Provides Mastercard Agent Connect-inspired dynamic payment routing and fee optimization, evaluating geography, card BIN networks, authorization probabilities, and rail costs to select Pareto-optimal settlement paths.
Provides dynamic payment routing and interchange fee optimization for Stripe transactions, evaluating geography, card BIN networks, authorization probabilities, and rail costs to select Pareto-optimal settlement paths.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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.
GenPark Official Website: https://www.genpark.ai
LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)
Related MCP server: PayRouter
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.
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()
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute embedded test harness:
python mcp_server.py --testInspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.
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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