The 5 best platforms supporting MCP apps

For years, chatting with an AI agent has felt a lot like texting. You type a prompt, get a wall of text back, and then type another prompt to get a little closer to what you actually want. It's powerful, but it can also be slow and clunky, especially when you're trying to do something complex. But what if our AI agents could have a "visual voice"? What if they could show us an interactive chart instead of just describing it, or present a form instead of asking a dozen different questions?

This is what MCP Apps are all about. They're an official extension to the Model Context Protocol (MCP) that lets AI agents display rich, interactive user interfaces right inside a conversation. It’s a huge step forward in making AI feel less like a command line and more like a real collaborator. In this post, we’ll check out the platforms that are leading the charge in supporting MCP apps.

What are MCP apps?

Before we dive into the list, let's make sure we're on the same page about what MCP apps are. Put simply, MCP apps are interactive UI components, like dashboards, forms, or data visualizations, that an AI tool can send back instead of just plain text. They're built on the Model Context Protocol, which is an open standard for connecting AI models with outside tools and data.

Until recently, MCP tools could only really exchange text and structured data. If a tool sent back a bunch of sales data, it was up to the app (like a chatbot or code editor) to figure out how to display it. This was a massive headache for developers, who had to build custom UIs for every single type of data.

MCP apps solve this by letting the tool server provide its own pre-built HTML interface. The host app just has to render this interface in a secure, sandboxed "iframe". This gives developers the power to create rich, interactive experiences that work consistently across any client that supports the MCP Apps extension. It’s a more natural way to work, bridging the gap between conversational AI and the graphical interfaces we use every day.

How we chose the best platforms for MCP apps

To find the best platforms jumping on the MCP app train, we looked for ones that are doing more than just ticking a box. We focused on platforms that are actively shaping how these apps are used in the real world. Our criteria came down to a few key things:

Genuinely curious about people's real experience with MCPs beyond the demo videos.... My top 3 pains so far: 1. No idea which MCPs actually work vs abandoned projects 2. Debugging is a nightmare - errors are cryptic AF 3. Every MCP has different auth setup, spending more time on config than coding What's driving you crazy about MCPs? Maybe we can share solutions....

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• Developer Experience: How easy is it for a developer to build and test MCP apps on the platform? We looked for good documentation, active communities, and solid tooling.
• Real-World Usefulness: Does the platform use MCP apps to solve real problems that are a pain to handle with text alone? We prioritized use cases that clearly show why interactive UIs are a big deal.
• Ecosystem Leadership: Is the platform a key player in pushing the MCP standard forward? We wanted to highlight tools from companies and communities that are really driving the protocol.
• Accessibility: Is the platform actually available for developers to try out today? We stuck to publicly accessible tools, especially those with free or developer-friendly options.

Comparison of the top platforms for MCP apps

Here’s a quick side-by-side look at the platforms we'll be diving into.

A closer look at the 5 best platforms with support for MCP apps

Alright, let's get into the details of each platform and see what makes them stand out.

1. VS Code (Insiders)

As the first major AI code editor with full MCP Apps support, Microsoft’s Visual Studio Code is a huge deal for developers. The integration, which you can try in its daily Insiders build, transforms the AI coding agent from a simple text-based helper into a true collaborative partner. A recent VS Code blog post showed how an agent can now render an interactive flame graph directly in the chat panel instead of just trying to describe a performance bottleneck in words.

Why it’s on the list: VS Code showcases some of the most compelling reasons for developers to care about MCP apps. We're talking interactive list reordering, performance visualizations, and feature flag selectors right in your editor. By bringing interactive AI into the environment where developers already live, it makes building and using developer-focused MCP tools feel incredibly natural.

• Pros: The integration is baked right into the developer's existing workflow, so there's no need to switch between different windows or apps. Since VS Code is where many people will be building their MCP servers anyway, it creates a really tight "develop and test" loop. The examples they've shown solve real problems that are almost impossible to manage with text prompts alone.
• Cons: Full MCP app support is currently only in the Insiders version, which can be a bit buggy and might not be stable enough for everyone's day-to-day work. The experience is also closely tied to the GitHub Copilot ecosystem, so you'll need a subscription to get the most out of it.

Pricing:

• VS Code Insiders is free to download and use.
• GitHub Copilot plans, which power the agent, come in a few flavors: Free, Team ($4 per user, per month), and Enterprise ($21 per user, per month).

2. Claude

Anthropic’s Claude, one of the leading conversational AI assistants, has brought MCP app support to its web and desktop platforms. As a co-author of the MCP Apps extension, Anthropic has set the standard for how to implement it. For users, this means tools can now serve up rich interfaces like interactive dashboards, multi-step forms, and document viewers directly in a chat with Claude. It makes complex tasks, like digging into sales data or configuring a server, feel much more intuitive.

Why it’s on the list: Claude is the top general-purpose AI chat client with full support for MCP apps. Its implementation is a great example of how interactive UIs can improve all kinds of tasks, not just coding. If you're building an MCP server, it's advisable to test it against Claude to make sure it works for a large and growing audience.

• Pros: The user experience is smooth, with apps rendering seamlessly right in the conversation. Claude's support for custom connectors makes it easy for developers to test their own MCP servers running on their local machine. Plus, the implementation is solid and backed by one of the teams that actually created the spec.
• Cons: To use custom MCP connectors for testing your local servers, you need a paid Claude plan. It's not a huge barrier, but it's something for hobbyist developers to keep in mind. Since it's a closed-source platform, you're also depending on Anthropic's roadmap and release schedule.

Pricing:

• Free: Gives you limited access to their models.
• Claude Pro: It's $17 per month if you pay annually ($20 billed monthly) and gives you higher usage limits and access to those remote MCP connectors.
• Claude Max: Starts at $100 a month for the real power users.

3. Storybook

Storybook is a popular open-source tool for building UI components in isolation, and its integration with MCP apps is a natural fit. Using the official "@storybook/addon-mcp", a developer can ask their AI agent something like, "build a login form using our design system," and the agent will pop up an interactive preview of that component right in the chat. This connects the dots between describing a piece of UI and actually seeing it, which allows for super-fast iteration and visual feedback.

Why it’s on the list: Storybook's integration is a strong example of how MCP apps can streamline a very specific, but very important, part of the frontend development workflow. As a core Storybook contributor pointed out, this gets rid of the need to constantly switch between your chat and the Storybook UI to see changes. It turns UI development into a more conversational and visual process.

• Pros: You get immediate visual feedback, which is valuable for UI development. It taps into a tool that millions of frontend developers already know and love. And since it's an open-source project, you can dig into the code yourself and even contribute.
• Cons: The use case is very specific to frontend and UI developers who are already using Storybook, so it's not as broadly useful as a general-purpose client like Claude. The addon is also still pretty new and considered experimental.

Pricing:

• Storybook is completely free and open-source.

4. Goose

Goose is a configurable, open-source AI agent from Block, Inc. that you run on your own machine. It acts as a reference implementation for the MCP standard and was one of the very first clients to support agentic UIs. Goose gives developers and power users a lot of control over their AI agent, letting them connect different tools and tweak its behavior. With MCP app support, Goose can render interactive experiences like calendars and maps right in its desktop or command-line interface.

Why it’s on the list: Goose is a powerful, local-first, and highly customizable choice for developers who want total control. Because it's a reference implementation, its support for MCP apps is thorough and always up-to-date. It's a great platform for pushing the boundaries of what agentic UIs can do in a self-hosted setup, and it even has a handy tutorial for building MCP apps.

• Pros: Being open-source and local-first means you get a high degree of control and privacy. It's super configurable, making it a great sandbox for advanced experiments. As a reference implementation, it's also an authoritative source for understanding how the protocol is supposed to work.
• Cons: Setting up and configuring Goose requires more technical know-how than using a web client like Claude. The user interface is more functional than flashy, which might be a downside for anyone looking for a polished, out-of-the-box experience.

Pricing:

• Goose is free and open-source under the Apache-2.0 license.

5. Postman

Postman, a widely used tool in API development, has expanded its toolkit to help with testing and developing MCP servers. Its product offerings include an MCP server and a catalog of external servers you can connect to. Developers can use Postman to send requests to their MCP server, check out the UI resources it sends back, and debug the whole conversation between the host and the app.

Why it’s on the list: Postman provides the essential debugging and testing infrastructure that anyone building MCP servers needs. While it doesn't render the interactive UI like a chat client does, it lets you inspect the raw HTML and communication payloads, which is critical during development. Its adoption contributes to the establishment of MCP as a serious protocol for professional API and tool development.

• Pros: It brings MCP server development into a familiar environment for the many developers who already use it for API testing. It allows for a deep dive into requests and responses, which is very helpful for debugging. It also supports multiple transport types, making it a versatile tool for testing different server setups.
• Cons: Postman is a testing tool, not a host client. It won't actually render your interactive MCP app, so you can't test the user-facing experience directly inside it. You'll still need a host like VS Code or Claude for that end-to-end testing.

Pricing:

• Free: Core features for individuals and small teams.
• Basic: $14 per user, per month (billed annually) for collaboration features.
• Professional: $29 per user, per month (billed annually) with more advanced tools.
• Enterprise: $49 per user, per month (billed annually) for large organizations.

Tips for getting started with MCP apps

Inspired to build your first interactive AI experience? Here are a few tips to get you going.

• Start with the official SDK. The "@modelcontextprotocol/ext-apps" package has the essential "App" class and server-side helpers that handle most of the tedious stuff for you. The official examples repository is the best place to find some starter templates.
• Think "show, don't just tell". Effective MCP apps are the ones that present information visually or interactively in a way that’s just plain better than text. A map beats a list of addresses. A color picker beats guessing hex codes.
• Consider build vs. buy. Building a custom MCP app is effective for creating bespoke developer tools or unique agentic experiences. If your goal is to automate business workflows like customer support, a pre-built platform can be a practical alternative. For example, solutions like eesel AI offer an interactive experience by learning from your existing knowledge base without requiring custom code.

For a great high-level overview of what MCP apps are and why they matter, check out this video from the MCP Developers Summit. The co-creators of MCP-UI break down the core concepts and the vision behind making AI interactions more visual and intuitive.

The co-creators of MCP-UI break down the core concepts and the vision behind making AI interactions more visual and intuitive.

The future is interactive with MCP apps

MCP apps represent a big shift in how we interact with AI. By giving agents a visual voice, we're moving beyond simple question-and-answer sessions and into true, dynamic collaboration. The platforms we've looked at here are leading this movement, providing the tools and environments needed to build the next generation of agentic software.

Whether you're a developer looking to make your coding assistant smarter or a business leader aiming to streamline your team's workflows, the move toward interactive AI is happening now.

For businesses looking to implement this technology, platforms such as eesel AI for customer service provide a code-free option for deploying interactive AI. These systems can learn from your business data to begin automating workflows.