---
title: "Overview"
description: "Build interactive widgets using the official MCP Apps protocol with cross-client compatibility"
icon: "blocks"
---

MCP Apps is the **official standard** for interactive widgets in the Model Context Protocol ecosystem. It provides a standardized way to create rich, interactive user interfaces that work across MCP-compatible clients.

<Info>
  **mcp-use Advantage**: Write your widgets once using `type: "mcpApps"` and
  they'll work with **both** MCP Apps clients AND ChatGPT automatically. This
  unique dual-protocol support means maximum compatibility with minimal effort.
</Info>

<video
  alt="MCP Apps Widget Example"
  style={{
    maxWidth: "600px",
    borderRadius: "8px",
    marginBottom: "1.5rem",
    boxShadow: "0 2px 12px rgba(0,0,0,0.08)",
  }}
  muted
  autoPlay
  loop
>
  <source src="/images/widget.mp4" type="video/mp4" />
</video>

## Why MCP Apps?

MCP Apps is the recommended widget protocol for MCP servers because it:

- **MCP-native**: Built specifically for the Model Context Protocol ecosystem
- **Open Standard**: Ensuring long-term compatibility across the MCP ecosystem
- **Secure**: Double-iframe sandbox architecture with granular CSP control
- **Feature-rich**: JSON-RPC 2.0 communication with full MCP integration
- **Future-proof**: Supported by growing ecosystem of MCP clients

## Quick Start

Start with the MCP Apps template:

```bash
npx create-mcp-use-app my-mcp-server --template mcp-apps
cd my-mcp-server
npm install
npm run dev
```

This creates a project with dual-protocol support out of the box.

## MCP Apps vs ChatGPT Apps SDK

While both protocols enable interactive widgets, they have key differences:

| Feature             | MCP Apps (Standard)                    | ChatGPT Apps SDK               |
| ------------------- | -------------------------------------- | ------------------------------ |
| **Protocol**        | JSON-RPC 2.0 over `postMessage`        | `window.openai` global API     |
| **MIME Type**       | `text/html;profile=mcp-app`            | `text/html+skybridge`          |
| **Specification**   | MCP Apps (open standard)               | OpenAI proprietary             |
| **Architecture**    | Double-iframe sandbox                  | Single iframe                  |
| **CSP Format**      | camelCase (`connectDomains`)           | snake_case (`connect_domains`) |
| **Client Support**  | MCP Apps clients (Claude, Goose, etc.) | ChatGPT                        |
| **mcp-use Support** | ✅ Full support                        | ✅ Full support                |

<Note>
  **Best of Both Worlds**: With mcp-use, you don't have to choose! Using `type:
  "mcpApps"` generates metadata for **both** protocols automatically, so your
  widgets work everywhere.
</Note>

## Creating an MCP Apps Widget

Widgets can receive partial tool arguments in real time (`partialToolInput` / `isStreaming`) when the host streams them, enabling live previews as the LLM generates the tool call. See [useWidget  -  Streaming tool arguments](/typescript/server/widget-components/usewidget#streaming-tool-arguments) for details.

### Using Dual-Protocol Type (Recommended)

The `mcpApps` type enables your widgets to work with both MCP Apps clients and ChatGPT:

```typescript
import { MCPServer } from "mcp-use/server";

const server = new MCPServer({
  name: "my-server",
  version: "1.0.0",
  baseUrl: process.env.MCP_URL || "http://localhost:3000",
});

// Register a dual-protocol widget
server.uiResource({
  type: "mcpApps", // 👈 Works with BOTH MCP Apps AND ChatGPT
  name: "weather-display",
  htmlTemplate: `
    <!DOCTYPE html>
    <html>
      <head>
        <meta charset="UTF-8">
        <title>Weather Display</title>
      </head>
      <body>
        <div id="root"></div>
        <script type="module" src="/resources/weather-display.js"></script>
      </body>
    </html>
  `,
  metadata: {
    // Unified CSP configuration - works for both protocols
    csp: {
      connectDomains: ["https://api.weather.com"],
      resourceDomains: ["https://cdn.weather.com"],
    },
    prefersBorder: true,

    // Invocation status text  -  shown in inspector and ChatGPT while tool runs / after completion
    invoking: "Fetching weather...",   // auto-default: "Loading weather-display..."
    invoked: "Weather loaded",         // auto-default: "weather-display ready"

    // ChatGPT-specific metadata (optional)
    widgetDescription: "Displays current weather conditions",
  },
});
```

This single configuration automatically generates metadata for **both protocols**:

**For MCP Apps Clients:**

```typescript
{
  mimeType: "text/html;profile=mcp-app",
  _meta: {
    ui: {
      resourceUri: "ui://widget/weather-display.html",
      csp: {
        connectDomains: ["https://api.weather.com"],
        resourceDomains: ["https://cdn.weather.com"]
      },
      prefersBorder: true
    }
  }
}
```

**For ChatGPT (Apps SDK):**

```typescript
{
  mimeType: "text/html+skybridge",
  _meta: {
    "openai/outputTemplate": "ui://widget/weather-display.html",
    "openai/widgetCSP": {
      connect_domains: ["https://api.weather.com"],
      resource_domains: ["https://cdn.weather.com"]
    },
    "openai/widgetPrefersBorder": true,
    "openai/widgetDescription": "Displays current weather conditions"
  }
}
```

## Data Flow: What the LLM Sees vs What the Widget Sees

Per the [MCP Apps specification](https://github.com/modelcontextprotocol/ext-apps), a tool result has three fields with different visibility:

| Field | LLM sees it? | Widget sees it? | Purpose |
|---|---|---|---|
| `content` | **Yes** | Yes | Text summary for the model's context |
| `structuredContent` | **No** | Yes (as `props`) | Rendering data for the widget |
| `_meta` | **No** | Yes (as `metadata`) | Protocol + custom metadata |

This separation enables a powerful pattern: the **server computes data for the widget** (like search results, computed visualizations, etc.) without polluting the model's context window.

### Server: Returning data

```typescript
server.tool({
  name: "search-products",
  schema: z.object({ query: z.string() }),
  annotations: {
    readOnlyHint: true,
    destructiveHint: false,
    openWorldHint: true, // e.g. database or HTTP search  -  adjust per implementation
  },
  outputSchema: z.object({
    query: z.string(),
    results: z.array(
      z.object({ id: z.string(), name: z.string(), price: z.number() })
    ),
  }),
  widget: { name: "product-search-result" },
}, async ({ query }) => {
  const results = await db.search(query);

  return widget({
    // Widget rendering data  -  becomes structuredContent (hosts often validate against outputSchema)
    props: { query, results },
    // Text summary  -  LLM sees this
    output: text(`Found ${results.length} products matching "${query}"`),
  });
});
```

### Widget: Reading data

```tsx
const { props, toolInput, isPending } = useWidget<ProductSearchProps>();

// props     = structuredContent from tool result (computed by server)
// toolInput = tool call arguments (what the model sent, e.g. { query: "mango" })
```

The `props` and `toolInput` are intentionally separate:
- **`toolInput`** is what the model decided to pass (e.g. `{ query: "mango" }`)
- **`props`** is what the server computed and returned (e.g. `{ query: "mango", results: [...16 items] }`)

## Display Modes

Widgets can request different display modes from the host:

```tsx
const { displayMode, requestDisplayMode } = useWidget();

// Request fullscreen
await requestDisplayMode("fullscreen");

// Request picture-in-picture
await requestDisplayMode("pip");

// Return to inline
await requestDisplayMode("inline");
```

The host may decline the request  -  always check `displayMode` for the actual state. Common pattern for a toolbar:

```tsx
const isFullscreen = displayMode === "fullscreen";
const isPip = displayMode === "pip";

return (
  <div>
    {!isFullscreen && !isPip && (
      <>
        <button onClick={() => requestDisplayMode("pip")}>PiP</button>
        <button onClick={() => requestDisplayMode("fullscreen")}>Fullscreen</button>
      </>
    )}
    {(isFullscreen || isPip) && (
      <button onClick={() => requestDisplayMode("inline")}>Exit</button>
    )}
  </div>
);
```

## Follow-Up Messages

Widgets can send messages to the conversation on behalf of the user using `sendFollowUpMessage`. This triggers a new LLM turn:

```tsx
const { sendFollowUpMessage } = useWidget();

// String shorthand (most common)
<button onClick={() => sendFollowUpMessage("Tell me more about mangoes")}>
  Ask the AI
</button>

// Full content array  -  supports text, image, and resource blocks per SEP-1865
<button onClick={() => sendFollowUpMessage([
  { type: "text", text: "Tell me more about mangoes" },
])}>
  Ask the AI
</button>
```

Per the MCP Apps specification, this maps to the `ui/message` JSON-RPC request with `role: "user"` and a `content` array. The host adds the message to the conversation and triggers the model to respond.

When running inside ChatGPT (Apps SDK), only the text content of the blocks is used since `window.openai.sendFollowUpMessage` accepts a plain string prompt.

## Host Context: Locale, Theme, and More

`useWidget` exposes host context fields provided by the host:

```tsx
const { theme, locale, timeZone, safeArea, userAgent, maxHeight } = useWidget();
```

| Field | Type | Description |
|---|---|---|
| `theme` | `"light" \| "dark"` | Current host theme |
| `locale` | `string` | BCP 47 locale (e.g. `"en-US"`, `"fr-FR"`) |
| `timeZone` | `string` | IANA timezone (e.g. `"America/New_York"`) |
| `safeArea` | `{ insets: { top, right, bottom, left } }` | Safe area for notched devices |
| `userAgent` | `{ device: { type }, capabilities: { hover, touch } }` | Device info |
| `maxHeight` | `number` | Max available height in pixels |
| `maxWidth` | `number \| undefined` | Max available width (MCP Apps only) |

Use the Inspector's playground controls to test different locales, devices, and themes during development.

## Response Metadata

The tool result's `_meta` field is available as `metadata` in `useWidget`. Use the `metadata` option in the `widget()` helper to pass extra data that doesn't belong in `structuredContent`:

```typescript
// Server side
return widget({
  props: { items: filteredItems },
  metadata: { totalCount: 1000, nextCursor: "abc123" },
  output: text(`Showing ${filteredItems.length} of 1000 results`),
});
```

```tsx
// Widget side
const { props, metadata } = useWidget();

// props    = { items: [...] }           (from structuredContent)
// metadata = { totalCount, nextCursor } (from _meta)
```

This works across both MCP Apps (`ui/notifications/tool-result` → `params._meta`) and ChatGPT (`window.openai.toolResponseMetadata`).

## Widget Code (Protocol-Agnostic)

Your widget code is **identical** regardless of which protocol the host uses:

```tsx
// resources/weather-display.tsx
import React from "react";
import { useWidget, McpUseProvider, type WidgetMetadata } from "mcp-use/react";
import { z } from "zod";

const propSchema = z.object({
  city: z.string(),
  temperature: z.number(),
  conditions: z.string(),
});

export const widgetMetadata: WidgetMetadata = {
  description: "Display weather information",
  props: propSchema,
};

const WeatherDisplay: React.FC = () => {
  const { props, isPending } = useWidget<z.infer<typeof propSchema>>();

  if (isPending) {
    return <div>Loading weather...</div>;
  }

  return (
    <McpUseProvider autoSize>
      <div className="weather-card">
        <h2>{props.city}</h2>
        <div className="temperature">{props.temperature}°C</div>
        <div className="conditions">{props.conditions}</div>
      </div>
    </McpUseProvider>
  );
};

export default WeatherDisplay;
```

**The same widget code works with:**

- ✅ Claude Desktop (MCP Apps protocol)
- ✅ ChatGPT (Apps SDK protocol)
- ✅ Goose (MCP Apps protocol)
- ✅ Any MCP Apps-compatible client
- ✅ Future clients supporting either protocol

## Interactive Widgets with Tool Calling

Widgets can call MCP tools using the `useCallTool` hook for interactive functionality:

```tsx
import { useWidget, useCallTool } from "mcp-use/react";

const InteractiveWidget = () => {
  const { props, isPending: propsPending } = useWidget();
  const { callTool, data, isPending } = useCallTool("fetch-data");

  if (propsPending) {
    return <div>Loading...</div>;
  }

  return (
    <div>
      <button onClick={() => callTool({ id: props.itemId })}>
        {isPending ? "Loading..." : "Fetch Data"}
      </button>
      {data && (
        <div>{JSON.stringify(data.structuredContent)}</div>
      )}
    </div>
  );
};
```

**Benefits:**
- ✅ **Type-safe**: Automatic TypeScript inference for tool inputs and outputs
- ✅ **State management**: Built-in loading, success, and error states
- ✅ **Cross-protocol**: Works identically in MCP Apps and ChatGPT

See [useCallTool()](/typescript/server/widget-components/usecalltool) for complete documentation.

## State Management

State in an MCP Apps widget falls into three categories:

| State type | Where it lives | Lifetime | Example |
|---|---|---|---|
| **Business data** | MCP server / backend | Long-lived | Tasks, search results, documents |
| **UI state** | Widget instance | Per-widget | Selected row, expanded panel, filter |
| **Cross-session** | Your backend | Across conversations | Saved preferences, workspace settings |

### Business Data (Server-Owned)

Business data is the source of truth. The widget receives it via `props` (from `structuredContent`) and can request updates by calling server tools:

```tsx
const { props } = useWidget<{ tasks: Task[] }>();
const { callTool, data } = useCallTool("update-task");

const handleToggle = (taskId: string) => {
  callTool({ taskId, done: true });
  // Server returns updated task list in structuredContent
  // Widget re-renders with new props on next tool call
};
```

### UI State (Widget-Owned)

UI state describes **how** data is being viewed, not the data itself. Use `setState` from `useWidget`:

```tsx
type MyState = { favorites: string[] };

const { props, state, setState } = useWidget<Props, Output, Meta, MyState>();

const favorites = state?.favorites ?? [];

const toggleFavorite = (id: string) => {
  const current = state?.favorites ?? [];
  const next = current.includes(id)
    ? current.filter(f => f !== id)
    : [...current, id];
  setState({ favorites: next });
};
```

**What happens under the hood:**

- **ChatGPT**: Calls `window.openai.setWidgetState()` for host-managed persistence across message reloads.
- **MCP Apps**: Updates local React state **and** sends `ui/update-model-context` to the host, so the model can reason about UI state on future turns.

<Note>
  `ui/update-model-context` is the MCP Apps equivalent of ChatGPT's
  widget state persistence. Each call overwrites the previous context. The host
  includes this data in the model's context on the next user message.
</Note>

### Cross-Session State (Backend-Owned)

For state that must persist across conversations, store it on your backend. Use `callTool` from the widget to read/write preferences via your server's tools  -  don't rely on `localStorage`.

## MCP Apps Bridge API

For widgets that need direct protocol access, mcp-use provides the MCP Apps bridge:

```typescript
import { getMcpAppsBridge } from 'mcp-use/react';

function MyWidget() {
  const bridge = getMcpAppsBridge();

  // The bridge automatically connects
  // All methods return promises

  // Call an MCP tool
  const result = await bridge.callTool('search', { query: 'hello' });

  // Read an MCP resource
  const data = await bridge.readResource('file:///data.json');

  // Send a message to the host
  await bridge.sendMessage({ type: 'info', text: 'Processing...' });

  // Open a link
  await bridge.openLink('https://example.com');

  // Request display mode change
  await bridge.requestDisplayMode('fullscreen');

  return <div>My Widget</div>;
}
```

<Info>
  Most widgets won't need the bridge directly. The `useWidget()` hook provides a
  simplified API that works across both protocols automatically.
</Info>

## Protocol-Specific Metadata

While mcp-use handles protocol differences automatically, you can provide protocol-specific metadata when needed:

```typescript
server.uiResource({
  type: "mcpApps",
  name: "my-widget",
  htmlTemplate: `...`,
  metadata: {
    // Shared metadata (used by both)
    csp: { connectDomains: ["https://api.example.com"] },
    prefersBorder: true,

    // MCP Apps specific (ignored by ChatGPT)
    autoResize: true,
    supportsLocalStorage: true,

    // ChatGPT specific (ignored by MCP Apps clients)
    widgetDescription: "Special description for ChatGPT",
    widgetDomain: "https://chatgpt.com",
  },
});
```

## Migration from Apps SDK

If you have existing widgets using `type: "appsSdk"`, you can migrate to dual-protocol support. Note that this requires updating both the type and the metadata format.

### Before (ChatGPT only):

```typescript
server.uiResource({
  type: "appsSdk", // Only works with ChatGPT
  name: "my-widget",
  htmlTemplate: `...`,
  appsSdkMetadata: {
    "openai/widgetCSP": {
      connect_domains: ["https://api.example.com"],
      resource_domains: ["https://cdn.example.com"],
    },
    "openai/widgetPrefersBorder": true,
    "openai/widgetDescription": "My widget description",
  },
});
```

### After (Universal compatibility):

```typescript
server.uiResource({
  type: "mcpApps", // Works with ChatGPT AND MCP Apps clients
  name: "my-widget",
  htmlTemplate: `...`,
  metadata: {
    csp: {
      connectDomains: ["https://api.example.com"],
      resourceDomains: ["https://cdn.example.com"],
    },
    prefersBorder: true,
    widgetDescription: "My widget description",
  },
});
```

**Key changes:**

- `type: "appsSdk"` → `type: "mcpApps"`
- `appsSdkMetadata` → `metadata` (field name change)
- `"openai/widgetCSP"` → `csp` (remove openai/ prefix)
- `connect_domains` → `connectDomains` (snake_case → camelCase)
- `resource_domains` → `resourceDomains` (snake_case → camelCase)
- `"openai/widgetPrefersBorder"` → `prefersBorder` (remove openai/ prefix)

Your **widget code** requires **no changes** - only the server registration changes.

### Migration Options

You have three options when migrating:

**Option 1: Full Migration (Recommended)**

Migrate completely to the new `metadata` format for maximum clarity:

```typescript
server.uiResource({
  type: "mcpApps",
  name: "my-widget",
  htmlTemplate: `...`,
  metadata: {
    csp: { connectDomains: ["https://api.example.com"] },
    prefersBorder: true,
  },
});
```

**Option 2: Backward Compatible Migration**

Keep both formats if you need ChatGPT-specific overrides:

```typescript
server.uiResource({
  type: "mcpApps",
  name: "my-widget",
  htmlTemplate: `...`,
  // New unified format (used by both protocols)
  metadata: {
    csp: { connectDomains: ["https://api.example.com"] },
    prefersBorder: true,
  },
  // ChatGPT-specific overrides (optional)
  appsSdkMetadata: {
    "openai/widgetDescription": "ChatGPT-specific description",
  },
});
```

**Option 3: Stay on Apps SDK**

If you only need ChatGPT support, you can stay on `type: "appsSdk"`:

```typescript
server.uiResource({
  type: "appsSdk", // ChatGPT only
  name: "my-widget",
  htmlTemplate: `...`,
  appsSdkMetadata: {
    "openai/widgetCSP": {
      connect_domains: ["https://api.example.com"],
    },
  },
});
```

### Field Mapping Reference

Complete mapping from Apps SDK to MCP Apps metadata:

| Apps SDK (`appsSdkMetadata`)   | MCP Apps (`metadata`) | Notes                                                   |
| ------------------------------ | --------------------- | ------------------------------------------------------- |
| `"openai/widgetCSP"`           | `csp`                 | CSP configuration object                                |
| `connect_domains`              | `connectDomains`      | Array of connection domains                             |
| `resource_domains`             | `resourceDomains`     | Array of resource domains                               |
| `frame_domains`                | `frameDomains`        | Array of frame domains                                  |
| `redirect_domains`             | `redirectDomains`     | Array of redirect domains (ChatGPT-specific)            |
| `script_directives`            | `scriptDirectives`    | Array of script CSP directives                          |
| `style_directives`             | `styleDirectives`     | Array of style CSP directives                           |
| `"openai/widgetPrefersBorder"`         | `prefersBorder`       | Boolean                                                 |
| `"openai/widgetDomain"`                | `domain`              | String (custom domain)                                  |
| `"openai/widgetDescription"`           | `widgetDescription`   | String (widget description)                             |
| `"openai/widgetAccessible"`            | `widgetAccessible`    | Boolean (ChatGPT-specific, can stay in appsSdkMetadata) |
| `"openai/locale"`                      | `locale`              | String (ChatGPT-specific, can stay in appsSdkMetadata)  |
| `"openai/toolInvocation/invoking"`     | `invoking`            | String  -  status text while tool runs (auto-defaulted)   |
| `"openai/toolInvocation/invoked"`      | `invoked`             | String  -  status text after tool completes (auto-defaulted) |

See [Content Security Policy](./content-security-policy) for full CSP configuration and field details.

### Migration Checklist

Follow these steps to migrate:

1. ✅ **Change the widget type**:

   ```typescript
   type: "appsSdk" → type: "mcpApps"
   ```

2. ✅ **Rename the metadata field**:

   ```typescript
   appsSdkMetadata: { ... } → metadata: { ... }
   ```

3. ✅ **Transform CSP configuration**:

   ```typescript
   // Before
   "openai/widgetCSP": {
     connect_domains: ["..."],
     resource_domains: ["..."]
   }

   // After
   csp: {
     connectDomains: ["..."],
     resourceDomains: ["..."]
   }
   ```

4. ✅ **Transform other metadata fields**:
   - Remove `"openai/` prefix from all keys
   - Convert remaining snake_case to camelCase

5. ✅ **Test in both environments**:
   - Test in ChatGPT (Apps SDK protocol)
   - Test in MCP Inspector with protocol toggle
   - Test in MCP Apps-compatible client (Claude, Goose, etc.)

6. ✅ **Verify widget behavior**:
   - Props received correctly
   - Theme syncing works
   - Tool calls function properly
   - CSP allows required resources (see [Content Security Policy](./content-security-policy))

## Comparison with MCP UI

MCP Apps is designed for **interactive widgets**, while MCP UI is better for simpler, static content:

| Feature              | MCP Apps                                              | MCP UI                   |
| -------------------- | ----------------------------------------------------- | ------------------------ |
| **Use Case**         | Interactive widgets with state                        | Static/simple content    |
| **Interactivity**    | Full React components, tool calls                     | Limited (mostly display) |
| **State Management** | `setState` + `ui/update-model-context`                | No state                 |
| **Tool Calls**       | ✅ Can call other MCP tools                           | ❌ No tool access        |
| **ChatGPT Support**  | ✅ Via dual-protocol                                  | ❌ Not supported         |
| **Complexity**       | Higher (full React app)                               | Lower (simple HTML)      |

**When to use MCP Apps:**

- Interactive dashboards
- Forms and data entry
- Multi-step workflows
- Real-time updates
- Complex visualizations

**When to use MCP UI:**

- Simple content display
- Read-only views
- Lightweight embeds

## Testing Your Widgets

### Using the Inspector

The MCP Inspector fully supports both MCP Apps and ChatGPT Apps SDK protocols:

1. **Start your server**: `npm run dev`
2. **Open Inspector**: `http://localhost:3000/inspector`
3. **Protocol Toggle**: Switch between MCP Apps and ChatGPT protocols
4. **Debug Controls**: Test different devices, locales, CSP modes
5. **Display Modes**: Test inline, picture-in-picture, and fullscreen

See [Debugging Widgets](/inspector/debugging-chatgpt-apps) for the complete guide.

### Testing in Production Clients

**ChatGPT:**

1. Enable Developer Mode in Settings → Connectors → Advanced
2. Add your MCP server URL
3. Start a conversation and trigger your tools

**Claude Desktop** (upcoming MCP Apps support):

1. Add your server to Claude's MCP configuration
2. Widgets render automatically when tools return them

**Goose:**

1. Configure your MCP server in Goose
2. Call tools that return widgets

## Protocol Adapters (Advanced)

Behind the scenes, mcp-use uses protocol adapters to transform your widget configuration:

```typescript
import { McpAppsAdapter, AppsSdkAdapter } from "mcp-use/server";

// These are created automatically when you use type: "mcpApps"
const mcpAppsAdapter = new McpAppsAdapter();
const appsSdkAdapter = new AppsSdkAdapter();

// Transform your unified metadata to each protocol
const mcpAppsMetadata = mcpAppsAdapter.transformMetadata(yourMetadata);
const appsSdkMetadata = appsSdkAdapter.transformMetadata(yourMetadata);
```

You rarely need to interact with adapters directly, but they're available for advanced use cases.

## Next Steps

- [Widget Components](/typescript/server/widget-components/usewidget) - Explore React components and hooks
- [Creating MCP Apps Server](/typescript/server/creating-mcp-apps-server) - Complete server setup guide
- [Debugging Widgets](/inspector/debugging-chatgpt-apps) - Test and debug with the Inspector

## Learn More

- [MCP Apps Extension (SEP-1865)](https://spec.modelcontextprotocol.io/specification/2025-11-21/extensions/apps/)
- [Official MCP Apps SDK](https://github.com/modelcontextprotocol/ext-apps)
- [Model Context Protocol](https://modelcontextprotocol.io/)
