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genpark-agentic-commerce-rfq-multi-vendor-bidding-arbiter-skill

mcp
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SUMMARY

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 re | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-agentic-commerce-rfq-multi-vendor-bidding-arbiter-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)

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.

🌐 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 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()

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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