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ReplenishRadar MCP

by ReplenishRadar

Connects AI agents to ReplenishRadar inventory data for Shopify and Amazon workflows with guarded replenishment actions.

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Install ReplenishRadar MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "replenishradar-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@replenishradar/mcp-server"
      ],
      "env": {
        "REPLENISHRADAR_API_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y @replenishradar/mcp-server

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 19d ago.

Overview

ReplenishRadar/MCP MCP server connects MCP-compatible agents to live ReplenishRadar inventory data for Shopify and Amazon sellers. It exposes read tools for stockout risk, inventory position, forecasts, purchase orders, sales, alerts, SKU health, sync status, and store health. Write tools cover notes, approvals, alert acknowledgement, and replenishment workflows, with draft-first and approval safeguards for purchasing and stock changes. Use it when an agent needs inventory analysis or controlled replenishment operations through a ReplenishRadar API key.

Use cases

  • Identify SKUs at risk of stockout
  • Review demand forecasts and replenishment recommendations
  • Inspect purchase-order history and attached documents
  • Analyze top sellers, slow movers, and lost sales
  • Prepare draft replenishment actions for human approval

Key features

  • Inventory and stockout-risk analysis
  • Demand forecasting and sales analytics
  • Purchase-order and supplier visibility
  • Replenishment actions with draft-first execution
  • Alerts, sync, freshness, and store-health checks
  • Human approval and guarded write workflows

Tools49

What ReplenishRadar MCP lets your agent do. Expand a tool for its parameters.

Self-reported~733 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

18/19 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 12d ago via OSV.dev · @replenishradar/mcp-server (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars2Stargazers on the source repository.
npm downloads241Package downloads in the last 30 days.
Last commit2mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Tools exposed49Callable tools this server registers over MCP.
Directory activity4 viewsConfig copies, upvotes, and views on AllMCPs.

How ReplenishRadar MCP works

What ReplenishRadar/MCP MCP server does

ReplenishRadar/MCP connects an MCP-compatible AI client to live inventory data held by ReplenishRadar. The service is intended for sellers operating across Shopify and Amazon channels. Agents can inspect inventory conditions, identify replenishment needs, review purchasing records, and retrieve operational or product knowledge information.

The read surface covers stockout risk, inventory by location, demand forecasts, suggested purchase orders, replenishment actions, active alerts, items, suppliers, purchase orders, sales history, top sellers, slow movers, inventory value, SKU health, estimated lost sales, store health, data freshness, setup progress, recent activity, and synchronization history. It also includes a knowledge search and a deterministic fit evaluation that does not use organization data.

How it works

The client launches the package as a local stdio MCP process with npx, then supplies a ReplenishRadar API key through the process environment. Tool calls use that authenticated connection to query the organization’s ReplenishRadar data. The repository documentation identifies Claude Desktop, OpenClaw, and MCP-compatible clients as supported connection options.

Write access is separated by capability and plan. Basic writes include acknowledging alerts, adding purchase-order or replenishment-action notes, requesting approval, and managing documented action workflows. Sensitive operations include syncing inventory, creating or changing purchase orders, sending or cancelling orders, creating locations, and setting stock at manual locations.

Purchase orders begin as drafts. The default replenishment execution mode creates a draft purchase order or transfer without sending it to a supplier or committing stock. Autonomous execution can send or commit only when the API key has the required capabilities, operation group, enabled budget, and passing guardrails. Preparation, timestamps, idempotency keys, and approval fallbacks support safer retries and stale-state handling.

Setup and configuration

Obtain an API key from ReplenishRadar under Settings > API Keys. Add a server entry to the MCP client configuration using the @replenishradar/mcp-server package and set REPLENISHRADAR_API_KEY to the key value. Claude Desktop configuration is edited from Settings > Developer > Edit Config, followed by a client restart.

The repository also documents a public agent-intake endpoint that can provide a short-lived, read-only sandbox key for users without an account. API-key permissions, namespaces, sensitive capabilities, and autonomous spend budgets are managed by human administrators in ReplenishRadar settings rather than through MCP tools.

Tools and capabilities

ReplenishRadar/MCP includes tools for:

  • Reading inventory, demand, sales, alerts, suppliers, purchase orders, documents, notes, and synchronization state.
  • Reviewing replenishment actions and their event history.
  • Searching ReplenishRadar product knowledge and evaluating product fit without organization data.
  • Acknowledging alerts, adding notes, and requesting human approval.
  • Preparing, executing, resolving, retrying, or dismissing replenishment actions where the API key and plan permit.
  • Managing documented agent-context entries as bounded, organization-scoped JSON objects when the relevant namespace permissions are granted.

Plan limits documented for the service are 10 calls per hour on Standard, 100 on Growth, and 1,000 on Scale. The available read, basic-write, sensitive-write, and diagnosis/status capabilities vary by plan.

Read the full README View source on GitHub

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Frequently Asked Questions about ReplenishRadar MCP

ReplenishRadar/MCP is an MCP server that connects AI agents to live ReplenishRadar inventory data for Shopify and Amazon sellers. Its main tools cover stockout risk, inventory position, forecasts, purchase orders, sales analytics, alerts, replenishment actions, and controlled writes such as notes, approvals, and draft purchase orders.

Install it by adding the `@replenishradar/mcp-server` package to your MCP client configuration with `npx` and setting `REPLENISHRADAR_API_KEY`. In Claude Desktop, edit the configuration from Settings > Developer > Edit Config, then restart the client.

Yes, it works with Cursor as an MCP-compatible client. The repository states that the server works with any MCP-compatible client; configure the stdio command and provide the `REPLENISHRADAR_API_KEY` environment variable.

By default, no: replenishment execution creates a draft and does not send it to a supplier. Autonomous sending is possible only when the API key has the required sensitive-write permissions and operation group, an enabled budget, and all guardrails pass; otherwise the action falls back to a draft or is refused.

It requires a ReplenishRadar API key supplied as `REPLENISHRADAR_API_KEY`. Users can obtain a key from ReplenishRadar Settings > API Keys, while users without an account can request a short-lived, read-only sandbox key through the documented public agent-intake endpoint.

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