MCP 服务器

shopops-mcp

AI e-commerce operations manager for MCP. Inventory forecasting, pricing optimization, RFM customer segmentation, order anomaly detection, and automated reports for Shopify and WooCommerce.

README

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 stdio and Streamable HTTP (MCPize).
TypeScript, @modelcontextprotocol/sdk v1.29+, 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.