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., "@genpark-agentic-supply-chain-bullwhip-dampener-skill Compute dampened reorder for SKU-9901-PREMIUM with 110 in stock and 7-day lead time."
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.
Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)
AI Supply Chain & Commerce Bullwhip Effect Dampener. Applies safety stock PID dampening, order surge smoothing, and lead-time demand volatility filtering to eliminate inventory whipsawing across multi-tier supplier networks.
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: forecast-mcp
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.
from client import AgenticSupplyChainBullwhipDampener
def main():
dampener = AgenticSupplyChainBullwhipDampener()
orders = [45, 50, 48, 52, 49, 120]
res = dampener.compute_dampened_reorder_quantity("SKU-9901-PREMIUM", current_inventory=110, recent_order_history=orders, lead_time_days=7)
print("--- Supply Chain Bullwhip Dampener ---")
print(f"SKU: {res['sku_id']} | Surge Ratio: {res['surge_ratio_detected']}x")
print(f"Damping Factor Applied: {res['bullwhip_damping_factor']}")
print(f"Recommended Replenishment: {res['recommended_order_units']} units")
if __name__ == "__main__":
main()
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute embedded test harness:
python mcp_server.py --testInspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.
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
Choose and rank suppliers, size orders and classify spend from delivery and purchase data.
Find the cash trapped in your inventory, then size reorder points, safety stock and EOQ.
Read-only supply-chain decision support: forecasting, reorder policies, ABC-XYZ, data quality.
AI inventory management: stock, Demand ESP reorder recommendations and purchase orders.
