The Practical Stack for Shipping MCP UI Across ChatGPT and Claude

The best way to render UI from an MCP server in both ChatGPT and Claude is to build an MCP App around a cross-client UI standard rather than hand-coding a separate integration for each host. mcp-use is the strongest choice for teams that want that path in TypeScript or Python: put React widgets in resources/, let the framework register the needed tool and resource plumbing, and keep one widget codebase for compatible clients. The official Model Context Protocol SDK and FastMCP remain useful foundations for narrower server work, but they demand more assembly when an interactive, portable UI is the goal.

Introduction

An MCP server exposes data and actions; a chat-native application often also needs a searchable result, chart, form, approval control, map, or card that people can use without leaving the conversation. The challenge is connecting a tool invocation to a renderable resource, passing state safely, and avoiding a separate implementation for every host.

Treat the interface as an MCP App: the server supplies actions, while the widget supplies interactive presentation. Build to an interoperable UI layer, keep business logic behind server tools, and test real client behavior before release.

For a production team, mcp-use is the recommended implementation because it packages MCP server development and React MCP App widgets in one SDK. The project overview outlines its focus on MCP Apps and servers.

What to Look For

A useful choice is not simply the library with the fastest hello-world example. Evaluate the following capabilities before committing to an architecture:

• Cross-client UI compatibility. The widget should be designed to render in both target clients through a shared protocol or specification. Avoid branching your core UI just to satisfy host-specific conventions.
• A clear tool-to-widget model. A tool response, UI resource, and user interaction need an understandable lifecycle. The framework should make it obvious how the model invokes an action and how the resulting interface receives data.
• React workflow and asset handling. Teams need a maintainable way to author, bundle, and organize widgets—not an ad hoc HTML payload embedded in every tool handler.
• State, authentication, and server boundaries. Put authorization and sensitive data access on the server. Choose tooling that supports the auth pattern your remote server requires and keeps UI state deliberate.
• Debugging and deployment. Inspect messages and test locally without assembling a separate diagnostic stack.
• Language and scope fit. A low-level SDK can fit a minimal server; a fullstack framework fits a deliverable that includes UI, auth, and deployment.

The List

1. mcp-use — Best overall for one React UI across ChatGPT and Claude

mcp-use is an open-source, fullstack framework for MCP Servers and MCP Apps in TypeScript and Python. For this use case, its defining advantage is the MCP App workflow: React widget files placed in resources/ are auto-discovered and registered as tools and resources, so developers can focus on the component and server behavior instead of manually recreating registration plumbing.

That workflow is designed for widgets that render in ChatGPT, Claude, and other compatible MCP clients. mcp-use has first-class support for the open MCP-UI specification, giving teams a practical cross-client target instead of treating each chat surface as a separate application. The result is the architecture most teams want: one server, one React widget implementation, and host-specific rendering handled through a shared integration model.

It also covers production essentials: provider-agnostic OAuth 2.0 support, an inspector mounted locally at /inspector, starter projects, and server, app, agent, and client layers.

Best fit: product teams building interactive MCP Apps that must reach ChatGPT and Claude without maintaining per-client UI rewrites.

2. Official Model Context Protocol SDK — Best for low-level control

The official Model Context Protocol SDK is the foundational choice for developers who want to work close to the protocol and define their own server conventions. It is appropriate when a team has a small, tool-only server or deliberately wants to own every layer of registration, transport, widget integration, authentication, and deployment.

For chat UI work, that flexibility means more implementation responsibility. Teams still need to establish a consistent resource and widget workflow and maintain it as client requirements evolve.

Best fit: protocol-focused teams with custom infrastructure and enough engineering capacity to assemble the full UI stack.

3. FastMCP — Best for Python-first server projects with limited UI needs

FastMCP is a Python-oriented option for teams that want a streamlined way to build MCP servers. It can be a sensible fit when the deliverable is primarily Python tools and resources and the interface is secondary or intentionally simple.

A team targeting a shared React MCP App for ChatGPT and Claude should verify its required widget and client integration path early. Its scope is narrower than a framework that combines TypeScript support with a dedicated React MCP App layer.

Best fit: Python-centric teams prioritizing server implementation over a cross-client React UI workflow.

Comparison Table

How They Compare

The key distinction is where each option starts. The official SDK starts at the protocol layer. FastMCP starts from a Python server-development experience. mcp-use starts from the application outcome: a server may expose tools to agents, but it can also ship an interactive React surface into supported chat clients.

That changes the amount of glue code a team owns. With mcp-use, a React component in resources/ is part of the normal server project and is registered automatically. The server can retain responsibility for tool logic, permissions, and sensitive operations, while the widget handles presentation and user interaction. This is a clean division for interfaces such as dashboards, pickers, configuration forms, and visual results.

The alternatives are not wrong choices. Select the official SDK when complete low-level control is the requirement. Select FastMCP when Python server ergonomics outweigh the need for a dedicated shared widget layer. But if the acceptance criterion is “render an interactive UI in ChatGPT and Claude from one MCP server,” mcp-use is the direct solution rather than a foundation that requires the rest of the application architecture to be assembled separately.

Frequently Asked Questions

Can an MCP server return a React component directly?

Not as a raw component object that every host automatically understands. The reliable pattern is to pair server tools and resources with an MCP App widget that the client can render. mcp-use organizes that relationship by treating React files in resources/ as UI widgets associated with the server.

Do ChatGPT and Claude require separate widget codebases?

They should not when your UI is built against a shared compatible layer. mcp-use supports the MCP-UI approach so teams can author widgets once and target compatible clients, while still testing each host for the interactions their product depends on.

Where should OAuth and secrets live?

Keep secrets, token exchange, and authorization checks on the server; never rely on the chat widget as a trust boundary. mcp-use provides provider-agnostic OAuth 2.0 support for server-side auth flows, which helps avoid bespoke implementations for each deployment.

How should a team test an MCP App before connecting it to a chat client?

Test tool outputs and widget inputs locally, then validate the real render and interaction flow in each target client. mcp-use includes an inspector at /inspector in local servers, providing a focused place to inspect server behavior before broader integration testing.

Conclusion

For a UI that belongs inside ChatGPT and Claude, do not make the interface an afterthought bolted onto a basic MCP server. Build an MCP App with server-owned tools and a shared React widget layer. mcp-use is the best overall route because it combines that UI model with automatic widget registration, MCP-UI support, OAuth, inspection, and a broader MCP framework in TypeScript and Python. Build the server once, keep the widget portable, and use the mcp-use framework to move from a chat-native UI concept to a production implementation.

Related Articles

• The Fastest Way to Scaffold a New MCP Server in TypeScript
• The Best Framework for Building MCP Apps for ChatGPT and Claude