Item MCP Overview
The Model Context Protocol (MCP) is an open standard that connects large language models (LLMs) with external tools and data sources. It allows AI systems to understand and interact with real-world context by breaking down integration barriers and eliminating data silos.
What is MCP
MCP follows a client-server architecture, where a host application connects to one or more servers to access specific capabilities.
General Architecture
| Component | Description |
|---|---|
| MCP Hosts | Applications like Claude Desktop, IDEs, or AI chat tools that interact with MCP servers. |
| MCP Clients | Lightweight clients that maintain direct (1:1) communication with a specific MCP server. |
| MCP Servers | Small, pluggable programs that expose structured capabilities (e.g., data access, task execution) via the MCP protocol. |
| Local Data Sources | Files, databases, or services on your computer that MCP servers can securely access. |
| Remote Services | Online APIs or systems (e.g., e-commerce platforms, CRM, ERP) that MCP servers can connect to. |
For more information, please refer to the MCP Official Documentation (opens in a new tab).
What is Item MCP
Item MCP is an open-source platform that provides a centralized hub for deploying and managing MCP servers specifically tailored for Item’s ecosystem (Item DI, OMS, WMS, BNP, etc.).
What It Offers
- Standardized server deployment for various system connectors.
- MCP server marketplace for quick installation of prebuilt integrations.
- Multi-agent collaboration via AI Chat.
- Configurable AI agents and task scheduling.
- Secure access control for sensitive data.
Why Integrate with Item MCP
| Benefit | Description | Example |
|---|---|---|
| Context-Aware AI | Provide real-time system context to AI models | Automatically link order info with shipment data |
| Standardized Protocol | Use a unified API interface across systems | All agents follow the same structure |
| Cross-System Operations | Support multi-agent workflows | Create Shopify order → auto-fetch shipment info |
| Smart Task Scheduling | Assign tasks to different agents | One handles orders, another manages inventory |
| Reduced R&D Costs | Add new capabilities without touching core code | Integrate customer support system via a new agent |
| Secure Access Control | Only authorized agents access specific data | Sensitive data protected by role-based permissions |
Key Features
item MCP Server VS item MCP Client
| Aspect | MCP Server | MCP Client |
|---|---|---|
| What it is | A centralized data platform that unifies and standardizes information from multiple sources and provides secure interfaces for external systems to access data. | An application that uses the MCP protocol to call data interfaces, enabling intelligent task automation and improving human-computer interaction. |
| Why it matters | Solves issues like data silos, delays in data flow, and cross-system collaboration challenges in industries such as warehousing and logistics. | Helps users make informed decisions, facilitates customer service, and handles tasks like replenishment and exception management. |
| Target Users | System administrators and implementation teams responsible for configuring and deploying the server. | Data entry teams, customer service representatives, and end-users who interact with the system. |
| Use Cases | 1) Updates and pushes OMS inventory to third-party platforms (e.g., OMS → Shopify). 2) Pushes order delivery information to third-party platforms (e.g., OMS → Shopify). | 1) User requests: Querying OMS order status. 2) Automatic task triggering: Low inventory warnings, system notifications, and actionable suggestions. |
| Business Scenarios | Integrates with OMS and other systems to manage order fulfillment and receive updates on warehouse and transportation system statuses. | Provides customer service Q&A, order status queries, and system status monitoring via mobile or web interfaces. |
| How it works | Configures data access sources and permission rules, exposes standard APIs that clients call for data. | Users interact through web or mobile interfaces, sending requests, receiving responses, or triggering actions. |
| Example | Updating OMS data to Shopify, managing integrations between systems. | Querying order status, receiving low-inventory alerts, or handling customer service tasks. |
Terminology
This glossary defines key terms you'll encounter when working with MCP clients and servers. It includes foundational concepts, communication structures, and configuration terms commonly used during setup, testing, and deployment.
Core Concepts
MCP (Model Context Protocol)
A lightweight protocol that allows AI clients to invoke structured tools provided by external servers.
MCP Server
A standalone program (Node.js or Python) that exposes callable tools using the MCP schema and responds over a supported connection type.
MCP Client An interface (e.g., AI chat, command line, dashboard) that connects to an MCP server, discovers its tools, and triggers tool executions.
Tool
A unit of functionality defined by an MCP server. Each tool includes a name, description, input schema, and an execute() method.
Tool Call A request from the client to invoke a specific tool with parameters, following MCP JSON format.
Capabilities File (capabilities.json)
A JSON manifest listing all tools a server provides, their schemas, and metadata. This is how clients discover what a server can do.
Prompt Template A predefined instruction that guides the AI’s response style or behavior during tool usage. Configured on the client side.
Resource A data object (e.g., file, folder, image) referenced in context but not directly executed like a tool. Resources often support agent reasoning.
Connection Types
STDIO (Standard I/O) A local connection method where the server is launched as a subprocess and communicates via standard input/output streams.
SSE (Server-Sent Events) A remote connection method where the client receives streamed JSON responses from a web-based API server.
Streamable HTTP
A type of server connection that supports continuous data exchange without closing the HTTP connection after each response, providing a more efficient way for AI model servers or integration endpoints to send updates or partial results progressively.
Configuration Parameters
Command
The system command used to launch the server (required in STDIO mode), "python" or "node".
Arguments (Args)
A list of arguments passed to the command (usually the server script path), ["index.js"].
Environment Variables (Env)
Optional variables used to configure runtime behavior (e.g., auth paths, system tools), { "UV_PATH": "/usr/bin/uv" }.
Server URL
The entry point for an SSE-based server (usually a web API endpoint), "https://api.example.com/mcp".
Headers
Optional metadata or authentication passed in HTTP requests for SSE servers, { "Authorization": "Bearer token" }.
Server Type
Declares the connection method (STDIO or SSE) when adding or registering a server, "stdio" or "sse".
Debugging & Testing
MCP Playground / Inspector A visual testbed for connecting to MCP servers, running tools, and viewing results in real time. Useful for debugging before deploying.
Tool History A log of previous tool calls and their outcomes (input → output). Helpful for reviewing session behavior.
Session A persistent communication state between a client and a server, allowing tools to be called with context.