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# MCP Apps

The [MCP Apps extension](https://github.com/modelcontextprotocol/ext-apps) allows MCP tools to serve interactive HTML UIs via `ui://` resources. When a tool has an associated app resource, Mastra Studio renders it in a sandboxed iframe alongside the tool form or inline in agent chat.

## When to use MCP Apps

Use MCP Apps when a tool result is better presented as an interactive UI rather than plain text. For example:

- A calculator that renders input fields and buttons for computation
- A color picker that displays swatches and hex values
- A form builder that captures structured user input
- A data visualizer that renders charts

## Quickstart

Define app resources on your `MCPServer` by providing a `ui://` URI mapped to inline HTML or an HTML file path.

```typescript
import { MCPServer } from '@mastra/mcp'
import { createTool } from '@mastra/core/tools'
import { z } from 'zod'

const calculatorTool = createTool({
  id: 'calculatorWithUI',
  description: 'An interactive calculator',
  inputSchema: z.object({
    num1: z.number(),
    num2: z.number(),
    operation: z.enum(['add', 'subtract']),
  }),
  execute: async ({ num1, num2, operation }) => {
    const result = operation === 'add' ? num1 + num2 : num1 - num2
    return {
      content: [{ type: 'text', text: 'An interactive calculator is displayed.' }],
      structuredContent: { result },
    }
  },
})

const server = new MCPServer({
  id: 'my-app-server',
  name: 'My App Server',
  version: '1.0.0',
  tools: { calculatorTool },
  appResources: {
    'ui://calculator/main': {
      name: 'Interactive Calculator',
      html: `<html>
        <body>
          <h2>Calculator</h2>
          <button id="btn">Compute</button>
          <script type="module">
            import { App } from 'https://cdn.jsdelivr.net/npm/@modelcontextprotocol/ext-apps/+esm';
            const app = new App({ name: 'Calculator', version: '1.0.0' });
            app.ontoolinput = (params) => {
              console.log('Tool input:', params.arguments);
            };
            document.getElementById('btn').addEventListener('click', async () => {
              const result = await app.callServerTool({
                name: 'calculatorWithUI',
                arguments: { num1: 10, num2: 5, operation: 'add' }
              });
              document.body.innerHTML += '<p>Result: ' + JSON.stringify(result) + '</p>';
            });
            await app.connect();
          </script>
        </body>
      </html>`,
    },
  },
})
```

Link the tool to its app resource by adding `_meta.ui.resourceUri` to the tool definition:

```typescript
calculatorTool._meta = {
  ui: { resourceUri: 'ui://calculator/main' },
}
```

> **Note:** Visit [MCPServer reference](https://mastra.ai/reference/tools/mcp-server) for the full `appResources` configuration.

## Connecting MCP Apps to agents

Agents consume tools — they don't need to know about MCP servers. Pass tools to the agent's `tools` config, and register the MCP server at the Mastra level so Studio can resolve app resources.

```typescript
import { Agent } from '@mastra/core/agent'
import { calculatorTool } from '../mcp/tools'

export const myAgent = new Agent({
  id: 'my-agent',
  name: 'My Agent',
  instructions: 'You have access to interactive UI tools.',
  model: 'openai/gpt-5-mini',
  tools: { calculatorTool },
})
```

Register the MCP server at the Mastra level. Studio scans registered MCP servers to map tools to their app resources.

```typescript
import { Mastra } from '@mastra/core/mastra'
import { myAgent } from './agents'
import { myAppServer } from './mcp/server'

export const mastra = new Mastra({
  agents: { myAgent },
  mcpServers: { myAppServer },
})
```

For remote MCP servers, use `MCPClient.listTools()` to get tools and `toMCPServerProxies()` to register the server:

```typescript
import { MCPClient } from '@mastra/mcp'

const mcpClient = new MCPClient({
  servers: {
    remoteApp: { url: new URL('https://remote-mcp-server.example.com/mcp') },
  },
})

const myAgent = new Agent({
  id: 'my-agent',
  name: 'My Agent',
  model: 'openai/gpt-5-mini',
  tools: await mcpClient.listTools(),
})

export const mastra = new Mastra({
  agents: { myAgent },
  mcpServers: { ...mcpClient.toMCPServerProxies() },
})
```

When tools come from `MCPClient.listTools()`, each tool's `_meta.ui` is automatically stamped with a `serverId` so Studio can resolve its app resources without scanning all servers.

## How MCP Apps work

MCP Apps follow a specific communication pattern between the host (Mastra Studio) and the iframe:

1. The tool executes and returns a brief summary in `content` (visible to the model) and detailed data in `structuredContent` (visible to the UI only).
2. The host renders the app HTML in a sandboxed iframe.
3. The iframe communicates with the host via a JSON-RPC postMessage protocol.
4. The app can call server tools using `callServerTool()` and inject messages into the chat using `sendMessage()`.

```text
Agent calls tool → Tool returns brief content + structuredContent
  → Host renders iframe with app HTML
  → User interacts with UI
  → UI calls callServerTool() for computation
  → UI calls sendMessage() to inject result into chat
```

## Tool result format

Tools with app resources should return two fields:

- `content`: A brief text summary for the model. Keep this short so the agent doesn't parrot the full result.
- `structuredContent`: The data payload that hydrates the UI. The model doesn't see this field.

```typescript
execute: async ({ num1, num2, operation }) => {
  const result = operation === 'add' ? num1 + num2 : num1 - num2
  return {
    content: [{ type: 'text', text: 'An interactive calculator is displayed.' }],
    structuredContent: { result },
  }
}
```

## App API (guest-side)

MCP App HTML uses the standard [`App` class from `@modelcontextprotocol/ext-apps`](https://github.com/modelcontextprotocol/ext-apps) to communicate with the host. Import it via ESM CDN or bundle it.

```javascript
import { App } from 'https://cdn.jsdelivr.net/npm/@modelcontextprotocol/ext-apps/+esm'
const app = new App({ name: 'MyApp', version: '1.0.0' })
```

### `app.callServerTool(params)`

Calls an MCP server tool from within the iframe. This is useful for interactive computation without leaving the UI.

```javascript
const result = await app.callServerTool({
  name: 'calculatorWithUI',
  arguments: { num1: 42, num2: 8, operation: 'add' },
})
```

### `app.sendMessage(params)`

Injects a user message into the agent chat, triggering a new model turn. Use this for sharing results or requesting follow-up actions.

```javascript
await app.sendMessage({
  role: 'user',
  content: [{ type: 'text', text: 'The result of 42 + 8 is 50' }],
})
```

### `app.ontoolinput`

A callback that fires when the host delivers tool input data to the iframe, allowing pre-population of form fields. The `params.arguments` object contains the tool call arguments.

```javascript
app.ontoolinput = params => {
  document.getElementById('num1').value = params.arguments.num1
}
```

> **Preventing UI flicker:** If your app has default form values, the user may briefly see them before `ontoolinput` hydrates the correct values. To prevent this, start the body hidden and reveal it after hydration:
>
> ```html
> <style>
>   body {
>     opacity: 0;
>     transition: opacity 0.15s;
>   }
>   body.ready {
>     opacity: 1;
>   }
> </style>
> <script type="module">
>   import { App } from 'https://cdn.jsdelivr.net/npm/@modelcontextprotocol/ext-apps/+esm'
>   const app = new App({ name: 'MyApp', version: '1.0.0' })
>
>   app.ontoolinput = params => {
>     // Hydrate form fields from params.arguments
>     document.body.classList.add('ready')
>   }
>
>   await app.connect()
>   // Fallback: reveal after connection if no tool input arrives
>   setTimeout(() => document.body.classList.add('ready'), 150)
> </script>
> ```

### `app.connect()`

Establishes the connection to the host. Call this after registering all event handlers.

```javascript
await app.connect()
```

> **Note:** See the [`App` class API reference](https://apps.extensions.modelcontextprotocol.io/api/classes/app.App.html) for the full list of methods, callbacks, and lifecycle hooks.

## Using external MCP servers with apps

External (non-Mastra) MCP servers that implement the MCP Apps extension work with Mastra via `MCPClient`. Use `listTools()` for agent tools and `toMCPServerProxies()` to register them in Studio.

```typescript
import { Mastra } from '@mastra/core/mastra'
import { MCPClient } from '@mastra/mcp'
import { Agent } from '@mastra/core/agent'

const mcpClient = new MCPClient({
  servers: {
    'external-server': {
      command: 'node',
      args: ['path/to/external-server.js'],
    },
  },
})

const myAgent = new Agent({
  id: 'my-agent',
  name: 'My Agent',
  model: 'openai/gpt-5-mini',
  tools: await mcpClient.listTools(),
})

export const mastra = new Mastra({
  agents: { myAgent },
  mcpServers: {
    ...mcpClient.toMCPServerProxies(),
  },
})
```

> **Note:** Visit [MCPClient reference](https://mastra.ai/reference/tools/mcp-client) for more details on proxying external servers.

## Sandbox security

Mastra Studio uses [`@mcp-ui/client`](https://www.npmjs.com/package/@mcp-ui/client) to render MCP App iframes through a sandbox proxy. The proxy loads app HTML via `postMessage` rather than `srcDoc`, providing additional isolation.

App iframes are sandboxed with the following permissions:

- `allow-scripts`: Enables JavaScript execution
- `allow-forms`: Allows form submission
- `allow-popups`: Permits `window.open()` and link targets

The iframe doesn't have access to the parent page's DOM, cookies, or storage. All communication happens through the JSON-RPC postMessage protocol managed by `@mcp-ui/client`'s `AppRenderer` on the host side and `@modelcontextprotocol/ext-apps`'s `App` class on the guest side.

## Related

- [MCP overview](https://mastra.ai/docs/mcp/overview)
- [MCPServer reference](https://mastra.ai/reference/tools/mcp-server)
- [MCPClient reference](https://mastra.ai/reference/tools/mcp-client)
- [MCP Apps extension spec](https://github.com/modelcontextprotocol/ext-apps)