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., "@Stripe Agentic Dynamic Payment Routing & Interchange Fee Optimizer What's the best rail for a $500 Visa debit payment in the US?"
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.
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
This server cannot be deployed
Plan agent workload routes from evaluation and cost data. $1 USDC per plan; modeled savings only.
Real-time, drift-verified LLM pricing across 26 providers with cost+quality model routing.
Check paid API offers before your agent spends. Qualifying routes: $0.003 USDC. Normal misses: free.
OPX is a buyer-side routing layer for x402 machine commerce. Before an agent pays a downstream provider, OPX ranks currently tested x402 services using recent paid performance evidence including success rate, latency, price, freshness and task fit, then returns the best provider plus independent fallbacks. Each routing decision costs $0.002 USDC on Base.
