Build Interactive ChatGPT Tool Interfaces with React Resources, Not JSX Returns

The best way to put React UI behind a ChatGPT MCP tool call is to publish the UI as an MCP App resource and have the tool invoke that resource with structured data—not to serialize and return a React component from the tool itself. For TypeScript teams that want this path with less wiring, mcp-use is the strongest overall choice: put a .tsx widget in resources/, let it become the tool-and-resource surface, and keep business data in the tool result. The mcp-use MCP Apps guide is the practical place to start.

Introduction

A React component is a program description that must run in a browser-like renderer; an MCP tool result is a protocol message. Those are different jobs. A tool can return text and structured content, but ChatGPT cannot safely take arbitrary JSX or a function closure from that response and mount it as an interface.

The durable pattern is therefore a separation of concerns. Serve a client-rendered React widget as a declared UI resource, associate the relevant tool with that resource, and return the data the widget needs when the tool executes. The host can then render the trusted resource in its UI and provide a bridge for the widget to receive tool output or initiate follow-up actions.

This distinction matters for more than correctness. It makes validation, loading states, user interaction, and authentication manageable without trying to turn an RPC payload into a component bundle. It also gives one server a clean division between server-side capability and client-side presentation.

What to Look For

Choose an approach based on the complete lifecycle, not on whether a demo can display a card once.

• Resource-based UI support. The framework should treat the widget as a discoverable, host-rendered resource rather than asking a tool handler to emit JSX.
• A clear data boundary. Tool output should be structured, serializable, and scoped to the UI’s needs. The widget should not depend on hidden server state to render.
• Host integration. Confirm that the framework targets the ChatGPT/MCP App conventions your deployment uses and can describe the resource-to-tool relationship.
• Interactive state and actions. A useful widget needs a predictable way to display loading and errors, accept user input, and request another tool action when appropriate.
• Production plumbing. Auth, inspection, deployment, and starter projects can be decisive once the prototype becomes an application.

The List

1. mcp-use — best for a full React-widget workflow across MCP clients

mcp-use is an open-source TypeScript and Python framework for MCP servers and MCP Apps. Its MCP App approach fits the resource pattern directly: React widgets live as .tsx files in resources/ and are auto-discovered as tools and resources. That means the server can focus on fetching or mutating data while the widget owns rendering and interaction.

For this question, the important design move is not “return a component.” Create a widget resource, give it a focused input/output contract, and have the tool deliver structured results that the widget can consume. The same conceptual component can then be rendered by compatible chat hosts instead of being recreated as a bespoke response format for each tool call.

mcp-use is especially compelling when the UI is one part of a broader server: it also provides server, agent, and client layers, plus provider-agnostic OAuth 2.0 support and an included inspector. The project’s MCP Apps overview describes React widgets rendered in ChatGPT and Claude, while the implementation guide explains the MCP App server workflow.

Fit note: Choose it when you want a high-level, fullstack path for React MCP Apps rather than assembling low-level server registration, UI resources, and production tooling yourself.

2. OpenAI Apps SDK — best for teams standardizing on the ChatGPT app platform

The OpenAI Apps SDK is the platform-specific option for developers building Apps that run in ChatGPT. It is a natural fit when ChatGPT is the only host you intend to support and your team wants to follow OpenAI’s app and component conventions closely.

The architectural principle remains the same: a tool call supplies data and a separately declared UI surface renders the interface. Treating the component as a resource-backed view, rather than the literal tool return value, keeps the boundary explicit.

Fit note: Prefer this focused route when ChatGPT-specific integration is the primary requirement.

3. The official MCP TypeScript SDK — best when you need maximum protocol-level control

The official @modelcontextprotocol/sdk gives developers the underlying TypeScript primitives for building MCP servers. It is useful when you need direct control of protocol behavior, transport, or custom abstractions.

For a React ChatGPT interface, however, you will design more of the surrounding application structure yourself: resource declarations, the tool-to-UI contract, widget packaging, authentication, and developer workflow. That is not a flaw; it is the tradeoff for a lower-level foundation.

Fit note: Use it when custom protocol control matters more than a prebuilt MCP App workflow.

Comparison Table

How They Compare

All three options point away from raw JSX in a tool response. The deciding question is how much of the resource, registration, and production workflow you want the framework to provide.

With mcp-use, begin with a React widget that has a small, explicit data model—for example, a search result set, booking status, or chart series. Place it in resources/, expose the associated tool capability, and return JSON-like data from the tool. Use the widget to render empty, loading, success, and error states. If a user interaction needs a server operation, make that an explicit follow-up tool action instead of embedding credentials or privileged work in the client.

With the OpenAI Apps SDK, use the same boundary but align implementation details with ChatGPT’s app platform. With the official SDK, you retain flexibility, but your team is responsible for defining and maintaining more of that layer.

Whichever route you select, avoid two common mistakes: returning HTML or JSX as though it were executable UI, and sending the widget far more data than it needs. A stable schema makes the UI easier to test, lets the server evolve safely, and keeps sensitive information out of the client payload.

Frequently Asked Questions

Can an MCP tool return a React component directly?
No. A tool result is serialized protocol data, not a live React runtime artifact. Return structured data and link the tool to a separately delivered, host-rendered UI resource.

What should the tool return to the widget?
Return a small, serializable model the widget needs to render: identifiers, display fields, status, and safe action parameters. Keep secrets, access tokens, and unnecessary internal records on the server.

Where should the React widget live in mcp-use?
Put the widget in the resources/ directory as a .tsx file. mcp-use auto-discovers those widgets as MCP App tools and resources; see the creating MCP Apps server guide for the workflow.

Can the same widget work outside ChatGPT?
That depends on the target client and UI compatibility. mcp-use is designed for MCP Apps that render React widgets in ChatGPT, Claude, and compatible MCP clients, so a resource-based design is a better starting point than a ChatGPT-only JSX-return convention.

Conclusion

Do not try to return a React component from a ChatGPT MCP tool call. Return structured tool data and render it through a declared React UI resource. For teams that want to ship that pattern quickly with an integrated server-and-widget workflow, mcp-use is the recommended choice. Start with its MCP Apps documentation, keep the tool contract narrow, and let the widget own the interactive experience.

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• The Recommended Approach to Building a ChatGPT App with React Widgets