What this tutorial covers

Model Context Protocol, or MCP, is an open client-server protocol for connecting AI applications with external tools and systems. In the verified research context, those tools include web search, database queries, API calls, code execution, and device control. An MCP client can obtain a list of available tools from an MCP server, provide the tool descriptions to a model, receive a requested tool call, invoke the tool, and return the result to the model.

That interoperability is valuable, but interoperability is not authorization. A tool description is not proof that a server is safe. A model request is not proof that the caller is allowed to perform an action. A returned result is not automatically trustworthy context. This distinction is the central design principle for a secure MCP gateway.

This tutorial describes a security boundary for an AI application built around a Next.js web layer and an OpenAI model integration. The verified context does not establish particular Next.js, OpenAI, or MCP SDK versions, so the tutorial intentionally avoids presenting unverified package commands or pretending that a framework configuration alone provides security. The design can be implemented with those technologies, but the controls must live in your server-side policy layer.

Why an MCP gateway needs an execution boundary

The MCP interaction loop creates several trust transitions. A host application connects an MCP client to one or more servers. The client receives tool names, descriptions, and input requirements. The model uses that context to decide whether to request a tool. The client then invokes the selected tool and sends the result back into the model conversation.

Each transition can carry risk. Tool descriptions may be incomplete, misleading, or deliberately crafted to influence an agent. Tool implementations can change after review. A server may call an endpoint that is different from the endpoint suggested by its source code or documentation. Results may include secrets, personal information, internal URLs, or instructions that should not be followed. The model may also request a tool with arguments that are syntactically valid but outside the caller’s business permission.

The verified AgentBound research describes a concrete attack pattern involving an apparently innocuous maps server. When a function is executed, its code can change from a legitimate API location to a malicious one, enabling outcomes that range from data exfiltration to downloading and executing malware. This example is why a gateway must verify more than the tool name and JSON shape.

The correct mental model is a reference monitor. The model proposes an action. The gateway independently decides whether that action is permitted. The MCP client transports the request and result, but it does not replace identity, authorization, destination controls, data classification, or monitoring.