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., "@Agentic Commerce RFQ Multi-Vendor Bidding Arbiter Evaluate these vendor bids for 5,000 optical lenses and choose the best supplier"
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)
Autonomous B2B RFQ (Request for Quote) Multi-Vendor Bidding Arbiter. Scores wholesale quotations across unit volume pricing, minimum order quantities (MOQ), landed tariffs, net payment terms (Net 30/60), and historical supplier lead-time reliability.
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: Negotiator MCP Server
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 AgenticCommerceRFQMultiVendorArbiter
def main():
arbiter = AgenticCommerceRFQMultiVendorArbiter()
rfq = {"part_number": "OPTIC-LENS-F2", "target_quantity": 5000, "max_unit_budget": 25.0}
bids = [
{"vendor_name": "Zeiss Industrial", "unit_price_usd": 22.0, "moq": 2000, "shipping_cost_usd": 800.0, "payment_terms_days": 90, "quality_rating_pct": 99.5},
{"vendor_name": "Apex Optics Co", "unit_price_usd": 19.5, "moq": 5000, "shipping_cost_usd": 1200.0, "payment_terms_days": 30, "quality_rating_pct": 94.0}
]
res = arbiter.arbitrate_vendor_bids(rfq, bids)
w = res['winning_vendor']
print("--- B2B RFQ Multi-Vendor Arbitration ---")
print(f"Selected Supplier: {w['vendor_name']} (Score: {w['arbitration_score']})")
print(f"Landed Cost: ${w['total_landed_cost_usd']} (Adjusted Net Terms: ${w['adjusted_cost_with_terms_usd']})")
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
Autonomous commerce for AI agents: discover, quote, order, pay, verify.
Supplier sourcing, procurement, commercial intelligence, RFQ routing and B2B deal coordination.
Agent-native supply network for components, fabrication, industrial RFQs, offers, and fulfillment.
Deep purchase intelligence for agents and apps: live prices, landed cost, trust scores. Free.
