Agentic Inventory Restock Predictive Balancer
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 Inventory Restock Predictive BalancerCheck stock for ECOFLOW-DELTA-PRO-3 and auto-generate a purchase order if it needs restocking."
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.
genpark-agentic-inventory-restock-predictive-balancer-skill
Autonomous Multi-Channel Inventory Restock Predictive Balancer & PO Generator. Forecasts SKU sales velocity using exponential moving averages (EMA), calculates safety stock buffers ($SS = z \cdot \sigma_d \sqrt{L}$), dynamically computes reorder points (ROP), and synthesizes supplier Purchase Orders.
🌟 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, andtools/call.Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
Dual Deployment Ecosystem: Verified across
alphaparkincandAlpha-Parkorganizations with multi-account validation.
Related MCP server: StockPilot-MCP-Server
🚀 Quick Start
1. Direct Python SDK Usage
"""Example usage for AgenticInventoryRestockPredictiveBalancer."""
import sys
import json
from client import AgenticInventoryRestockPredictiveBalancer
sys.stdout.reconfigure(encoding='utf-8')
def main():
print("=== Agentic Commerce Supply Chain Inventory Balancer Demo ===")
balancer = AgenticInventoryRestockPredictiveBalancer()
sales_7d = [45.0, 52.0, 48.0, 55.0, 50.0, 60.0, 53.0] # mean ~51.8 units/day
print("\n--- 1. Evaluating SKU Run-Rate and Reorder Point (ROP) ---")
eval_res = balancer.evaluate_sku_inventory(
sku="ECOFLOW-DELTA-PRO-3",
current_stock=180,
lead_time_days=7,
sales_history_7d=sales_7d,
unit_cost_usd=1200.00
)
print(f"Mean Daily Demand: {eval_res['mean_daily_sales']} units")
print(f"Safety Stock Buffer: {eval_res['safety_stock_units']} units")
print(f"Reorder Point: {eval_res['reorder_point_units']} units")
print(f"Current Stock: {eval_res['current_stock']} units (Days Left: {eval_res['days_of_inventory_remaining']})")
print(f"Needs Restock: {eval_res['needs_restock']} (Status: {eval_res['restock_urgency']})")
if eval_res["needs_restock"]:
print("\n--- 2. Synthesizing Autonomous Supplier Purchase Order ---")
po = balancer.generate_purchase_order(
sku="ECOFLOW-DELTA-PRO-3",
supplier_id="SUP-ECOFLOW-OFFICIAL",
evaluation=eval_res
)
print(f"Created PO #{po['purchase_order_id']} for {po['ordered_quantity']} units")
print(f"Estimated Total PO Cost: ${po['total_estimated_amount_usd']:,.2f}")
if __name__ == "__main__":
main()
2. Run as Model Context Protocol (MCP) Server
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute 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
Licensed under the MIT License. Copyright © 2026 GenPark AI.
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