[{"_1":2,"_434":-5,"_435":-5},"loaderData",{"_3":4,"_22":23,"_252":253},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"bot","id",17376273,"membership","referenceId","0199a1cf-fc95-73df-b48c-1059b28acf51","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_24":25,"_65":-5,"_66":26,"_27":67,"_168":169,"_250":251},"availableLocales",[26,38,45,52,59],{"_27":28,"_31":35,"_36":37},"mcpServer",{"_29":30,"_31":32,"_33":34},"description","A bridge between Unity and AI assistants that enables AI to interact with Unity game environments through a standardized interface for code execution, scene analysis, and runtime debugging.","name","Unity-MCP","readme","# Unity-MCP\n\nA bridge between Unity and AI assistants using the Model Context Protocol (MCP).\n\n## Overview\n\nUnity-MCP is an open-source implementation of the Model Context Protocol for Unity game development. It enables AI assistants to interact with Unity game environments through a standardized interface, allowing for AI-assisted game development, automated testing, scene analysis, and runtime debugging.\n\n### Architecture\n\nThe architecture has been simplified to use AILogger for persistence, removing the need for a separate server component:\n\n```\nAI Assistant <-> Unity-MCP STDIO Client <-> Unity Client <-> AILogger\n```\n\n- **AI Assistant**: Communicates with the Unity-MCP STDIO Client using the MCP protocol\n- **Unity-MCP STDIO Client**: Forwards commands to the Unity Client and stores results in AILogger\n- **Unity Client**: Executes commands in Unity and returns results\n- **AILogger**: Stores logs and results for later retrieval\n\nThe Unity-MCP STDIO Client communicates directly with the Unity Client, which provides endpoints for both code execution and queries. The query tool transforms queries into code execution by wrapping them in a `return` statement.\n\n## Features\n\n- Execute C# code in the Unity runtime environment\n- Inspect game objects and their components\n- Analyze scene hierarchies and structures\n- Run tests and receive results\n- Invoke methods on game objects and components\n- Modify game state during runtime\n\n## Deployment Options\n\n- Unity Editor Extension: An Editor extension that persists beyond game execution cycles\n- Docker Container: A containerized version that communicates with Unity over the network\n- NPX Package: A Node.js package that can be installed and run via NPX\n\n## Documentation\n\n- [MCP Architecture](docs/mcp-architecture.md): Overview of the MCP architecture and namespaces\n- [MCP STDIO Client](docs/mcp-stdio-client.md): Information about the MCP STDIO client and its logging capabilities\n- [Query Tool](docs/query-tool.md): Detailed information about the query tool and how it works\n- [AILogger Integration](docs/ai-logger-sdk.md): Detailed information about the AILogger integration\n- [API Reference](docs/api-reference.md): Detailed information about the API endpoints\n- [Installation Guide](docs/installation.md): Step-by-step instructions for installing and setting up Unity-MCP\n- [Development Guide](docs/development.md): Information about the development environment and workflow\n- [Hot Reloading Guide](docs/hot-reloading.md): Detailed information about hot reloading in the development environment\n- [Hot Reloading Quick Reference](docs/hot-reloading-quick-reference.md): Quick reference guide for hot reloading commands and tips\n- [Contributing Guide](docs/contributing-guide.md): Guidelines for contributing to the project\n\n## Getting Started\n\nTo get started with Unity-MCP, follow these steps:\n\n1. Clone the repository:\n ```bash\n git clone https://github.com/TSavo/Unity-MCP.git\n cd Unity-MCP\n ```\n\n2. Install dependencies:\n ```bash\n npm install\n ```\n\n3. Build the project:\n ```bash\n npm run build\n ```\n\n4. Start the MCP STDIO client:\n ```bash\n npm start\n ```\n\n This will start the MCP STDIO client that communicates with Unity and uses AILogger for persistence.\n\n Note: Make sure AILogger is running on http://localhost:3030 or set the AI_LOGGER_URL environment variable to point to your AILogger instance.\n\n5. Run tests:\n ```bash\n # Run all tests\n npm test\n\n # Run only unit tests\n npm run test:unit\n\n # Run only e2e tests\n npm run test:e2e\n\n # Run tests with a specific pattern\n npm test -- --testNamePattern=\"should return the server manifest\"\n npm run test:unit -- --testNamePattern=\"should return the server manifest\"\n npm run test:e2e -- --testNamePattern=\"should discover the test server\"\n ```\n\nFor more detailed instructions, see the [Installation Guide](docs/installation.md).\n\n## Connecting to AI Assistants\n\nTo connect the Unity-MCP bridge to an AI assistant, you need to create an MCP configuration file:\n\n```json\n{\n \"mcpServers\": {\n \"unity-ai-bridge\": {\n \"url\": \"http://localhost:8080/sse\"\n }\n }\n}\n```\n\nPlace this file in the appropriate location for your AI assistant. For Claude, this would typically be in the Claude Desktop app's configuration directory.\n\n### Available Tools\n\nThe Unity-MCP bridge provides the following tools:\n\n1. **execute_code**: Execute C# code directly in Unity.\n2. **query**: Execute a query using dot notation to access objects, properties, and methods.\n3. **get_logs**: Retrieve logs from AILogger.\n4. **get_log_by_name**: Retrieve a specific log from AILogger.\n\n## Usage Examples\n\n### Executing Code in Unity\n\nYou can execute C# code in Unity using the `execute_code` tool. The code will be executed in the Unity runtime environment, and the result will be stored in AILogger for later retrieval.\n\n#### JSON-RPC Request\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"execute_code\",\n \"arguments\": {\n \"code\": \"Debug.Log(\\\"Hello from Unity!\\\"); return GameObject.FindObjectsOfType().Length;\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### JSON-RPC Response\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-execute-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123}}\"\n }\n ]\n }\n}\n```\n\n### Querying Unity Objects\n\nYou can query Unity objects using the `query` tool. This allows you to access objects, properties, and methods using dot notation.\n\n#### JSON-RPC Request\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"query\",\n \"arguments\": {\n \"query\": \"Camera.main.transform.position\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### JSON-RPC Response\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-query-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":{\\\"x\\\":0,\\\"y\\\":1,\\\"z\\\":-10},\\\"executionTime\\\":45}}\"\n }\n ]\n }\n}\n```\n\n### Retrieving Results from AILogger\n\nYou can retrieve the results of previous operations from AILogger using the `get_log_by_name` tool.\n\n#### JSON-RPC Request\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"get_log_by_name\",\n \"arguments\": {\n \"log_name\": \"unity-execute-1712534400000\",\n \"limit\": 1\n }\n }\n}\n```\n\n#### JSON-RPC Response\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"entries\\\":[{\\\"id\\\":\\\"123e4567-e89b-12d3-a456-426614174000\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"data\\\":{\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"}]}\"\n }\n ]\n }\n}\n```\n\n### Example Usage\n\nOnce the AI assistant has access to the Unity tool, you can ask it to perform tasks like:\n\n```\nCan you execute the following C# code in Unity?\n\nGameObject.Find(\"Player\").transform.position = new Vector3(0, 1, 0);\n```\n\n## License\n\nMIT\n\n## Author\n\nT Savo ([@TSavo](https://github.com/TSavo))\n","English","tag","en-US",{"_27":39,"_31":43,"_36":44},{"_29":40,"_31":41,"_33":42},"Un puente entre Unity y los asistentes de IA que permite a la IA interactuar con los entornos de juego de Unity a través de una interfaz estandarizada para la ejecución de código, el análisis de escenas y la depuración en tiempo de ejecución.","Unidad-MCP","# Unidad-MCP\n\nUn puente entre Unity y los asistentes de IA que utilizan el Protocolo de contexto de modelo (MCP).\n\n## Descripción general\n\nUnity-MCP es una implementación de código abierto del Protocolo de Contexto de Modelo para el desarrollo de juegos Unity. Permite que los asistentes de IA interactúen con los entornos de juego Unity a través de una interfaz estandarizada, lo que facilita el desarrollo de juegos asistido por IA, las pruebas automatizadas, el análisis de escenas y la depuración en tiempo de ejecución.\n\n### Arquitectura\n\nSe ha simplificado la arquitectura para utilizar AILogger para la persistencia, eliminando la necesidad de un componente de servidor separado:\n\n```\nAI Assistant <-> Unity-MCP STDIO Client <-> Unity Client <-> AILogger\n```\n\n* **Asistente de IA** : se comunica con el cliente Unity-MCP STDIO mediante el protocolo MCP\n* **Cliente Unity-MCP STDIO** : reenvía comandos al cliente Unity y almacena los resultados en AILogger\n* **Cliente de Unity** : ejecuta comandos en Unity y devuelve resultados\n* **AILogger** : almacena registros y resultados para su posterior recuperación\n\nEl cliente STDIO de Unity-MCP se comunica directamente con el cliente de Unity, que proporciona puntos finales tanto para la ejecución de código como para las consultas. La herramienta de consultas transforma las consultas en ejecución de código envolviéndolas en una declaración `return` .\n\n## Características\n\n* Ejecutar código C# en el entorno de ejecución de Unity\n* Inspeccionar los objetos del juego y sus componentes\n* Analizar jerarquías y estructuras de escenas\n* Ejecutar pruebas y recibir resultados\n* Invocar métodos en objetos y componentes del juego\n* Modificar el estado del juego durante el tiempo de ejecución\n\n## Opciones de implementación\n\n* Extensión del editor de Unity: una extensión del editor que persiste más allá de los ciclos de ejecución del juego\n* Contenedor Docker: una versión en contenedor que se comunica con Unity a través de la red\n* Paquete NPX: un paquete Node.js que se puede instalar y ejecutar a través de NPX\n\n## Documentación\n\n* [Arquitectura MCP](docs/mcp-architecture.md) : descripción general de la arquitectura y los espacios de nombres de MCP\n* [Cliente MCP STDIO](docs/mcp-stdio-client.md) : información sobre el cliente MCP STDIO y sus capacidades de registro\n* [Herramienta de consulta](docs/query-tool.md) : información detallada sobre la herramienta de consulta y cómo funciona\n* [Integración con AILogger](docs/ai-logger-sdk.md) : información detallada sobre la integración con AILogger\n* [Referencia de API](docs/api-reference.md) : información detallada sobre los puntos finales de la API\n* [Guía de instalación](docs/installation.md) : instrucciones paso a paso para instalar y configurar Unity-MCP\n* [Guía de desarrollo](docs/development.md) : información sobre el entorno de desarrollo y el flujo de trabajo\n* [Guía de recarga en caliente](docs/hot-reloading.md) : información detallada sobre la recarga en caliente en el entorno de desarrollo\n* [Referencia rápida de recarga en caliente](docs/hot-reloading-quick-reference.md) : Guía de referencia rápida para comandos y consejos de recarga en caliente\n* [Guía de contribución](docs/contributing-guide.md) : Pautas para contribuir al proyecto\n\n## Empezando\n\nPara comenzar a utilizar Unity-MCP, siga estos pasos:\n\n1. Clonar el repositorio:\n\n ```bash\n git clone https://github.com/TSavo/Unity-MCP.git\n cd Unity-MCP\n ```\n\n2. Instalar dependencias:\n\n ```bash\n npm install\n ```\n\n3. Construir el proyecto:\n\n ```bash\n npm run build\n ```\n\n4. Inicie el cliente MCP STDIO:\n\n ```bash\n npm start\n ```\n\n Esto iniciará el cliente MCP STDIO que se comunica con Unity y utiliza AILogger para la persistencia.\n\n Nota: asegúrese de que AILogger se esté ejecutando en o configure la variable de entorno AI\\_LOGGER\\_URL para que apunte a su instancia de AILogger.\n\n5. Ejecutar pruebas:\n\n ```bash\n # Run all tests\n npm test\n\n # Run only unit tests\n npm run test:unit\n\n # Run only e2e tests\n npm run test:e2e\n\n # Run tests with a specific pattern\n npm test -- --testNamePattern=\"should return the server manifest\"\n npm run test:unit -- --testNamePattern=\"should return the server manifest\"\n npm run test:e2e -- --testNamePattern=\"should discover the test server\"\n ```\n\nPara obtener instrucciones más detalladas, consulte la [Guía de instalación](docs/installation.md) .\n\n## Conexión con asistentes de IA\n\nPara conectar el puente Unity-MCP a un asistente de IA, debe crear un archivo de configuración MCP:\n\n```json\n{\n \"mcpServers\": {\n \"unity-ai-bridge\": {\n \"url\": \"http://localhost:8080/sse\"\n }\n }\n}\n```\n\nColoque este archivo en la ubicación adecuada para su asistente de IA. En el caso de Claude, normalmente se encuentra en el directorio de configuración de la aplicación Claude Desktop.\n\n### Herramientas disponibles\n\nEl puente Unity-MCP proporciona las siguientes herramientas:\n\n1. **execute\\_code** : ejecuta código C# directamente en Unity.\n2. **consulta** : ejecuta una consulta utilizando la notación de puntos para acceder a objetos, propiedades y métodos.\n3. **get\\_logs** : recupera registros de AILogger.\n4. **get\\_log\\_by\\_name** : recupera un registro específico de AILogger.\n\n## Ejemplos de uso\n\n### Ejecución de código en Unity\n\nPuedes ejecutar código C# en Unity con la herramienta `execute_code` . El código se ejecutará en el entorno de ejecución de Unity y el resultado se almacenará en AILogger para su posterior recuperación.\n\n#### Solicitud JSON-RPC\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"execute_code\",\n \"arguments\": {\n \"code\": \"Debug.Log(\\\"Hello from Unity!\\\"); return GameObject.FindObjectsOfType().Length;\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### Respuesta JSON-RPC\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-execute-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123}}\"\n }\n ]\n }\n}\n```\n\n### Consulta de objetos de Unity\n\nPuedes consultar objetos de Unity con la herramienta `query` . Esto te permite acceder a objetos, propiedades y métodos mediante notación de puntos.\n\n#### Solicitud JSON-RPC\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"query\",\n \"arguments\": {\n \"query\": \"Camera.main.transform.position\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### Respuesta JSON-RPC\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-query-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":{\\\"x\\\":0,\\\"y\\\":1,\\\"z\\\":-10},\\\"executionTime\\\":45}}\"\n }\n ]\n }\n}\n```\n\n### Recuperación de resultados de AILogger\n\nPuede recuperar los resultados de operaciones anteriores de AILogger utilizando la herramienta `get_log_by_name` .\n\n#### Solicitud JSON-RPC\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"get_log_by_name\",\n \"arguments\": {\n \"log_name\": \"unity-execute-1712534400000\",\n \"limit\": 1\n }\n }\n}\n```\n\n#### Respuesta JSON-RPC\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"entries\\\":[{\\\"id\\\":\\\"123e4567-e89b-12d3-a456-426614174000\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"data\\\":{\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"}]}\"\n }\n ]\n }\n}\n```\n\n### Ejemplo de uso\n\nUna vez que el asistente de IA tenga acceso a la herramienta Unity, puedes pedirle que realice tareas como:\n\n```\nCan you execute the following C# code in Unity?\n\nGameObject.Find(\"Player\").transform.position = new Vector3(0, 1, 0);\n```\n\n## Licencia\n\nInstituto Tecnológico de Massachusetts (MIT)\n\n## Autor\n\nT Savo ( [@TSavo](https://github.com/TSavo) )","Spanish","es-ES",{"_27":46,"_31":50,"_36":51},{"_29":47,"_31":48,"_33":49},"コード実行、シーン分析、ランタイムデバッグのための標準化されたインターフェースを通じて AI が Unity ゲーム環境と対話できるようにする、Unity と AI アシスタント間のブリッジです。","ユニティ-MCP","# ユニティ-MCP\n\nモデルコンテキストプロトコル (MCP) を使用した Unity と AI アシスタント間のブリッジ。\n\n## 概要\n\nUnity-MCPは、Unityゲーム開発向けのモデルコンテキストプロトコル（Model Context Protocol）のオープンソース実装です。AIアシスタントが標準化されたインターフェースを介してUnityゲーム環境と対話できるようにすることで、AIを活用したゲーム開発、自動テスト、シーン分析、ランタイムデバッグが可能になります。\n\n### 建築\n\n永続性のために AILogger を使用するようにアーキテクチャが簡素化され、別個のサーバーコンポーネントが不要になりました。\n\n```\nAI Assistant <-> Unity-MCP STDIO Client <-> Unity Client <-> AILogger\n```\n\n* **AIアシスタント**:MCPプロトコルを使用してUnity-MCP STDIOクライアントと通信します。\n* **Unity-MCP STDIOクライアント**: コマンドをUnityクライアントに転送し、結果をAILoggerに保存します。\n* **Unityクライアント**: Unityでコマンドを実行し、結果を返します\n* **AILogger** : ログと結果を後から取得できるように保存します\n\nUnity-MCP STDIOクライアントは、コード実行とクエリの両方のエンドポイントを提供するUnityクライアントと直接通信します。クエリツールは、クエリを`return`文でラップすることで、コード実行に変換します。\n\n## 特徴\n\n* Unity ランタイム環境で C# コードを実行する\n* ゲームオブジェクトとそのコンポーネントを検査する\n* シーンの階層と構造を分析する\n* テストを実行して結果を受け取る\n* ゲームオブジェクトとコンポーネントのメソッドを呼び出す\n* 実行中にゲームの状態を変更する\n\n## 展開オプション\n\n* Unity エディター拡張機能: ゲーム実行サイクルを超えて存続するエディター拡張機能\n* Dockerコンテナ: ネットワーク経由でUnityと通信するコンテナ化されたバージョン\n* NPX パッケージ: NPX 経由でインストールおよび実行できる Node.js パッケージ\n\n## ドキュメント\n\n* [MCP アーキテクチャ](docs/mcp-architecture.md): MCP アーキテクチャと名前空間の概要\n* [MCP STDIO クライアント](docs/mcp-stdio-client.md): MCP STDIO クライアントとそのログ機能に関する情報\n* [クエリツール](docs/query-tool.md): クエリツールとその仕組みに関する詳細情報\n* [AILogger 統合](docs/ai-logger-sdk.md): AILogger 統合の詳細情報\n* [APIリファレンス](docs/api-reference.md): APIエンドポイントの詳細情報\n* [インストールガイド](docs/installation.md): Unity-MCP のインストールと設定の手順\n* [開発ガイド](docs/development.md): 開発環境とワークフローに関する情報\n* [ホットリロードガイド](docs/hot-reloading.md):開発環境でのホットリロードに関する詳細情報\n* [ホットリロードクイックリファレンス](docs/hot-reloading-quick-reference.md)：ホットリロードコマンドとヒントのクイックリファレンスガイド\n* [貢献ガイド](docs/contributing-guide.md): プロジェクトへの貢献に関するガイドライン\n\n## はじめる\n\nUnity-MCP を使い始めるには、次の手順に従います。\n\n1. リポジトリをクローンします。\n\n ```bash\n git clone https://github.com/TSavo/Unity-MCP.git\n cd Unity-MCP\n ```\n\n2. 依存関係をインストールします:\n\n ```bash\n npm install\n ```\n\n3. プロジェクトをビルドします。\n\n ```bash\n npm run build\n ```\n\n4. MCP STDIO クライアントを起動します。\n\n ```bash\n npm start\n ```\n\n これにより、Unity と通信し、永続性のために AILogger を使用する MCP STDIO クライアントが起動します。\n\n 注意: AILogger がで実行されていることを確認するか、AI\\_LOGGER\\_URL 環境変数を AILogger インスタンスを指すように設定してください。\n\n5. テストを実行します:\n\n ```bash\n # Run all tests\n npm test\n\n # Run only unit tests\n npm run test:unit\n\n # Run only e2e tests\n npm run test:e2e\n\n # Run tests with a specific pattern\n npm test -- --testNamePattern=\"should return the server manifest\"\n npm run test:unit -- --testNamePattern=\"should return the server manifest\"\n npm run test:e2e -- --testNamePattern=\"should discover the test server\"\n ```\n\n詳細な手順については、[インストールガイド](docs/installation.md)を参照してください。\n\n## AIアシスタントへの接続\n\nUnity-MCP ブリッジを AI アシスタントに接続するには、MCP 構成ファイルを作成する必要があります。\n\n```json\n{\n \"mcpServers\": {\n \"unity-ai-bridge\": {\n \"url\": \"http://localhost:8080/sse\"\n }\n }\n}\n```\n\nこのファイルをAIアシスタントの適切な場所に配置してください。Claudeの場合、通常はClaudeデスクトップアプリの設定ディレクトリ内にあります。\n\n### 利用可能なツール\n\nUnity-MCP ブリッジは次のツールを提供します。\n\n1. **execute\\_code** : Unity で C# コードを直接実行します。\n2. **query** : ドット表記を使用してクエリを実行し、オブジェクト、プロパティ、メソッドにアクセスします。\n3. **get\\_logs** : AILogger からログを取得します。\n4. **get\\_log\\_by\\_name** : AILogger から特定のログを取得します。\n\n## 使用例\n\n### Unityでコードを実行する\n\n`execute_code`ツールを使用して、Unity で C# コードを実行できます。コードは Unity ランタイム環境で実行され、結果は AILogger に保存され、後で取得できます。\n\n#### JSON-RPCリクエスト\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"execute_code\",\n \"arguments\": {\n \"code\": \"Debug.Log(\\\"Hello from Unity!\\\"); return GameObject.FindObjectsOfType().Length;\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### JSON-RPCレスポンス\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-execute-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123}}\"\n }\n ]\n }\n}\n```\n\n### Unityオブジェクトのクエリ\n\n`query`ツールを使用してUnityオブジェクトをクエリできます。これにより、ドット表記を使用してオブジェクト、プロパティ、メソッドにアクセスできます。\n\n#### JSON-RPCリクエスト\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"query\",\n \"arguments\": {\n \"query\": \"Camera.main.transform.position\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### JSON-RPCレスポンス\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-query-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":{\\\"x\\\":0,\\\"y\\\":1,\\\"z\\\":-10},\\\"executionTime\\\":45}}\"\n }\n ]\n }\n}\n```\n\n### AILoggerから結果を取得する\n\n`get_log_by_name`ツールを使用して、AILogger から以前の操作の結果を取得できます。\n\n#### JSON-RPCリクエスト\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"get_log_by_name\",\n \"arguments\": {\n \"log_name\": \"unity-execute-1712534400000\",\n \"limit\": 1\n }\n }\n}\n```\n\n#### JSON-RPCレスポンス\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"entries\\\":[{\\\"id\\\":\\\"123e4567-e89b-12d3-a456-426614174000\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"data\\\":{\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"}]}\"\n }\n ]\n }\n}\n```\n\n### 使用例\n\nAI アシスタントが Unity ツールにアクセスできるようになると、次のようなタスクを実行するように依頼できます。\n\n```\nCan you execute the following C# code in Unity?\n\nGameObject.Find(\"Player\").transform.position = new Vector3(0, 1, 0);\n```\n\n## ライセンス\n\nマサチューセッツ工科大学\n\n## 著者\n\nTサボ（ [@TSavo](https://github.com/TSavo) ）","Japanese","ja-JP",{"_27":53,"_31":57,"_36":58},{"_29":54,"_31":55,"_33":56},"Unity와 AI 어시스턴트를 연결하는 브리지로, 코드 실행, 장면 분석, 런타임 디버깅을 위한 표준화된 인터페이스를 통해 AI가 Unity 게임 환경과 상호 작용할 수 있도록 해줍니다.","유니티-MCP","# 유니티-MCP\n\nMCP(Model Context Protocol)를 사용하여 Unity와 AI 어시스턴트를 연결합니다.\n\n## 개요\n\nUnity-MCP는 Unity 게임 개발을 위한 모델 컨텍스트 프로토콜(Model Context Protocol)의 오픈 소스 구현입니다. AI 어시스턴트가 표준화된 인터페이스를 통해 Unity 게임 환경과 상호 작용할 수 있도록 지원하여 AI 지원 게임 개발, 자동 테스트, 장면 분석 및 런타임 디버깅을 가능하게 합니다.\n\n### 건축학\n\nAILogger를 사용하여 지속성을 유지하도록 아키텍처가 간소화되어 별도의 서버 구성 요소가 필요 없게 되었습니다.\n\n지엑스피1\n\n* **AI Assistant** : MCP 프로토콜을 사용하여 Unity-MCP STDIO 클라이언트와 통신합니다.\n* **Unity-MCP STDIO 클라이언트** : Unity 클라이언트에 명령을 전달하고 결과를 AILogger에 저장합니다.\n* **Unity 클라이언트** : Unity에서 명령을 실행하고 결과를 반환합니다.\n* **AILogger** : 나중에 검색할 수 있도록 로그와 결과를 저장합니다.\n\nUnity-MCP STDIO 클라이언트는 코드 실행과 쿼리 모두에 대한 엔드포인트를 제공하는 Unity 클라이언트와 직접 통신합니다. 쿼리 도구는 쿼리를 `return` 문으로 래핑하여 코드 실행으로 변환합니다.\n\n## 특징\n\n* Unity 런타임 환경에서 C# 코드 실행\n* 게임 객체와 그 구성 요소를 검사합니다\n* 장면 계층 구조 및 구조 분석\n* 테스트를 실행하고 결과를 받습니다.\n* 게임 객체 및 구성 요소에 대한 메서드 호출\n* 런타임 중 게임 상태 수정\n\n## 배포 옵션\n\n* Unity Editor 확장: 게임 실행 주기를 넘어 지속되는 Editor 확장\n* Docker 컨테이너: 네트워크를 통해 Unity와 통신하는 컨테이너화된 버전\n* NPX 패키지: NPX를 통해 설치 및 실행할 수 있는 Node.js 패키지\n\n## 선적 서류 비치\n\n* [MCP 아키텍처](docs/mcp-architecture.md) : MCP 아키텍처 및 네임스페이스 개요\n* [MCP STDIO 클라이언트](docs/mcp-stdio-client.md) : MCP STDIO 클라이언트 및 로깅 기능에 대한 정보\n* [쿼리 도구](docs/query-tool.md) : 쿼리 도구와 그 작동 방식에 대한 자세한 정보\n* [AILogger 통합](docs/ai-logger-sdk.md) : AILogger 통합에 대한 자세한 정보\n* [API 참조](docs/api-reference.md) : API 엔드포인트에 대한 자세한 정보\n* [설치 가이드](docs/installation.md) : Unity-MCP 설치 및 설정을 위한 단계별 지침\n* [개발 가이드](docs/development.md) : 개발 환경 및 워크플로에 대한 정보\n* [핫 리로딩 가이드](docs/hot-reloading.md) : 개발 환경에서 핫 리로딩에 대한 자세한 정보\n* [핫 리로딩 빠른 참조](docs/hot-reloading-quick-reference.md) : 핫 리로딩 명령 및 팁에 대한 빠른 참조 가이드\n* [기여 가이드](docs/contributing-guide.md) : 프로젝트 기여를 위한 가이드라인\n\n## 시작하기\n\nUnity-MCP를 시작하려면 다음 단계를 따르세요.\n\n1. 저장소를 복제합니다.\n\n ```bash\n git clone https://github.com/TSavo/Unity-MCP.git\n cd Unity-MCP\n ```\n\n2. 종속성 설치:\n\n ```bash\n npm install\n ```\n\n3. 프로젝트를 빌드하세요:\n\n ```bash\n npm run build\n ```\n\n4. MCP STDIO 클라이언트를 시작합니다.\n\n ```bash\n npm start\n ```\n\n 이렇게 하면 Unity와 통신하고 지속성을 위해 AILogger를 사용하는 MCP STDIO 클라이언트가 시작됩니다.\n\n 참고: AILogger가 에서 실행 중인지 확인하거나 AI\\_LOGGER\\_URL 환경 변수를 설정하여 AILogger 인스턴스를 가리키도록 하세요.\n\n5. 테스트 실행:\n\n ```bash\n # Run all tests\n npm test\n\n # Run only unit tests\n npm run test:unit\n\n # Run only e2e tests\n npm run test:e2e\n\n # Run tests with a specific pattern\n npm test -- --testNamePattern=\"should return the server manifest\"\n npm run test:unit -- --testNamePattern=\"should return the server manifest\"\n npm run test:e2e -- --testNamePattern=\"should discover the test server\"\n ```\n\n자세한 지침은 [설치 가이드를](docs/installation.md) 참조하세요.\n\n## AI 어시스턴트에 연결\n\nUnity-MCP 브리지를 AI 어시스턴트에 연결하려면 MCP 구성 파일을 만들어야 합니다.\n\n```json\n{\n \"mcpServers\": {\n \"unity-ai-bridge\": {\n \"url\": \"http://localhost:8080/sse\"\n }\n }\n}\n```\n\n이 파일을 AI 비서의 적절한 위치에 저장하세요. Claude의 경우, 일반적으로 Claude Desktop 앱의 구성 디렉터리에 저장됩니다.\n\n### 사용 가능한 도구\n\nUnity-MCP 브리지는 다음과 같은 도구를 제공합니다.\n\n1. **execute\\_code** : Unity에서 C# 코드를 직접 실행합니다.\n2. **쿼리** : 점 표기법을 사용하여 쿼리를 실행하여 객체, 속성, 메서드에 액세스합니다.\n3. **get\\_logs** : AILogger에서 로그를 검색합니다.\n4. **get\\_log\\_by\\_name** : AILogger에서 특정 로그를 검색합니다.\n\n## 사용 예\n\n### Unity에서 코드 실행\n\nUnity에서 `execute_code` 도구를 사용하여 C# 코드를 실행할 수 있습니다. 코드는 Unity 런타임 환경에서 실행되며, 결과는 나중에 검색할 수 있도록 AILogger에 저장됩니다.\n\n#### JSON-RPC 요청\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"execute_code\",\n \"arguments\": {\n \"code\": \"Debug.Log(\\\"Hello from Unity!\\\"); return GameObject.FindObjectsOfType().Length;\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### JSON-RPC 응답\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-execute-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123}}\"\n }\n ]\n }\n}\n```\n\n### Unity 객체 쿼리\n\n`query` 도구를 사용하여 Unity 객체를 쿼리할 수 있습니다. 이를 통해 점 표기법을 사용하여 객체, 속성 및 메서드에 액세스할 수 있습니다.\n\n#### JSON-RPC 요청\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"query\",\n \"arguments\": {\n \"query\": \"Camera.main.transform.position\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### JSON-RPC 응답\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-query-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":{\\\"x\\\":0,\\\"y\\\":1,\\\"z\\\":-10},\\\"executionTime\\\":45}}\"\n }\n ]\n }\n}\n```\n\n### AILogger에서 결과 검색\n\n`get_log_by_name` 도구를 사용하면 AILogger에서 이전 작업의 결과를 검색할 수 있습니다.\n\n#### JSON-RPC 요청\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"get_log_by_name\",\n \"arguments\": {\n \"log_name\": \"unity-execute-1712534400000\",\n \"limit\": 1\n }\n }\n}\n```\n\n#### JSON-RPC 응답\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"entries\\\":[{\\\"id\\\":\\\"123e4567-e89b-12d3-a456-426614174000\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"data\\\":{\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"}]}\"\n }\n ]\n }\n}\n```\n\n### 사용 예\n\nAI 어시스턴트가 Unity 도구에 액세스하면 다음과 같은 작업을 수행하도록 요청할 수 있습니다.\n\n```\nCan you execute the following C# code in Unity?\n\nGameObject.Find(\"Player\").transform.position = new Vector3(0, 1, 0);\n```\n\n## 특허\n\nMIT\n\n## 작가\n\nT 사보( [@TSavo](https://github.com/TSavo) )","Korean","ko-KR",{"_27":60,"_31":63,"_36":64},{"_29":61,"_31":32,"_33":62},"Unity 和 AI 助手之间的桥梁，使 AI 能够通过标准化接口与 Unity 游戏环境进行交互，以执行代码、场景分析和运行时调试。","# Unity-MCP\n\n使用模型上下文协议 (MCP) 在 Unity 和 AI 助手之间建立桥梁。\n\n## 概述\n\nUnity-MCP 是 Unity 游戏开发的模型上下文协议 (MCP) 的开源实现。它使 AI 助手能够通过标准化接口与 Unity 游戏环境进行交互，从而实现 AI 辅助游戏开发、自动化测试、场景分析和运行时调试。\n\n### 建筑学\n\n该架构已简化为使用 AILogger 进行持久化，从而无需单独的服务器组件：\n\n```\nAI Assistant <-> Unity-MCP STDIO Client <-> Unity Client <-> AILogger\n```\n\n* **AI 助手**：使用 MCP 协议与 Unity-MCP STDIO 客户端进行通信\n* **Unity-MCP STDIO 客户端**：将命令转发到 Unity 客户端并将结果存储在 AILogger 中\n* **Unity Client** ：在Unity中执行命令并返回结果\n* **AILogger** ：存储日志和结果以供日后检索\n\nUnity-MCP STDIO 客户端直接与 Unity 客户端通信，后者提供代码执行和查询的端点。查询工具通过将查询包装在`return`语句中，将其转换为代码执行。\n\n## 特征\n\n* 在 Unity 运行时环境中执行 C# 代码\n* 检查游戏对象及其组件\n* 分析场景层次和结构\n* 运行测试并接收结果\n* 调用游戏对象和组件的方法\n* 在运行时修改游戏状态\n\n## 部署选项\n\n* Unity 编辑器扩展：一个在游戏执行周期之外持续存在的编辑器扩展\n* Docker 容器：通过网络与 Unity 通信的容器化版本\n* NPX 包：可以通过 NPX 安装和运行的 Node.js 包\n\n## 文档\n\n* [MCP 架构](docs/mcp-architecture.md)：MCP 架构和命名空间概述\n* [MCP STDIO 客户端](docs/mcp-stdio-client.md)：有关 MCP STDIO 客户端及其日志记录功能的信息\n* [查询工具](docs/query-tool.md)：有关查询工具及其工作原理的详细信息\n* [AILogger 集成](docs/ai-logger-sdk.md)：有关 AILogger 集成的详细信息\n* [API 参考](docs/api-reference.md)：有关 API 端点的详细信息\n* [安装指南](docs/installation.md)：安装和设置 Unity-MCP 的分步说明\n* [开发指南](docs/development.md)：有关开发环境和工作流程的信息\n* [热重载指南](docs/hot-reloading.md)：关于开发环境中热重载的详细信息\n* [热重载快速参考](docs/hot-reloading-quick-reference.md)：热重载命令和技巧的快速参考指南\n* [贡献指南](docs/contributing-guide.md)：为项目做出贡献的指南\n\n## 入门\n\n要开始使用 Unity-MCP，请按照以下步骤操作：\n\n1. 克隆存储库：\n\n ```bash\n git clone https://github.com/TSavo/Unity-MCP.git\n cd Unity-MCP\n ```\n\n2. 安装依赖项：\n\n ```bash\n npm install\n ```\n\n3. 构建项目：\n\n ```bash\n npm run build\n ```\n\n4. 启动 MCP STDIO 客户端：\n\n ```bash\n npm start\n ```\n\n 这将启动与 Unity 通信并使用 AILogger 进行持久化的 MCP STDIO 客户端。\n\n 注意：确保 AILogger 在上运行或设置 AI\\_LOGGER\\_URL 环境变量以指向您的 AILogger 实例。\n\n5. 运行测试：\n\n ```bash\n # Run all tests\n npm test\n\n # Run only unit tests\n npm run test:unit\n\n # Run only e2e tests\n npm run test:e2e\n\n # Run tests with a specific pattern\n npm test -- --testNamePattern=\"should return the server manifest\"\n npm run test:unit -- --testNamePattern=\"should return the server manifest\"\n npm run test:e2e -- --testNamePattern=\"should discover the test server\"\n ```\n\n有关更详细的说明，请参阅[安装指南](docs/installation.md)。\n\n## 连接AI助手\n\n要将 Unity-MCP 桥接到 AI 助手，您需要创建一个 MCP 配置文件：\n\n```json\n{\n \"mcpServers\": {\n \"unity-ai-bridge\": {\n \"url\": \"http://localhost:8080/sse\"\n }\n }\n}\n```\n\n将此文件放置在适合您的AI助手的位置。对于Claude，通常位于Claude桌面应用程序的配置目录中。\n\n### 可用工具\n\nUnity-MCP 桥提供以下工具：\n\n1. **execute\\_code** ：在 Unity 中直接执行 C# 代码。\n2. **query** ：使用点符号执行查询来访问对象、属性和方法。\n3. **get\\_logs** ：从 AILogger 检索日志。\n4. **get\\_log\\_by\\_name** ：从 AILogger 检索特定日志。\n\n## 使用示例\n\n### 在 Unity 中执行代码\n\n您可以使用`execute_code`工具在Unity中执行C#代码。该代码将在Unity运行时环境中执行，并将结果存储在AILogger中以供后续检索。\n\n#### JSON-RPC 请求\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"execute_code\",\n \"arguments\": {\n \"code\": \"Debug.Log(\\\"Hello from Unity!\\\"); return GameObject.FindObjectsOfType().Length;\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### JSON-RPC 响应\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-execute-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123}}\"\n }\n ]\n }\n}\n```\n\n### 查询 Unity 对象\n\n您可以使用`query`工具查询 Unity 对象。这允许您使用点符号访问对象、属性和方法。\n\n#### JSON-RPC 请求\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"query\",\n \"arguments\": {\n \"query\": \"Camera.main.transform.position\",\n \"timeout\": 5000\n }\n }\n}\n```\n\n#### JSON-RPC 响应\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 2,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"logName\\\":\\\"unity-query-1712534400000\\\",\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":{\\\"x\\\":0,\\\"y\\\":1,\\\"z\\\":-10},\\\"executionTime\\\":45}}\"\n }\n ]\n }\n}\n```\n\n### 从 AILogger 检索结果\n\n您可以使用`get_log_by_name`工具从 AILogger 检索以前操作的结果。\n\n#### JSON-RPC 请求\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"get_log_by_name\",\n \"arguments\": {\n \"log_name\": \"unity-execute-1712534400000\",\n \"limit\": 1\n }\n }\n}\n```\n\n#### JSON-RPC 响应\n\n```json\n{\n \"jsonrpc\": \"2.0\",\n \"id\": 3,\n \"result\": {\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"{\\\"status\\\":\\\"success\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"entries\\\":[{\\\"id\\\":\\\"123e4567-e89b-12d3-a456-426614174000\\\",\\\"name\\\":\\\"unity-execute-1712534400000\\\",\\\"data\\\":{\\\"result\\\":{\\\"success\\\":true,\\\"result\\\":42,\\\"logs\\\":[\\\"Hello from Unity!\\\"],\\\"executionTime\\\":123},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"},\\\"timestamp\\\":\\\"2025-04-08T00:00:00.000Z\\\"}]}\"\n }\n ]\n }\n}\n```\n\n### 示例用法\n\n一旦 AI 助手可以访问 Unity 工具，您就可以要求它执行以下任务：\n\n```\nCan you execute the following C# code in Unity?\n\nGameObject.Find(\"Player\").transform.position = new Vector3(0, 1, 0);\n```\n\n## 执照\n\n麻省理工学院\n\n## 作者\n\nT Savo（ [@TSavo](https://github.com/TSavo) 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