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smart-retail-mcp

smart-retail-mcp

by eneidyscastellano
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

🤖 Smart Retail MCP Server

Tool server based on Model Context Protocol (MCP) that provides predictive inventory analysis capabilities to AI agents.

Available Tools

Tool

Description

get_current_stock

Gets the current inventory of all products along with supplier delivery times

get_sales_velocity

Calculates the average daily sales velocity per product over the last N days

create_ai_recommendation

Creates restocking alerts when stock will run out before the next delivery

Related MCP server: vanam-erp-mcp

Architecture

src/
├── index.ts   → Definición e implementación de herramientas MCP (stdio transport)
├── db.ts      → Pool de conexión a PostgreSQL
└── seed.ts    → Script para insertar datos de prueba

Configuration

Environment variables (.env)

DATABASE_URL=postgresql://usuario:password@localhost:5432/smart_retail

Scripts

npm start      # Ejecutar el servidor MCP
npm run dev    # Desarrollo con hot-reload (tsx watch)
npm run seed   # Insertar datos de prueba
npm run build  # Compilar TypeScript

Dependencies

  • @modelcontextprotocol/sdk — Official MCP SDK

  • pg — PostgreSQL client

  • dotenv — Environment variables

Communication

The server communicates via stdio (stdin/stdout). The Next.js frontend starts it as a child process using StdioClientTransport from the MCP SDK. It does not expose HTTP ports.

License

ISC

Available Tools

3 tools
create_ai_recommendationA

Crea una alerta de reabastecimiento en la base de datos cuando detectas que el stock se agotará antes de que el proveedor pueda entregar.

Inputs: reason (required), product_id (required), recommended_order_qty (required).

get_current_stockA

Obtiene los niveles actuales de inventario de todos los productos y los tiempos de entrega del proveedor.

Inputs: no named parameters.

get_sales_velocityA

Calcula el promedio de ventas diarias de los productos en los últimos X días. Útil para predecir cuándo se agotará el stock.

Inputs: days (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool targets a distinct inventory concern: current stock and lead times, sales velocity, and creating restock alerts. There is no meaningful overlap, so an agent can select the correct tool without ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: get_current_stock, get_sales_velocity, create_ai_recommendation. The naming is predictable and clear.

    Tool Count4/5

    Three tools is at the low end, but each tool has a clear role in the inventory-alerting workflow. The count feels appropriate for a narrow purpose, though the broad server name could imply more coverage.

    Completeness2/5

    The set covers the input side well, but create_ai_recommendation is a create-only operation with no way to list, update, or delete existing recommendations. This leaves agents with a dead end for managing alert state.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

    Resources

    Related MCP Connectors

    Related MCP Servers

    • vanam-erp-mcp

      LeeJunHeon
      F
      license
      A
      quality
      D
      maintenance
      MCP server for querying inventory items and stock levels via internal API, enabling AI chatbots to look up product codes and current quantities.
      2
      -

    • SupplyChainMCP

      amitverma102
      F
      license
      Not graded
      quality
      C
      maintenance
      Enterprise-grade MCP server for intelligent supply chain analytics, enabling managers to investigate forecast accuracy, vendor performance, demand trends, and root causes using natural language.
      -