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genpark-stripe-agentic-dynamic-payment-routing-fee-optimizer-skill

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SUMMARY

Stripe & Mastercard Agent Connect-inspired Dynamic Payment Routing & Interchange Fee Optimizer. Evaluates transaction geography, card BIN networks, authorization probabilities, and basis-point rail costs (ACH, FedNow, Card Rails) to route p | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-stripe-agentic-dynamic-payment-routing-fee-optimizer-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)

Stripe & Mastercard Agent Connect-inspired Dynamic Payment Routing & Interchange Fee Optimizer. Evaluates transaction geography, card BIN networks, authorization probabilities, and basis-point rail costs (ACH, FedNow, Card Rails) to route payments along Pareto-optimal settlement paths.

🌐 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 StripeAgenticDynamicPaymentRoutingOptimizer

def main():
    optimizer = StripeAgenticDynamicPaymentRoutingOptimizer()
    intent = {
        "amount_usd": 500.0,
        "currency": "USD",
        "card_network": "visa_debit",
        "country": "US"
    }
    res = optimizer.optimize_payment_route(intent)
    print("--- Stripe Dynamic Interchange Payment Routing ---")
    print(f"Optimal Rail: {res['selected_optimal_rail']['rail_name']}")
    print(f"Processing Fee: ${res['selected_optimal_rail']['processing_fee_usd']} (Effective: {res['selected_optimal_rail']['fee_effective_pct']}%)")
    print(f"Estimated Savings: ${res['savings_vs_standard_card_usd']} per transaction")

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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