genpark-personal-agent-dynamic-grocery-pantry-auto-replenisher-skill

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

Personal Agent Autonomous Smart Pantry & Dietary Grocery Replenisher. Tracks household food consumption velocities, calculates expiration decay curves, enforces dietary filters (organic, gluten-free, low-sugar), and synthesizes optimized grocery carts.

🌐 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: household-agent

🌟 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 PersonalAgentGroceryPantryAutoReplenisher

def main():
    agent = PersonalAgentGroceryPantryAutoReplenisher()
    pantry = [
        {"item_name": "Rolled Oats", "current_qty": 0.2, "daily_consumption_rate": 0.1, "days_until_expiration": 30, "estimated_unit_price_usd": 4.29},
        {"item_name": "Cold Brew Coffee", "current_qty": 0.3, "daily_consumption_rate": 0.3, "days_until_expiration": 4, "estimated_unit_price_usd": 6.99}
    ]
    res = agent.generate_replenishment_basket(pantry, {"weekly_budget_usd": 60.0, "organic_preference": False})
    print("--- Smart Pantry Auto-Replenishment ---")
    print(f"Total Cart: ${res['total_basket_cost_usd']} (Remaining: ${res['remaining_budget_usd']})")
    for it in res['cart_items']:
        print(f"* {it['item_name']} x{it['order_quantity']} (${it['total_cost_usd']}) - Reason: {it['urgency_reason']}")

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