Skip to main content
Glama
Sign In
Agentic Commerce RFQ Multi-Vendor Bidding Arbiter

Agentic Commerce RFQ Multi-Vendor Bidding Arbiter

by alphaparkinc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

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

Related MCP server: genpark-agentic-post-purchase-dispute-arbiter-skill

🌟 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

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Resources

Related MCP Connectors

Related MCP Servers

  • genpark-cross-platform

    Alpha-Park
    A
    license
    Not graded
    quality
    Not graded
    maintenance
    Enables agents to perform real-time multi-merchant price comparison, coupon stacking, delivery-speed trade-off analysis, stock volatility tracking, and optimal purchase routing across commerce platforms. It exposes these capabilities as an MCP-compatible tool for automated inventory and arbitrage decisions.
    MIT
  • genpark-agentic-post-purchase-dispute-arbiter-skill

    genpark-agentic-post-purchaseofficial

    alphaparkinc
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables agents to ingest carrier webhook events, track stalled or failed shipments, evaluate compensation eligibility, and emit structured merchant claim payloads over MCP. It runs as a zero-dependency Python JSON-RPC stdio server or importable module for post-purchase dispute arbitration.
    7
    MIT

  • Agentic Commerce RFQ Multi

    Alpha-Park
    F
    license
    Not graded
    quality
    Not graded
    maintenance
    Enables autonomous evaluation and ranking of wholesale vendor bids for B2B RFQs by weighing unit volume pricing, minimum order quantities, landed tariffs, net payment terms, and historical lead-time reliability to select the best supplier.
    -

  • via54-social-roi

    veawho
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI clients to run social-media ROI evaluations, multi-account matrix comparisons and budget allocation, search-index and e-commerce spillover estimates, and GEO share-of-voice analysis through a zero-dependency stdio JSON-RPC interface.
    1
    MIT