Unlock Seamless AI‑Tool Interaction with the Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open‑source interface that standardizes how large language models interact with external data sources and tools, offering a USB‑C‑like universal connector for AI applications, with built‑in session management, security, and flexible HTTP/SSE transport for seamless real‑world integration.
In the era of rapid AI model development, the Model Context Protocol (MCP) enables natural‑language interaction between AI applications and real‑world data sources, allowing users to query logistics information with simple commands.
MCP, originally released by Anthropic, is an open‑source protocol that standardizes bidirectional communication between large language models and external tools such as files, databases, and APIs. It is designed as a universal interface comparable to a USB‑C port.
Participants in the MCP ecosystem:
MCP Host: Coordinates and manages one or more MCP Clients (AI applications).
MCP Client: Maintains the connection to the MCP Server and obtains contextual information for the Host.
MCP Server: Provides contextual data to the Client.
The protocol addresses key challenges in distributed environments, including SSE session management with Node routing, rapid adaptation of existing services, tool resource isolation, and sensitive data compliance.
The MCP gateway adds core capabilities:
Transport layer supporting both HTTP+SSE and Streamable HTTP.
SSE mode with Redis‑managed session state and load‑balancing for dynamic routing.
OpenAPI‑to‑MCP conversion, allowing services described by OpenAPI to be exposed as MCP services.
Authentication, quota limiting, and observability at the AppKey/Tool level.
Enhanced data‑security layer using risk‑control policies for end‑to‑end protection of sensitive information.
Typical workflow:
User sends a request to an AI application, which routes the traffic to the MCP gateway.
The gateway manages the session, applying authentication, IP whitelisting, rate limiting, and load balancing.
The AI app queries the gateway for a list of available MCP Tools; the gateway filters the list based on the AppKey and returns it.
The AI app selects a tool; the gateway forwards the request to the appropriate backend service (HTTP/RPC).
The gateway returns the tool’s response to the AI application.
To integrate MCP, developers should register on the Zhongtong Open Platform, complete enterprise certification, create a developer application, apply for MCP access, add MCP services, configure IP whitelists, publish the app for review, and finally invoke MCP Tools after approval.
The platform continuously expands its MCP capability catalog, supporting both Streamable HTTP and HTTP SSE, with a recommendation to use Streamable HTTP for integration.
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