genpark-agentic-cross-carrier-freight-carbon-offset-router-skill

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

AI Cross-Carrier Freight Carbon Offset & Sustainable Logistics Router. Applies GLEC framework emissions factors (air, rail, maritime, electric ground), optimizes multi-carrier shipping routes, and generates verified carbon offset credit allocation certificates.

🌐 Official GenPark AI Ecosystem Links

• 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: freight-pulse

🌟 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 AgenticFreightCarbonOffsetRouter

def main():
    router = AgenticFreightCarbonOffsetRouter()
    cargo = {"weight_kg": 250.0, "max_acceptable_days": 5}
    routes = [
        {"carrier": "Standard Express Air", "mode": "air_freight", "transit_days": 2, "cost_usd": 320.0, "distance_km": 1500.0},
        {"carrier": "Green Electric Road Line", "mode": "road_electric", "transit_days": 4, "cost_usd": 140.0, "distance_km": 1500.0}
    ]
    res = router.optimize_freight_and_offset(cargo, routes)
    best = res['chosen_sustainable_route']
    print("--- Sustainable Freight Route Optimization ---")
    print(f"Selected: {best['carrier']} ({best['mode']})")
    print(f"Transit: {best['transit_days']} days | Landed Cost: ${best['total_cost_with_offset_usd']}")
    print(f"CO2 Footprint: {best['co2_emissions_kg']} kg (Offset Cost: ${best['carbon_offset_cost_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