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Below are the one-click install options available.\n\n### Install MCP on Kiro\n\n[![Add to Kiro](https://kiro.dev/images/add-to-kiro.svg)](https://kiro.dev/launch/mcp/add?name=vega-devtools-mcp&config=%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%40amazon-devices%2Fvega-devtools-mcp%40latest%22%5D%2C%22disabled%22%3Afalse%7D)\n\n### Install MCP on Cursor\n\n[![Install MCP Server](https://cursor.com/deeplink/mcp-install-dark.svg)](https://cursor.com/en-US/install-mcp?name=vega-devtools-mcp&config=eyJkaXNhYmxlZCI6ZmFsc2UsImNvbW1hbmQiOiJucHggLS12ZXJib3NlIC15IEBhbWF6b24tZGV2aWNlcy92ZWdhLWRldnRvb2xzLW1jcEBsYXRlc3QifQ%3D%3D)\n\n### Install MCP on VSCode\n\n[Add to VSCode](vscode:mcp/install?%7B%22name%22%3A%22vega-devtools-mcp%22%2C%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%40amazon-devices%2Fvega-devtools-mcp%40latest%22%5D%7D)\n\n\n## Installation with script\n\nRun the following command to **automatically** install Vega DevTools MCP in your AI Agent and add Vega-specific context document to your project to guide AI agents:\n\n```bash\nnpx @amazon-devices/vega-devtools-mcp@latest --init-context\n```\n\nThis interactive command will:\n\n1. **Display available AI agents** and their context file requirements\n2. **Let you select your preferred AI agent** from the supported list\n3. **Update selected agent's MCP settings** to configure the Vega DevTools MCP server \n4. **Ask for context installation directory** (defaults to current working directory)\n5. **Handle existing context files** by offering to merge or update content\n6. **Create the appropriate context file** in the correct location for your chosen AI agent\n\n### Example Usage\n\n```bash\n# Navigate to your project directory\ncd my-vega-project\n\n# Initialize context for your AI agent\nnpx @amazon-devices/vega-devtools-mcp@latest --init-context\n\n# Follow the interactive prompts:\n# 1. Select your AI agent (e.g., \"5\" for Kiro, \"7\" for Other/Custom)\n# 2. Enter y/n to automatically update Agent's MCP settings file\n# 3. Choose default context document installation path or enter custom path\n# 4. Choose action: merge, update or save file. Review the installed context document before proceeding.\n```\n\n### Using with Other AI Agents\n\nIf your AI agent isn't in the supported list, select **\"Other/Custom Agent\"** which provides:\n\n-   **View full content**: Display the complete context for manual copying\n-   **Manual setup**: Copy the content to your agent's configuration directory\n\n> ℹ️ Important: Start the MCP Server from Agent's MCP config, if not already started - check your current running MCPs to ensure the vega-devtools-mcp is listed as running/connected.\n\n## Configure Vega DevTools MCP manually\n\nTo manually configure the Vega DevTools MCP, add the MCP configuration in your AI Agent's MCP settings:\n\n```json\n  \"vega-devtools-mcp\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@amazon-devices/vega-devtools-mcp@latest\"],\n      \"type\": \"stdio\"\n  }\n```\n\nEach 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.\n\nBelow we list some popular AI agents and the link to how to install MCP servers.\n\n| #   | AI Agent               | MCP Setup Instructions Link                                                                                                                                    |\n| --- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 1   | Cursor                 | [Instructions](https://cursor.com/docs/context/mcp#using-mcpjson)                                                                                              |\n| 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\" |\n| 3   | Claude Code CLI        | [Instructions](https://code.claude.com/docs/en/mcp#option-3%3A-add-a-local-stdio-server)                                                                       |\n| 4   | Amazon Q IDE Extension | [Instructions](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/mcp-ide.html)                                                                          |\n| 5   | Amazon Q CLI           | [Instructions](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/command-line-mcp-config-CLI.html)                                                      |\n| 6   | Kiro                   | [Instructions](https://kiro.dev/docs/mcp/)                                                                                                                     |\n| 7   | Cline                  | [Instructions](https://docs.cline.bot/mcp/configuring-mcp-servers)                                                                                             |\n\n_If your agent is not listed, please ensure it supports MCP before continuing._\n\n> ℹ️ Important: Once configured, run `npx @amazon-devices/vega-devtools-mcp@latest --init-context` to install the Vega-context document for your preferred AI agent in the project directory\n\n## Usage\n\n### Command Line Options\n\n```bash\nnpx @amazon-devices/vega-devtools-mcp@latest --init-context     # Install Vega DevTools MCP and initialize Vega context for AI agents\nnpx @amazon-devices/vega-devtools-mcp@latest --version          # Show version information\nnpx @amazon-devices/vega-devtools-mcp@latest -v                 # Show version information (alias)\nnpx @amazon-devices/vega-devtools-mcp@latest --help             # Show help message\nnpx @amazon-devices/vega-devtools-mcp@latest -h                 # Show help message (alias)\n```\n\n### Verify Vega DevTools MCP is installed in your AI Agent\n\nIn your AI Agent's chat interface, run the following prompt\n\n```\nList the tools provided by Vega DevTools MCP\n```\n\nYou should see a response that includes the following tools:\n\n- analyze_perfetto_traces\n- read_document\n- list_documents\n\n## MCP Tools\n\nThe Vega DevTools MCP provides the following tools to assist with Vega app development:\n\n### 1. `read_document`\n\nRead documents related to App development for Amazon Vega OS. This tool provides access to comprehensive documentation about Vega app development and debugging topics.\n\n**Parameters:**\n- `document_name` (required): Name of the document to read (e.g., `react-native-for-vega-performance-best-practices.md`). Must be a markdown document with `.md` extension.\n\n**Example usage:**\n```\nRead the document react-native-for-vega-performance-best-practices.md\n```\n\n### 2. `list_documents`\n\nList 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.\n\n**Parameters:**\n- `documentType` (optional): Filter documents by type. Valid values: `KB` (Knowledge Base), `PROMPT`, `STEERING`, `WORKFLOW`\n\n**Example usage:**\n```\nList all available Vega documents\n```\nor\n```\nList documents of type KB\n```\n\n### 3. `analyze_perfetto_traces`\n\nAnalyze 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.\n\n**Parameters:**\n- `traceFilePath` (required): Path to the trace file to analyze. Usually found in Vega performance data output directories with names like `iter_*_vs_trace`\n- `queryType` (optional): Type of query to execute. Default: `kpi_analysis`\n- `kpiType` (optional): Specific KPI type to analyze. Options: `ttff` (Time to First Frame), `ttfd` (Time to First Display), `all`. Default: `all`\n- `customQuery` (optional): Custom PerfettoSQL query to execute (overrides queryType and kpiType if provided)\n- `processNames` (optional): Additional process names to filter by\n- `appProcessName` (optional): Main application process name to analyze (will be auto-detected if not provided)\n\n**Example usage:**\n```\nAnalyze the trace file at /path/to/iter_1_vs_trace\n```\n\n## MCP Prompts\n\n> Check if your AI Agents supports MCP Prompts (`/prompts`) in https://modelcontextprotocol.io/clients\n\nVega DevTools MCP provides the following pre-defined prompt templates for common workflows that can be 1-click executed in `/prompts` in your AI Agent:\n\n**Important**: Always run `/prompts` in your AI Agent to see the full list of prompts provided by Vega DevTools MCP\n\n### 1. `diagnose_kpi_ttff`\n\n**Description:** Diagnose Vega application's Time to First Frame (TTFF) KPI\n\n**Parameters:**\n- `kpi_report_file_path` (required, string): Absolute path to the KPI report file\n- `kpi_to_diagnose` (required, string): Name of the KPI from KPI report to diagnose\n\n**Example usage:**\n```\n> @diagnose_kpi_ttff /path/to/report.json ttff\n```\n\n### 2. `diagnose_kpi_ttfd`\n\n**Description:** Diagnose Vega application's Time to Fully Drawn (TTFD) KPI\n\n**Parameters:**\n- `kpi_report_file_path` (required, string): Absolute path to the KPI report file\n- `kpi_to_diagnose` (required, string): Name of the KPI from KPI report to diagnose\n\n**Example usage:**\n```\n> @diagnose_kpi_ttfd /path/to/report.json ttfd\n```\n\n### 3. `apply_performance_best_practices`\n\n**Description:** Diagnose and optimize React Native application performance issues including component rendering, memory management, navigation, network optimization, and state management.\n\n**Parameters:**\n- `app_source_path` (required, string): Path to the React Native application source code directory for analysis\n\n**Example usage:**\n```\n> @apply_performance_best_practices /path/to/my-vega-app/src\n```\n\n### 4. `detect_component_re-renders`\n\n**Description:** Diagnose and optimize Vega application UI fluidity performance issues caused by component re-rendering using React Native tools.\n\n**Parameters:**\n- `vega_app_package_path` (required, string): Absolute path to the Vega app package root directory\n\n**Example usage:**\n```\n> @detect_component_re-renders /path/to/my-vega-app\n```\n\n### 5. `upgrade_carousel_component`\n\n**Description:** Assists in migrating to newer versions of the Carousel component in the Vega SDK.\n\n**Parameters:**\n- `current_implementation_file_path` (required, string): Absolute path to the file containing the V1 implementation of Carousel\n- `current_version` (required, string): The current version of Carousel, independent of package\n- `target_version` (required, string): The target version of Carousel, independent of package\n\n**Example usage:**\n```\n> @upgrade_carousel_component /path/to/HomeScreen.tsx 1.0.6 2.0.0\n```\n","readmeFilename":"README.md"}