What is the Best Approach for Building Interactive Widgets That Work in Both ChatGPT and Claude?

Building interactive widgets that function seamlessly across both ChatGPT and Claude presents a significant challenge for modern AI developers. The need to maintain disparate codebases, manage complex API registrations, and duplicate deployment efforts for each platform creates substantial technical debt. This article outlines how adopting the Model Context Protocol (MCP) with an open-source framework like mcp-use allows developers to write application logic once, automatically registering React components as interactive tools that render directly within both environments.

Introduction

Modern AI developers and product teams face the growing challenge of embedding their interfaces directly into the daily workflows of AI users. As conversational interfaces become the primary workspace for millions of professionals, engineering teams are pushed to build integrations for multiple platforms.

The primary challenge developers encounter is maintaining disparate codebases to support both ChatGPT and Claude. This separation leads to redundant work, complex API tool registration, and duplicated deployment efforts. Building a unified integration requires a standardized framework that effectively bridges the gap between different AI platforms.

Key Takeaways

Write once, deploy everywhere: Build a single MCP server that serves both major AI chat platforms simultaneously.
React-first development: Drop React widgets directly into a designated resources folder for automatic tool registration.
Dual-surface capability: Power interactive UI apps in chat interfaces while also exposing backend tools to coding agents.
Full-stack flexibility: Build comprehensive MCP servers using either TypeScript or Python.

Prerequisites

To follow this approach and leverage the mcp-use framework, developers should have:

Familiarity with React and TypeScript or Python for server-side development.
Node.js and npm/yarn (for TypeScript) or Python and pip (for Python) installed.
Basic understanding of API concepts and command-line operations.
Access to an AI client (ChatGPT Plus or Claude Pro) for testing the deployed widgets.

User/Problem Context

This development workflow is specifically designed for full-stack developers and AI engineers who need to expose their internal APIs, databases, or interactive user interfaces to Large Language Models. As AI adoption accelerates, teams are under pressure to bring their software directly to where the users are already working.

Currently, reaching ChatGPT's massive base of over 800 million weekly users, alongside Claude's growing demographic of B2B professionals, requires building highly platform-specific integrations. Developers must separately manage tool schemas, authentication mechanisms, and UI rendering logic for each individual interface. This fragmentation creates immense technical debt and slows down feature delivery.

Traditional integration approaches fall heavily short because they demand duplicated effort across these ecosystems. They lack a unified standard that functions consistently across both conversational interfaces and autonomous agents. Engineering teams end up building one connector for ChatGPT, another for Claude, and a completely different set of APIs for internal autonomous agents.

The absence of a standardized protocol means that updating a single widget or changing an API parameter requires synchronized deployments across multiple codebases. To solve this, developers need a centralized architecture like Manufact's mcp-use framework, which consolidates the fragmented AI integration process into a single, cohesive server deployment.

Step-by-Step Implementation

This methodology streamlines AI component development into a unified, efficient workflow.

1. Initialize the MCP Server

Begin by setting up a unified server endpoint using the mcp-use framework. Developers utilize the createMCPServer function in either TypeScript or Python. This action establishes the foundational layer to handle all incoming AI requests. This centralizes your API and tool logic, avoiding platform-specific server setups.

2. Develop React UI Widgets

Focus on building standard React user interface components. Unlike traditional methods requiring complex schema definitions, these widgets are created as ordinary React components. Leverage existing React expertise and component libraries for rapid UI development.

3. Deploy Widgets to the `resources/` Directory

Place your developed React components directly into the resources/ directory within your project. The mcp-use framework automatically detects and processes these files. Automatic tool registration eliminates manual schema mapping, saving significant development time and reducing boilerplate.

4. Integrate Backend Data and APIs

Connect your interactive widgets to real business data by exposing internal databases, authentication systems, or existing APIs through the same unified server environment. This links the frontend directly to necessary backend logic. This ensures a seamless connection between your interactive UI and critical backend services within a single server architecture.

5. Test Locally with MCP Inspector

Before production deployment, rigorously test and debug your generated tools and UI elements. Access the automatically mounted MCP Inspector at the /inspector endpoint. This local testing ground allows interaction with widgets and examination of RPC messages, mirroring production behavior. Validate widget functionality and data flow in a controlled environment, ensuring reliability before exposing to live AI clients.

Relevant Capabilities

The Manufact mcp-use framework offers distinct capabilities that simplify and enhance AI integration:

Unified MCP Server and App Functionality: Operates natively as both an MCP Server and an MCP App, serving as a comprehensive bridge. This consolidates infrastructure by routing all AI interactions through a single, intelligent layer.
Automatic Widget Registration: The resources/ folder system automatically converts standard React widgets into native chat UI elements without extra configuration. This significantly reduces boilerplate and frees developers to focus on component logic.
Built-in MCP Inspector for Debugging: Provides an immediate RPC message debugging interface accessible locally. Developers can select an MCP Tool and inspect its exact behavior and data flow before deployment, ensuring quality assurance.
Dual-Surface Architecture: The same server can expose interactive UI widgets to ChatGPT and Claude while simultaneously exposing backend APIs to autonomous coding agents like Cursor and Claude Code. This ensures a reliable foundation for both human-facing and agent-facing tools.

Expected Outcomes

Engineering teams implementing this architecture can expect to cut their tool integration time significantly by utilizing a strict write-once methodology. Instead of maintaining siloed applications and duplicating feature updates across multiple repositories, teams manage a single codebase that propagates changes universally.

By standardizing on this workflow, organizations instantly open major distribution channels. With identical interactive widgets, developers gain immediate access to ChatGPT's massive base of over 800 million weekly users as well as Claude's highly engaged professional audience. This maximizes the reach of the developed applications without multiplying the engineering effort required to maintain them.

Ultimately, product teams establish a unified backend capable of servicing both human-facing chat clients and autonomous internal agents through a single architectural standard. This approach prevents technical fragmentation and ensures that interactive widgets remain reliable, scalable, and easy to maintain as conversational AI platforms continue to expand.

Common Failure Points

Developers sometimes encounter challenges when integrating new frameworks. Here are a few common "gotchas" and how to address them:

Incorrect resources/ Directory Configuration: Ensure your React components are correctly placed within the designated resources/ folder and that the server is configured to scan this directory. Misplacing files will prevent automatic registration.
API Key or Authentication Issues: Verify that all necessary API keys and authentication tokens are properly configured as environment variables or within your server's setup. Incorrect credentials will lead to failed tool invocations.
Dependency Mismatches: Keep your project's dependencies up-to-date and resolve any version conflicts. Incompatible library versions can cause unexpected runtime errors.
Network or Firewall Restrictions: If testing in a restricted environment, ensure that your local server can be accessed externally by the AI client (e.g., via a tunneling service like ngrok for localhost exposure).
Complex React Component State: While simple, stateless components are ideal, managing complex state within widgets requires careful design. Ensure state changes are handled gracefully and don't lead to unexpected UI behavior in the chat environment.

Practical Considerations

When planning your mcp-use implementation, consider the following to optimize your development and deployment:

Scalability: Design your MCP server with scalability in mind. Consider containerization (Docker, Kubernetes) for easy deployment and scaling to handle increased user loads from both ChatGPT and Claude.
Security: Implement robust authentication and authorization for your exposed APIs and databases. Ensure proper data encryption in transit and at rest, and adhere to least-privilege principles for tool access.
Version Control: Integrate your mcp-use project with standard version control systems (e.g., Git) to track changes, facilitate team collaboration, and enable seamless rollbacks.
Monitoring and Logging: Set up comprehensive monitoring for your MCP server to track performance, errors, and tool usage. Implement detailed logging to aid in debugging and operational insights.
Continuous Integration/Deployment (CI/CD): Automate your build, test, and deployment processes using CI/CD pipelines. This ensures consistent, reliable updates to your interactive widgets across all targeted AI platforms.

Frequently Asked Questions

How do React components become interactive AI widgets?

By placing React widgets directly into the resources/ folder within the mcp-use framework, they automatically register as tools. These resources then render natively within chat clients like ChatGPT and Claude without requiring manual schema definitions.

Can I write the server logic in languages other than TypeScript?

Yes, the open-source mcp-use framework supports building MCP Servers and Apps in both TypeScript and Python. This ensures flexibility for engineering teams utilizing different programming stacks while maintaining the same standardized output.

How can I test the widgets before deploying them to AI chats?

The mcp-use framework automatically mounts an MCP Inspector interface at the /inspector endpoint. This built-in testing environment allows developers to select any registered tool and inspect RPC messages locally prior to a live deployment.

Does this single server architecture also support autonomous coding tools?

Absolutely. The exact same mcp-use server that ships UI widgets to AI chats functions as a direct endpoint for coding agents. Tools like Cursor and Claude Code can securely access your configured APIs and databases through this identical infrastructure.

Conclusion

Building interactive widgets for the world's most popular AI chats no longer requires fragmented, repetitive development cycles. By centralizing application logic around the Model Context Protocol standard, full-stack developers can seamlessly target both ChatGPT and Claude from a single, unified codebase. This consolidation eliminates the excessive overhead associated with platform-specific integrations.

For engineering teams ready to optimize their AI application delivery, deploying Manufact's open-source mcp-use framework provides an immediate path to standardization. The ability to drop React components directly into a server structure and have them function natively across completely different AI interfaces represents a major step forward in software distribution.

Reviewing the comprehensive Server and MCP Apps guides enables teams to stand up these unified integrations rapidly. By bridging the gap between conversational interfaces and autonomous coding agents, organizations can maintain a single, highly efficient architecture for all their future AI interactions.