ShopOps MCP
AI-powered server that implements the Model Context Protocol (MCP) for managing Shopify and WooCommerce stores.
Features
- Store connectors for Shopify and WooCommerce.
- 11 MCP tools covering inventory, pricing, customers, orders, product performance and reporting.
- 4 MCP resources exposing store overview, inventory, recent orders and top customers.
- Inventory forecasting using moving-average demand plus safety-stock calculation.
- RFM-based customer segmentation (7 distinct segments).
- AI-driven pricing analysis and optimization.
- Order anomaly / fraud detection.
- ABC analysis of product performance.
- Automated daily and weekly reports.
- Dual transport: local
stdioand Streamable HTTP (MCPize). - TypeScript,
@modelcontextprotocol/sdkv1.29+, Zod v4. - Free tier, plus $25 and $45 paid plans.
Quick Start
# 1. Install the package
npm i shopops-mcp
# 2. Create a .env file (see Configuration section)
cp .env.example .env
# 3. Run the server (local stdio mode)
npx shopops-mcp run --transport stdio
# 4. Or start the HTTP endpoint (MCPize deployment)
npx shopops-mcp run --transport http --port 8080
The server will read the environment variables, connect to the configured store(s), and expose the MCP tools and resources.
MCP Tools
| Tool | Description |
|---|---|
store_connect |
Establishes a connection to a Shopify or WooCommerce store and validates credentials. |
inventory_status |
Returns current stock levels, back-order flags and low-stock alerts. |
inventory_forecast |
Projects future inventory requirements using moving-average demand and safety-stock buffers. |
pricing_analyze |
Generates a price elasticity report and identifies under-/over-priced SKUs. |
pricing_optimize |
Suggests optimal price points based on AI-driven demand forecasts and competitor data. |
customers_segment |
Performs RFM analysis and assigns customers to one of seven segments. |
customers_churn |
Scores customers for churn risk and provides retention recommendations. |
order_anomalies |
Detects potentially fraudulent or erroneous orders using pattern-recognition models. |
product_performance |
Conducts ABC analysis and returns contribution metrics per product class. |
report_daily |
Generates a JSON/CSV daily operations summary (sales, inventory, alerts). |
report_weekly |
Generates a weekly performance report with trend visualisations. |
MCP Resources
| Resource | Description |
|---|---|
store://overview |
High-level store metrics: total sales, orders, customers, and gross margin. |
store://inventory |
Full inventory catalogue with quantity on hand, reserved stock and forecasted shortages. |
store://orders/recent |
List of the most recent 100 orders with status, total value and payment method. |
store://customers/top |
Top 50 customers ranked by lifetime value, purchase frequency and recency. |
Configuration
Create a .env file at the project root. The following variables are required:
| Variable | Required for | Description |
|---|---|---|
SHOPIFY_API_KEY |
Shopify | Private app API key. |
SHOPIFY_API_PASSWORD |
Shopify | Private app password. |
SHOPIFY_STORE_DOMAIN |
Shopify | Store domain (e.g., myshop.myshopify.com). |
WOOCOMMERCE_CONSUMER_KEY |
WooCommerce | REST API consumer key. |
WOOCOMMERCE_CONSUMER_SECRET |
WooCommerce | REST API consumer secret. |
WOOCOMMERCE_STORE_URL |
WooCommerce | Store URL (e.g., https://example.com). |
MCP_PORT |
HTTP transport | Port for the Streamable HTTP endpoint (default 8080). |
MCP_LOG_LEVEL |
All | Logging verbosity (error, warn, info, debug). |
MCP_PRICING_MODEL |
Pricing tools | Select pricing model (basic, advanced). |
MCP_FORECAST_WINDOW_DAYS |
Inventory forecast | Number of days to forecast (default 30). |
Optional variables:
| Variable | Description |
|---|---|
MCP_ENABLE_ANONYMIZATION |
When set to true, personally identifiable data is masked in reports. |
MCP_REPORT_S3_BUCKET |
If provided, daily/weekly reports are uploaded to the specified S3 bucket. |
License
ShopOps MCP is released under the MIT License. See LICENSE for full terms.
Author: Automatia BCN
