing, AST syntax trees, and strict policy boundary validation.
High Concurrency & Low Latency: In-memory thread-safe state synchronization, vector clock resolution, and high-throughput regex scanners.
Related MCP server: Commerce Ops MCP
🚀 Quickstart
1. Direct Python Usage
from client import AgenticPostPurchaseDisputeArbiter
client = AgenticPostPurchaseDisputeArbiter()
result = client.evaluate_shipment_status()
print(result)2. Standalone MCP Server Execution
Run the MCP server via standard JSON-RPC 2.0 stdio:
python mcp_server.pyVerify standard compliance and self-tests:
python mcp_server.py --test3. Claude Desktop / Cursor MCP Configuration
Add this tool to your claude_desktop_config.json or Cursor MCP settings:
{
"mcpServers": {
"genpark-agentic-post-purchase-dispute-arbiter-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-agentic-post-purchase-dispute-arbiter-skill/mcp_server.py"]
}
}
}🛠️ Verification & Testing
Run the included verification suite:
python example_usage.py📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
Developed with ❤️ by the GenPark Autonomous Agent Ecosystem Team.
This server cannot be deployed
Maintenance
Related MCP Connectors
Real-time order tracking for Shopify merchants: look up, list, and refresh shipments.
Webhooks for AI agents: send events, manage endpoints, inspect and retry deliveries.
Ocean & multimodal freight intelligence: rates, landed cost, transit, customs, risk, ship decisions
Multi-carrier shipping for AI agents: compare rates, buy labels, track packages, validate addresses
Related MCP Servers
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- FlicenseNot gradedqualityCmaintenanceEnables commerce operations teams to investigate missing-delivery complaints and resolve them through natural language, with policy-enforced refunds and escalations.-
- FlicenseNot gradedqualityBmaintenanceEnables autonomous refund and dispute arbitration by validating carrier proofs and policies, producing structured output dossiers with execution telemetry.7-
- AlicenseNot gradedqualityBmaintenanceEnables agents to ingest carrier webhook events, track stalled or failed shipments, evaluate compensation eligibility, and emit structured merchant claim payloads over MCP. It runs as a zero-dependency Python JSON-RPC stdio server or importable module for post-purchase dispute arbitration.7MIT