AI Agent Integration (MCP Server)

Your Agent. Your Data. Our Math.

Every AI agent is only as good as the data behind it. Ask ChatGPT "how much inventory should I order?" and you get a textbook answer. Ask an agent connected to your actual stock levels, demand forecasts, and supplier lead times, and you get a number you can act on.

ReplenishRadar ships an MCP package that gives compatible AI agents structured access to your inventory data. Not a chatbot. Not a prompt wrapper. A set of tools that let your agent query real data and execute real workflows (stockout risk, demand forecasts, purchase order drafts, alerts) through the same math engine that powers the rest of the platform.

MCP (Model Context Protocol) is the standard many agent clients now use to interact with external systems in a structured, type-safe way. We built a purpose-built MCP package for multi-channel Shopify and Amazon inventory.

What Your Agent Can Do

The MCP package exposes 49 tools organized by function:

Monitoring and analysis

• Check stockout risk across all SKUs, ranked by urgency
• Pull inventory positions across every channel (Shopify on-hand, FBA available, FBA inbound)
• Get demand forecasts with configurable horizons
• Surface active alerts: stockout warnings, overstock flags, sync failures, forecast anomalies

Purchasing workflows

• Get suggested purchase orders grouped by supplier
• Create draft POs with supplier, SKUs, quantities, and reasoning
• Account for MOQs, casepack sizes, and working capital constraints

Data retrieval

• List items with filtering by supplier, status, or channel
• Get supplier details including lead times and ordering history
• Trigger inventory syncs to pull fresh data from Shopify and Amazon

PO creation through the public agent flow always starts as a draft, and sending requires prior approval. Write-capable keys can also trigger syncs, acknowledge alerts, add action notes, and set stock at manual locations when a human admin grants those tool groups. Agents cannot change Shopify or Amazon listings or pricing through this MCP surface.

The Approval Flow

When your agent drafts a purchase order, here is what happens:

1. The agent calls rr_create_purchase_order with supplier, SKUs, quantities, and a reason
2. ReplenishRadar creates the PO in draft status
3. The agent or workflow calls rr_request_approval to generate a signed approval URL
4. You click the link, review the details, and approve or reject
5. Only after approval can rr_send_purchase_order send the PO

The signed URL expires. It is org-scoped and cryptographically signed. Nobody else can use it. No agent can click it for you.

Every agent call is rate-limited, org-scoped, and logged. The agent is a very fast assistant that needs your signature on purchasing checks.

Pick Your Agent

The MCP package works with agent platforms that support local stdio MCP servers. We have tested and documented four setups:

Setup	Time	Best For	Cost
Claude Desktop	5 min	Morning briefings, ad hoc queries	$20/mo (Claude Pro)
OpenClaw	20-30 min	Teams, Slack-native workflows, scheduled alerts	Free (self-hosted)
Custom Python	1-2 hrs	Complex automations, scheduled reports	~$0.01-0.03/query
n8n / Make	30-60 min	Alert routing, no-code workflows	Free tier available

Claude Desktop is where most sellers start. Edit one JSON config file, restart the app, and start asking questions. Five minutes from zero to "show me my low-stock SKUs."

For background automation (scheduled morning briefings, reactive stockout alerts, auto-drafted POs), OpenClaw or a custom agent adds the persistence layer.

What This Looks Like in Practice

Monday, 7:02 AM. Open Claude Desktop. Type: "Morning briefing. Stockout risks, open alerts, anything weird."

The agent checks every SKU's position against its reorder point, factors in open POs and inbound inventory, and comes back with a ranked list. Three SKUs need attention. Two are covered by incoming shipments. You know this in 30 seconds instead of 20 minutes.

Monday, 7:03 AM. "Draft a PO for the Silicone Spatula Set. 500 units, same supplier as last time."

The agent creates a draft PO. You get a Slack notification with the details and an approval link. One click. Done before your coffee is ready.

That daily triage, the one that used to take 15-20 minutes of dashboard-clicking, is now a 30-second conversation. Over a year, that is roughly 80 hours of monitoring time returned to you.

Tier Access

Capability	Standard ($99/mo)	Growth ($199/mo)	Scale ($499/mo)
Inventory, forecast, alert, and PO suggestion reads	Yes	Yes	Yes
Basic write tools such as notes and safe workflow actions	Yes	Yes	Yes
Sensitive read tools such as costs, suppliers, and full audit detail	No	Yes	Yes
Sensitive write tools such as draft PO creation, sync triggers, approvals, and manual stock updates	No	Yes	Yes
API rate limit	10/hour	100/hour	1,000/hour

Standard is enough for low-volume agent checks. Growth adds the sensitive inventory and purchasing surface most sellers need for daily briefings and draft POs. Scale raises the rate limit for background agents, scheduled checks, and larger catalogs.

The Rails, Not the Bot

ReplenishRadar is the math engine. Your agent is the interface. We provide the data: inventory positions, demand forecasts, supplier lead times, ordering constraints, safety stock levels. The agent queries it, presents it conversationally, and executes workflows on your behalf.

Without structured data underneath, an agent has nothing useful to say. With it, the gap between "I should check my inventory" and actually having the answer goes from 20 minutes to 30 seconds.

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Related Features

• Purchase Orders - The PO engine that agents draft against
• Demand Forecasting - The forecasts agents query
• Unified Inventory - The stock data agents see

Learn More

• Connect Your AI Agent to Inventory (MCP Guide) - 5-minute setup walkthrough
• AI Purchase Orders: Automate Reordering Safely - How agent-drafted POs work
• What Can an AI Agent Do With Your Inventory Data? - Use cases and limitations
• Best AI Agent Setup for Amazon and Shopify - Platform comparison

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