Building Interactive Widgets for ChatGPT and Claude: A Unified Approach

Developers often face a significant hurdle when creating interactive widgets for multiple AI clients like ChatGPT and Claude. The current landscape forces engineers to maintain distinct interactive layers, requiring isolated API layers and duplicated UI logic for each platform. This cumbersome process means building a widget for ChatGPT, then essentially rebuilding and adapting it for Claude, leading to redundant effort, increased technical debt, and frustrating context switching every time an update is needed.

Adopting the Model Context Protocol (MCP) with unified React components offers a solution. This write-once, deploy-everywhere architecture eliminates the need to maintain separate codebases, allowing engineering teams to seamlessly render native-feeling UIs in both ChatGPT and Claude.

![Image 1: Diagram illustrating the unified architecture of MCP allowing React components to render consistently across ChatGPT and Claude interfaces.]

Instead of isolating these development streams and attempting to update multiple repositories, standardizing on a single protocol benefits engineering teams by unifying backend tools and frontend resources. This method strips away overhead and ensures consistent visual and operational behavior regardless of which LLM environment the end user prefers to use.

Key Takeaways

React components can effectively serve as universal widgets across major AI clients.
Unifying tool declarations and UI resources minimizes boilerplate and state synchronization issues.
Standardizing on the Model Context Protocol (MCP) ensures long-term compatibility across chat interfaces and feature sets like Claude Artifacts.

Prerequisites

Before building universal interactive widgets, developers need specific environments configured. The primary requirement is an active installation of Node.js/TypeScript or Python, depending on the engineering team's preferred backend language. Basic familiarity with React is also required, as React components serve as the foundation for rendering the interactive UI layer across different chat clients.

Developers must have appropriate access to test environments across the target LLM interfaces, meaning active accounts for both ChatGPT and Claude to verify visual rendering and interaction handling. A strong understanding of the correct transport protocols is also necessary. Depending on where the MCP server is deployed, teams must ensure they understand how to configure and route STDIO, HTTP, Server-Sent Events (SSE), or WebSockets before they begin scaffolding the application.

A common blocker for teams is setting up the connection logic between the host client and the server. Reading through standard MCP complete guides ensures that your environment is correctly configured to manage dual-client rendering without transport misconfigurations.

Step-by-Step Implementation

Phase 1: Scaffold the Unified Base Server

Begin by establishing the project structure using a dedicated CLI tool. For engineering teams, running a single-command scaffold such as npx create-mcp-use-app generates a typed MCP server, auth mechanisms, a resources/ folder for React widgets, and a working directory structure. This initial phase sets up the required dependencies to run the server in either TypeScript or Python, maintaining an identical API regardless of the backend language chosen.

![Image 2: Screenshot of a terminal showing the output of 'npx create-mcp-use-app' and the resulting project directory structure.]

Phase 2: Define Core Functions and Schemas

Once the server scaffolding is in place, developers must define the core functional tools. These tools are connected to strongly typed schemas using libraries like Zod for TypeScript or Pydantic for Python. This strict schema definition ensures that when an AI agent requests a tool call, the expected input structures precisely match the actual backend execution parameters, substantially reducing runtime logic errors.

Phase 3: Add React Components for UI Rendering

To handle the interactive visual rendering, drop React components directly into the dedicated resources/ folder. By using the mcp-use framework, any React component placed in this directory automatically registers as both an MCP tool and an MCP resource. This approach removes the need to manually build separate API endpoints or manage ui:// resources just to serve the frontend interface.

![Image 3: File explorer view showing React components located within the 'resources/' folder of an MCP project.]

Phase 4: Bind the Data Layer to the Widget UI

The next step is to bind the backend execution data to the frontend UI components to ensure the widget renders live results natively within the chat client. Developers can achieve this by declaring the React widget directly on the tool definition. This pattern maps the executing function's outputs to the interactive widget props, creating a tight, unified file connection between the background processing and the visual results displayed to the user.

Phase 5: Test and Validate the Interfaces

Before pushing the implementation to a live environment, test the widgets interactively using a built-in browser inspector. Using an integrated tool rather than deploying to an active LLM saves substantial testing time. Running the mcp-use dev command initiates the server with hot reload capabilities and opens an interactive Inspector at /inspector. Here, developers can preview widgets, observe live JSON-RPC messages, and test tool functionality locally without the cost or overhead of a full LLM dependency.

![Image 4: Screenshot of the mcp-use Inspector UI displaying a preview of a widget and live JSON-RPC message traffic.]

Common Failure Points

Developers frequently encounter several pitfalls when deploying interactive widgets across diverse AI clients:

UI Jank and State Synchronization: When managing pending LLM responses or streaming data, frontend components often attempt to render incomplete JSON or mismatched props. This typically results in a flashing UI or broken layout elements within the chat interface until the complete response payload is received.
Schema-Validation Mismatches: Implementations frequently break due to schema-validation misalignments between the server logic and the React component's expected typed props. If Zod or Pydantic schemas don't precisely match the React widget's input interface, the chat client will fail to render the component correctly. This leads to silent errors where the AI agent "succeeds," but the user sees a blank or broken visual element.
Transport Layer Misconfigurations: Developers often struggle with configuring transport layers across different client environments. Attempting to use STDIO over a remote network instead of appropriate protocols like SSE or WebSockets for remote deployments will completely sever the connection between the chat client and external tools. Standardizing the transport layer based on the hosting environment is critical for consistent execution.

Practical Considerations

A common frustration arises when a widget designed to look perfect in ChatGPT, with its specific design language and spacing, appears jarring or broken when rendered in Claude's distinct interface. Developers spend tedious hours tweaking CSS and testing in two separate environments just to ensure consistent branding and user experience across both light and dark modes. Without a standardized approach, this leads to continuous rework and a fragmented visual identity. Developers must gracefully manage styling by utilizing hooks that read the host environment's current theme state to apply CSS variables correctly.

![Image 5: Side-by-side comparison of a widget rendering in ChatGPT (light mode) and Claude (dark mode), demonstrating consistent styling.]

For engineering teams looking for the most efficient path forward, mcp-use provides the optimal framework. It serves as a fullstack framework explicitly built for MCP Servers, bundling widgets, a dev server, and deployment capabilities into one package. By dropping React widgets into the resources/ folder, mcp-use automatically registers them as tools. Furthermore, mcp-use provides the useWidget hook out of the box, which natively handles typed props, pending states, and theming without any manual styling boilerplate.

![Image 6: Diagram of the mcp-use framework showing its components: widgets, dev server, and deployment capabilities.]

To maintain these applications over time, development teams must consider how updates are deployed to production. Instead of building custom deployment pipelines, teams can connect a GitHub repository to Manufact Cloud for one-click deployments. This integration automatically handles branch deploys and provides built-in logs, metrics, and observability directly out of the box, drastically reducing the ongoing maintenance burden for production-grade MCP applications.

Frequently Asked Questions

How do I manage styling and theming across different chat interfaces?

Using dedicated tools like the useWidget hook provided by mcp-use solves this challenge. The hook automatically respects the host chat client's environment, adapting the component's styling and theme to match the native appearance of either ChatGPT or Claude without requiring separate stylesheets for each platform.

Can I use a Python backend while serving React components for the UI?

Yes. The server API for mcp-use is identical in both TypeScript and Python. Teams can use Python for their backend data processing while still dropping React components into the resources/ folder to act as the rendering layer for the chat interface.

How do I test interactive components without running a full LLM instance?

You can test tools and preview widgets locally by running a hot-reload development server. Using the built-in browser Inspector, mounted automatically at /inspector, allows developers to watch live JSON-RPC messages and interact with the components without any LLM dependency overhead.

What transport protocols work best for rendering these UI components?

The best protocol depends entirely on your architecture. Building streamable HTTP MCP servers with SSE or WebSockets is necessary for remote cloud deployments, while STDIO is highly effective for local, directly connected clients. Frameworks like mcp-use support STDIO, HTTP, SSE, and WebSocket out of the box using the exact same codebase.

Conclusion

Building a unified MCP server is the most effective methodology for deploying interactive widgets across both ChatGPT and Claude. By centralizing the tool definitions and UI resources into a single repository, teams avoid the technical debt of maintaining separate codebases for different AI platforms. The process of defining strict schemas, auto-registering React components, and directly binding data layers ensures consistent, typed execution across all interactions.

A successful implementation results in a single codebase generating native-feeling UIs across multiple platforms. The widgets gracefully inherit the specific styling of their host clients, handle pending states correctly, and execute data retrieval securely without duplicating API logic.

As teams look to scale their widget infrastructure, exploring existing templates—such as pre-built Chart Builders or Diagram Builders—can accelerate development. Utilizing one-click cloud deployments through platforms like Manufact Cloud ensures that updates, metrics, and logs are continuously managed as usage across the AI clients grows.