# Amazon Devices Builder Tools MCP Server

**English** | [日本語](README.ja.md)

A Model Context Protocol (MCP) server that provides context and tools for AI agents to develop and debug apps for Amazon's [Vega OS](https://developer.amazon.com/apps-and-games/vega).

## Features

The Amazon Devices Builder Tools MCP supports the following capabilities:

| App Dev Area                   | Description                                                                                      | Example Prompts                                                                                                                                                                                                                                                                                                                      |
| ------------------------------ | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Onboarding**                 | Onboard to Vega app development using AI agents with setup guidance and Q&A support              | <ul><li>`Help me set up Vega app project and test my VPKG on Virtual Device?`</li><li>`Help me setup Vega SDK?`</li><li>`Build and run the app on Virtual device/Connected Device.`</li><li>`Can you tell me about vega app development?`</li><li>`What is the Vega CLI command for measuring cpu usage on a Vega device?`</li></ul> |
| **Performance**                | Debug and fix Time to First Frame (TTFF), Time to Fully Drawn (TTFD), and UI re-rendering issues | <ul><li>`Why is the TTFF higher for my Vega app?`</li><li>`Can you help me debug and fix Time to First Frame issue in my Vega app?`</li><li>`Why is the TTFD higher for my Vega app?`</li><li>`Can you help me minimize unnecessary re-renders in my app?`</li><li>`Find any re-rendering issues in my app.`</li></ul>               |
| **Performance Best Practices** | Analyze components and code to ensure they follow React Native Vega performance best practices   | <ul><li>`Help me analyze if this component is following React Native Vega performance best practices`</li></ul>                                                                                                                                                                                                                      |
| **Best Practices**             | Validate manifest files and implement Vega components following best practices                   | <ul><li>`Can you help me upgrade my MovieCarousel.tsx from Carousel V1 to V2?`</li><li>`Can you help me implement Carousel?`</li><li>`Can you validate my manifest file and update as needed for my app?`</li></ul>                                                                                                                  |
| **UI Development**             | Create Vega app UI from screenshots and design mockups                                           | <ul><li>`I have an image at {image_path}. Create a Vega app by creating a new folder using the sample template, update the sample test app to match the image, and confirm that the app can be built and run.`</li></ul>                                                                                                             |
| **Focus Management**           | Implement D-Pad navigation and focus management for TV interfaces                                | <ul><li>`Can you create an app with 2 buttons to show case focus management?`</li></ul>                                                                                                                                                                                                                                              |
| **Give Feedback**              | Provide feedback to Amazon about your experience with the MCP                                    | <ul><li>`I want to provide feedback`</li><li>`How can I provide vega mcp feedback?`</li></ul>                                                                                                                                                                                                                                        |
| **Crash Analysis**              | Diagnose JavaScript, Native, LMK, and ANR crashes with automated ACR analysis                    | <ul><li>`Why did my app crash?`</li><li>`Help me analyze this ACR file /path/to/crash.acr`</li></ul>                                                                                                                                                                                                                                                |
| **Key Input Latency**          | Diagnose and resolve key input latency issues through JS Thread performance and input event analysis | <ul><li>`Why is my app responding slowly to key presses?`</li><li>`Help me diagnose key input latency in my Vega app`</li><li>`My button clicks feel laggy, can you help?`</li></ul>                                                                                                                                                 |
| **UI Fluidity**                | Diagnose UI fluidity KPI failures through component re-rendering and CPU performance analysis    | <ul><li>`My app's UI fluidity score is low, help me diagnose it`</li><li>`Help me fix frame drops and jank in my Vega app`</li><li>`Why is scrolling not smooth in my app?`</li></ul>                                                                                                                                                |
| **App Porting** *(Beta)*       | Port FireOS apps to Vega or wrap a web URL as a Vega app, with auto-detection of app type. Supports four input paths: FOS WebView → Vega WebView, FOS React Native → VegaScript, FOS Native (Java/Kotlin) → VegaScript *(partial support)*, and Web URL → Vega WebView. | <ul><li>`Port my TV app to Vega`</li><li>`Help me migrate my FireOS app at /path/to/fos-app to Vega`</li><li>`Port my FOS React Native app to VegaScript`</li><li>`Convert my FOS WebView app to a Vega Web App`</li><li>`Wrap https://example.com as a Vega WebView app`</li></ul> |
| **Appstore IAP Integration**   | Integrate and test Amazon Appstore In-App Purchasing (IAP) SDK in Vega apps                      | <ul><li>`Help me integrate IAP in my Vega app`</li><li>`How do I set up In-App Purchasing for my Vega app?`</li><li>`Help me test IAP in my Vega app`</li></ul>                                                                                                                                                                      |
| **SDK & CLI**                  | Install/update SDK and efficiently run CLI tools via spec-driven CLI                             | <ul><li>`Install/update SDK from AI agents`</li><li>`Build my app`</li><li>`Run my app on virtual device`</li><li>`Run my app on physical device`</li><li>`Run KPI Visualizer and get performance results`</li></ul>                                                                                                                 |
| **Remote Knowledge Search**    | Search and retrieve documentation from developer.amazon.com and community.amazondeveloper.com to get AI assistance on any documented app development topic beyond curated local workflows | <ul><li>`How can I do a beta test of my Vega app?`</li><li>`What are the accessibility guidelines for Vega apps?`</li><li>`How do I submit my app to the Amazon Appstore?`</li><li>`What DRM options are available for Vega?`</li></ul>                                                                                                |
| **RN83 Alpha Preview** *(Alpha)* | Guide allowlisted alpha partners through the full RN 0.83 preview lifecycle: private registry setup, preview SDK installation, migration, and cleanup/uninstall steps. | <ul><li>`Help me set up the RN83 alpha preview`</li><li>`How do I install the preview SDK?`</li><li>`How do I uninstall the RN83 preview SDK and clean up?`</li></ul> |
| **Media Playback**               | Add media playback to Vega apps using W3C MSE/EME APIs with adaptive streaming (HLS/DASH), DRM support, Shaka Player integration, headless playback architecture, and media controls | <ul><li>`Add a simple media playback implementation to my Vega app at /path/to/my-vega-app using https://example.com/stream.m3u8`</li><li>`Add DRM-protected playback to my app using Widevine with license URL https://license.example.com`</li><li>`Implement headless media playback architecture in my Vega app to decouple playback from the UI thread`</li><li>`Update Shaka Player from version x to version y in my Vega media playback app`</li><li>`Add captions and subtitle support to my media playback app`</li></ul> |

## One-Click Installation

Some of the popular AI Agents support installing MCP servers with a single click. Below are the one-click install options available.

### Install MCP on Kiro

[![Add to Kiro](https://kiro.dev/images/add-to-kiro.svg)](https://kiro.dev/launch/mcp/add?name=amazon-devices-buildertools-mcp&config=%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%40amazon-devices%2Famazon-devices-buildertools-mcp%40latest%22%5D%2C%22disabled%22%3Afalse%7D)

### Install MCP on Cursor

[![Install MCP Server](https://cursor.com/deeplink/mcp-install-dark.svg)](https://cursor.com/en-US/install-mcp?name=amazon-devices-buildertools-mcp&config=eyJjb21tYW5kIjoibnB4IC15IEBhbWF6b24tZGV2aWNlcy9hbWF6b24tZGV2aWNlcy1idWlsZGVydG9vbHMtbWNwQGxhdGVzdCJ9)

## Installation with script

Run the following command to **automatically** install Amazon Devices Builder Tools MCP in your AI Agent and add Vega-specific context document to your project to guide AI agents:

> ℹ️ Important: It is important to run `npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context` to install the Vega-context document for your preferred AI agent in the project directory. This step is required even after one-click install of mcp.

> ⚠️ **Deprecation Notice**: The `--init-context` flag is deprecated and will be removed in a future version. Please use the `init-context` subcommand instead.

```bash
# Recommended (new subcommand)
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context

# Deprecated (will be removed)
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest --init-context
```

This command supports both **interactive** and **non-interactive** modes:

**Interactive Mode (default):**

1. **Display available AI agents** and their context file requirements
2. **Let you select your preferred AI agent** from the supported list
3. **Update selected agent's MCP settings** to configure the Amazon Devices Builder Tools MCP server
4. **Ask for context installation directory** (defaults to current working directory)
5. **Handle existing context files** by offering to merge or update content
6. **Create the appropriate context file** in the correct location for your chosen AI agent

**Non-Interactive Mode:**
Use command-line options to skip prompts and automate the setup process.

### Command Line Options

```bash
init-context [options]

Options:
  -a, --agent <type>               Target AI agent (cursor, cline, kiro, claude-code-cli,
                                   claude-code-desktop, github-copilot, amazon-q, other)
  -p, --context-document-path      Path to save context document
  -d, --skip-context-document      Skip context document installation (MCP config only)
  -m, --skip-mcp-config            Skip MCP configuration (context document only)
  -f, --force                      Skip all confirmation prompts and force overwrite
  -h, --help                       Show help for init-context command
```

> ⚠️ **Note**: Command-line options are only supported with the `init-context` subcommand. The deprecated `--init-context` flag does not support these options.

### Example Usage

```bash
# Navigate to your project directory
cd my-vega-project

# Interactive mode (default)
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context

# Non-interactive with specific agent
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context --agent cursor --force
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context -a cursor -f

# MCP config only (skip context document)
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context --agent cursor --skip-context-document
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context -a cursor -d

# Context document only with custom path (skip MCP config)
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context --agent kiro --context-document-path ./docs --skip-mcp-config
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context -a kiro -p ./docs -m

# Show help
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context --help
```

### Interactive Mode Prompts

When running in interactive mode, you'll be guided through:

1. Select your AI agent (e.g., "1" for Cursor, "6" for Kiro, "8" for Other/Custom)
2. Enter y/n to automatically update Agent's MCP settings file
3. Choose default context document installation path or enter custom path
4. Choose action: merge, update or save file. Review the installed context document before proceeding.

### Using with Other AI Agents

If your AI agent isn't in the supported list, select **"Other/Custom Agent"** which provides:

- **View full content**: Display the complete context for manual copying
- **Manual setup**: Copy the content to your agent's configuration directory

> ℹ️ Important: Start the MCP Server from Agent's MCP config, if not already started - check your current running MCPs to ensure the amazon-devices-buildertools-mcp is listed as running/connected.

## Configure Amazon Devices Builder Tools MCP manually

To manually configure the Amazon Devices Builder Tools MCP, add the MCP configuration in your AI Agent's MCP settings:

```json
  "amazon-devices-buildertools-mcp": {
      "command": "npx",
      "args": ["-y", "@amazon-devices/amazon-devices-buildertools-mcp@latest"],
      "type": "stdio"
  }
```

Each Agent has slightly different instructions, but many involve using an "mcp.json" (or similar) file where you can add the specific configuration for this new MCP server.

Below we list some popular AI agents and the link to how to install MCP servers.

| #   | AI Agent               | MCP Setup Instructions Link                                                                                                                                    |
| --- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1   | Cursor                 | [Instructions](https://cursor.com/docs/context/mcp#using-mcpjson)                                                                                              |
| 2   | Github Copilot         | [Instructions](https://docs.github.com/en/copilot/how-tos/provide-context/use-mcp/extend-copilot-chat-with-mcp) then choose "Configuring MCP Servers Manually" |
| 3   | Claude Code            | [Instructions](https://code.claude.com/docs/en/mcp#option-3%3A-add-a-local-stdio-server)                                                                       |
| 4   | Claude Code Desktop    | [Instructions](https://support.claude.com/en/articles/10949351-getting-started-with-local-mcp-servers-on-claude-desktop)                                       |
| 5   | Amazon Q IDE Extension | [Instructions](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/mcp-ide.html)                                                                          |
| 6   | Amazon Q CLI           | [Instructions](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/command-line-mcp-config-CLI.html)                                                      |
| 7   | Kiro                   | [Instructions](https://kiro.dev/docs/mcp/)                                                                                                                     |
| 8   | Cline                  | [Instructions](https://docs.cline.bot/mcp/configuring-mcp-servers)                                                                                             |

_If your agent is not listed, please ensure it supports MCP before continuing._

> ℹ️ Important: Once configured, run `npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context` to install the Vega-context document for your preferred AI agent in the project directory

## Usage

### Command Line Options

```bash
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest init-context       # Install Amazon Devices Builder Tools MCP and initialize Vega context for AI agents
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest check-status       # Check setup status of AI agents
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest --version          # Show version information
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest -v                 # Show version information (alias)
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest --help             # Show help message
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest -h                 # Show help message (alias)
```

### Check Agent Setup Status

After installing the MCP server and context documents, you can verify your setup using the `check-status` command:

```bash
# Check all agents
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest check-status

# Check specific agent
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest check-status --agent kiro
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest check-status -a cursor

# Show help
npx -y @amazon-devices/amazon-devices-buildertools-mcp@latest check-status --help
```

**What it checks:**
- ✅ Context document installation status and version
- ✅ MCP configuration status
- ✅ Detection of legacy configurations
- ✅ Overall setup summary

**Example output:**

```
====================================================
🔍 Checking Agent Setup Status
====================================================

─────────────────────────────────────────────────────────────────────────────
Agent                  │ Context Document         │ MCP Configuration        
─────────────────────────────────────────────────────────────────────────────
Cursor                 │ ✅ v5.1                   │ ✅ Configured             
Kiro                   │ ✅ v5.1                   │ ✅ Configured             
─────────────────────────────────────────────────────────────────────────────

📊 Summary:
   Total agents checked: 2
   ✅ Fully configured: 2
   ⚠️  Partially configured: 0
   ❌ Not configured: 0
```

### Verify Amazon Devices Builder Tools MCP is installed in your AI Agent

In your AI Agent's chat interface, run the following prompt

```
List the tools provided by Amazon Devices Builder Tools MCP
```

You should see a response that includes the following tools:

- analyze_perfetto_traces
- get_app_hot_functions
- list_documents
- read_asset
- read_document
- search_documentation
- symbolicate_acr

## MCP Tools

The Amazon Devices Builder Tools MCP provides the following tools to assist with Vega app development:

### 1. `analyze_perfetto_traces`

Analyze Vega platform traces using Perfetto trace processor to extract KPI metrics and related performance data. This tool helps diagnose performance issues and analyze app launch times.

**Parameters:**

- `traceFilePath` (required): Path to the trace file to analyze. Usually found in Vega performance data output directories with names like `iter_*_vs_trace`
- `queryType` (optional): Type of query to execute. Default: `kpi_analysis`
- `kpiType` (optional): Specific KPI type to analyze. Options: `ttff` (Time to First Frame), `ttfd` (Time to First Display), `all`. Default: `all`
- `customQuery` (optional): Custom PerfettoSQL query to execute (overrides queryType and kpiType if provided)
- `processNames` (optional): Additional process names to filter by
- `appProcessName` (optional): Main application process name to analyze (will be auto-detected if not provided)

**Example usage:**

```
Analyze the trace file at /path/to/iter_1_vs_trace
```

### 2. `get_app_hot_functions`

Reads and analyzes CPU trace files to identify hot functions (CPU-intensive operations) contributing to performance bottlenecks. This tool helps pinpoint which functions in your application are consuming the most CPU time, enabling targeted performance optimization.

**Parameters:**

- `traceDataFilePath` (required): Path to the trace data file from the Activity Monitor. Usually found in Vega performance data output directories with names like `iter_<iteration>_trace<epoch>-converted.json` (e.g., `iter_2_trace1766067380670018106-converted.json`)
- `limit` (optional): Maximum number of hot functions to return (default: 10)
- `useSelfTime` (optional): Flag to indicate whether to use self CPU time or total CPU time for sorting (default: false)
- `includeLibraryFunctions` (optional): Flag to indicate whether to include library functions (default: false)
- `startTimeOffset` (optional): Start time offset in seconds from trace beginning (e.g., 5.2 for analysis starting 5.2 seconds after trace start)
- `endOffsetSeconds` (optional): End time offset in seconds from trace beginning (e.g., 15.8 for analysis ending 15.8 seconds after trace start)

**Example usage:**

```
Analyze hot functions in the trace file at /path/to/iter_2_trace1766067380670018106-converted.json
```

or with time window filtering:

```
Analyze hot functions in /path/to/trace.json from 5 to 15 seconds with a limit of 20 functions
```

### 3. `list_documents`

List all available Vega documents related to App development for Amazon Vega OS. Returns name and description of available documents that can be retrieved using the `read_document` tool.

**Parameters:**

- `documentType` (optional): Filter documents by type. Valid values: `KB` (Knowledge Base), `PROMPT`, `STEERING`, `WORKFLOW`

**Example usage:**

```
List all available Vega documents
```

or

```
List documents of type KB
```

### 4. `read_asset`

Read assets referenced in Vega documentation. Assets are saved to a temporary location and the path is returned. Images can be viewed with
`fs_read` and scripts executed with the appropriate interpreter (e.g., `python3`)

**Parameters:**

- `asset_id` (required): Path of the asset to read (e.g., `assets/scripts/example.py` or `assets/images/diagram.png`)

**Example usage:**

```
Read the script assets/scripts/setup.sh and execute it
```

or

```
Read the image assets/images/architecture.png and describe what it shows
```

### 5. `read_document`

Read documents related to App development for Amazon Vega OS. This tool provides access to comprehensive documentation about Vega app development and debugging topics.

**Parameters:**

- `document_uri` (required): URI of the document to read. Accepts a local document name (e.g., `vega_cli_commands_reference.md`) or a developer.amazon.com URL.

**Example usage:**

```
Read the document react_native_for_vega_architecture.md
```

### 6. `search_documentation`

Search Vega development documentation using full-text search. Returns document URIs and relevant snippets matching the query. Use `read_document` with the returned URI to get full content.

**Parameters:**

- `query` (required): Search query string to find relevant documents

**Example usage:**

```
Search for documents about "performance optimization"
```

### 7. `symbolicate_acr`

Symbolicate ACR (Amazon Crash Report) files and create a report. Supports both GDB-based native crash symbolication and JavaScript stack trace symbolication.

**Parameters:**

- `acrFilePath` (required): Path to the ACR file to symbolicate
- `decodeMode` (required): `gdb` for native crashes (requires `debugFsPath` or `deviceId`), `js` for JavaScript stack traces
- `debugFsPath` (optional): Path to Debug Root FS. Used with `gdb` mode.
- `deviceId` (optional): Target device serial number to fetch Debug FS. Used with `gdb` mode.

**Example usage:**

```
Symbolicate the crash file at /path/to/crash.acr using JS mode
```

## MCP Prompts

> Check if your AI Agents supports MCP Prompts (`/prompts`) in https://modelcontextprotocol.io/clients

Amazon Devices Builder Tools MCP provides the following pre-defined prompt templates for common workflows that can be 1-click executed in `/prompts` in your AI Agent:

**Important**: Always run `/prompts` in your AI Agent to see the full list of prompts provided by Amazon Devices Builder Tools MCP

### 1. `apply_performance_best_practices`

**Description:** Diagnose and optimize React Native application performance issues including component rendering, memory management, navigation, network optimization, and state management.

**Parameters:**

- `app_source_path` (required, string): Path to the React Native application source code directory for analysis

**Example usage:**

```
> @apply_performance_best_practices /path/to/my-vega-app/src
```

### 2. `detect_component_re-renders`

**Description:** Diagnose and optimize Vega application UI fluidity performance issues caused by component re-rendering using React Native tools.

**Parameters:**

- `vega_app_package_path` (required, string): Absolute path to the Vega app package root directory

**Example usage:**

```
> @detect_component_re-renders /path/to/my-vega-app
```

### 3. `diagnose-slowAppLaunch`

**Description:** Diagnose Vega application's slow app launch KPIs (TTFF and/or TTFD).

**Parameters:**

- `kpi_report_file_path` (required, string): Absolute path to the KPI report file
- `kpi_to_diagnose` (required, string): Name of the KPI from KPI report to diagnose

**Example usage:**

```
> @diagnose-slowAppLaunch /path/to/report.json ttff
```

### 4. `diagnose_crash`

**Description:** Diagnose crashes in Vega applications including JavaScript, Native, LMK (Low Memory Killer), and ANR (Application Not Responding) crashes. Automatically discovers ACR files, symbolicates stack traces, and routes to the appropriate analysis workflow.

**Parameters:**

- `acr_file_path` (optional, string): Path to ACR file. If not provided, the tool will auto-discover ACR files from the device temp directory.

**Example usage:**

```
> @diagnose_crash /path/to/crash.acr
```

### 5. `diagnose_key_input_latency`

**Description:** Diagnose and resolve key input latency issues in React Native Vega apps through systematic analysis of JS Thread performance, CPU bottlenecks, and input event handling optimization.

**Parameters:**

- `vega_app_package_path` (required, string): Absolute path to the Vega app package root directory
- `app_process_name` (optional, string): Name of the app process experiencing input latency. If not provided, resolved automatically from manifest.toml.
- `perfetto_trace_file_path` (optional, string): Absolute path to existing Perfetto trace file (if already captured)
- `target_component_or_button` (optional, string): Specific button or component name to investigate
- `latency_threshold_ms` (optional, number): Maximum acceptable input latency in milliseconds. Default is 100.

**Example usage:**

```
> @diagnose_key_input_latency /path/to/my-vega-app
```

### 6. `diagnose_ui_fluidity`

**Description:** Comprehensive diagnosis of UI fluidity KPI failures for React Native Vega apps through systematic analysis of component re-rendering and CPU performance bottlenecks.

**Parameters:**

- `vega_app_package_path` (required, string): Absolute path to the Vega app package root directory

**Example usage:**

```
> @diagnose_ui_fluidity /path/to/my-vega-app
```

### 7. `fix_hot_functions`

**Description:** Diagnose and optimize Vega application UI fluidity issues by analyzing and fixing CPU-intensive hot functions.

**Parameters:**

- `vega_app_package_path` (required, string): Absolute path to the Vega app package root directory

**Example usage:**

```
> @fix_hot_functions /path/to/my-vega-app
```

### 8. `react_native_for_vega_add_simple_media_playback_implementation`

**Description:** Add a simple media playback implementation to an existing React Native for Vega application.

**Parameters:**

- `vega_app_package_path` (required, string): Absolute path to the Vega app package root directory

**Example usage:**

```
> @react_native_for_vega_add_simple_media_playback_implementation /path/to/my-vega-app
```

### 9. `react_native_for_vega_ai_assisted_rn_upgrade`

**Description:** Trigger generic turn-based workflow for React Native version upgrades with AI-assisted migration.

**Parameters:**

- `vega_app_package_path` (required, string): Absolute path to the Vega app package root directory

**Example usage:**

```
> @react_native_for_vega_ai_assisted_rn_upgrade /path/to/my-vega-app
```

### 10. `upgrade_carousel_component`

**Description:** Assists in migrating to newer versions of the Carousel component in the Vega SDK.

**Parameters:**

- `current_implementation_file_path` (required, string): Absolute path to the file containing the V1 implementation of Carousel
- `current_version` (required, string): The current version of Carousel, independent of package
- `target_version` (required, string): The target version of Carousel, independent of package

**Example usage:**

```
> @upgrade_carousel_component /path/to/HomeScreen.tsx 1.0.6 2.0.0
```

### 11. `react_native_for_vega_rn83_alpha_preview`

**Description:** Guide allowlisted alpha partners through the full RN 0.83 preview lifecycle using the bundled knowledge base. Covers private registry configuration, preview SDK installation, migration, and cleanup/uninstall steps.

**Parameters:** None

**Example usage:**

```
> @react_native_for_vega_rn83_alpha_preview
```
