[{"_1":2,"_345":-5,"_346":-5},"loaderData",{"_3":4,"_29":30,"_31":32,"_259":260},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","0196b39e-3e5f-7610-b57e-9f3da2fd097e","title","This MCP server does not support inspection","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":24,"_25":-5,"_26":27,"_28":-5},"attributes",[22],"BOT","id",6155836,"membership","referenceId","0196b39e-3e44-76a1-aca7-9ecfc65d76bc","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_33":34,"_75":-5,"_76":35,"_36":77,"_178":179,"_258":144},"availableLocales",[35,47,54,61,68],{"_36":37,"_40":44,"_45":46},"mcpServer",{"_38":39,"_40":41,"_42":43},"description","Integrates ElevenLabs Text-to-Speech capabilities with Cursor through the Model Context Protocol, allowing users to convert text to speech with selectable voices within the Cursor editor.","name","ElevenLabs Text-to-Speech MCP","readme","# Project Jessica (ElevenLabs TTS MCP)\n\nThis project integrates ElevenLabs Text-to-Speech capabilities with Cursor through the Model Context Protocol (MCP). It consists of a FastAPI backend service and a React frontend application.\n\n## Features\n\n- Text-to-Speech conversion using ElevenLabs API\n- Voice selection and management\n- MCP integration for Cursor\n- Modern React frontend interface\n- WebSocket real-time communication\n- Pre-commit hooks for code quality\n- Automatic code formatting and linting\n\n## Project Structure\n\n```\njessica/\n├── src/\n│ ├── backend/ # FastAPI backend service\n│ └── frontend/ # React frontend application\n├── terraform/ # Infrastructure as Code\n├── tests/ # Test suites\n└── docs/ # Documentation\n```\n\n## Requirements\n\n- Python 3.11+\n- Poetry (for backend dependency management)\n- Node.js 18+ (for frontend)\n- Cursor (for MCP integration)\n\n## Local Development Setup\n\n### Backend Setup\n\n```bash\n# Clone the repository\ngit clone https://github.com/georgi-io/jessica.git\ncd jessica\n\n# Create Python virtual environment\npython -m venv .venv\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Install backend dependencies\npoetry install\n\n# Configure environment\ncp .env.example .env\n# Edit .env with your ElevenLabs API key\n\n# Install pre-commit hooks\npoetry run pre-commit install\n```\n\n### Frontend Setup\n\n```bash\n# Navigate to frontend directory\ncd src/frontend\n\n# Install dependencies\nnpm install\n```\n\n## Development Servers\n\n### Starting the Backend\n\n```bash\n# Activate virtual environment if not active\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Start the backend\npython -m src.backend\n```\n\nThe backend provides:\n- REST API: http://localhost:9020\n- WebSocket: ws://localhost:9020/ws\n- MCP Server: http://localhost:9020/sse (integrated with the main API server)\n\n### Starting the Frontend\n\n```bash\n# In src/frontend directory\nnpm run dev\n```\n\nFrontend development server:\n- http://localhost:5173\n\n## Environment Configuration\n\n### Backend (.env)\n```env\n# ElevenLabs API\nELEVENLABS_API_KEY=your-api-key\n\n# Server Configuration\nHOST=127.0.0.1\nPORT=9020\n\n# Development Settings\nDEBUG=false\nRELOAD=true\n```\n\n### Frontend (.env)\n```env\nVITE_API_URL=http://localhost:9020\nVITE_WS_URL=ws://localhost:9020/ws\n```\n\n## Code Quality Tools\n\n### Backend\n\n```bash\n# Run all pre-commit hooks\npoetry run pre-commit run --all-files\n\n# Run specific tools\npoetry run ruff check .\npoetry run ruff format .\npoetry run pytest\n```\n\n### Frontend\n\n```bash\n# Lint\nnpm run lint\n\n# Type check\nnpm run type-check\n\n# Test\nnpm run test\n```\n\n## Production Deployment\n\n### AWS ECR and GitHub Actions Setup\n\nTo enable automatic building and pushing of Docker images to Amazon ECR:\n\n1. Apply the Terraform configuration to create the required AWS resources:\n ```bash\n cd terraform\n terraform init\n terraform apply\n ```\n\n2. The GitHub Actions workflow will automatically:\n - Read the necessary configuration from the Terraform state in S3\n - Build the Docker image on pushes to `main` or `develop` branches\n - Push the image to ECR with tags for `latest` and the specific commit SHA\n\n3. No additional repository variables needed! The workflow fetches all required configuration from the Terraform state.\n\n### How it Works\n\nThe GitHub Actions workflow is configured to:\n1. Initially assume a predefined IAM role with S3 read permissions\n2. Fetch and extract configuration values from the Terraform state file in S3\n3. Re-authenticate using the actual deployment role from the state file\n4. Build and push the Docker image to the ECR repository defined in the state\n\nThis approach eliminates the need to manually configure GitHub repository variables and ensures that the CI/CD process always uses the current infrastructure configuration.\n\n### Quick Overview\n\n- Frontend: Served from S3 via CloudFront at jessica.georgi.io\n- Backend API: Available at api.georgi.io/jessica\n- WebSocket: Connects to api.georgi.io/jessica/ws\n- Docker Image: Stored in AWS ECR and can be deployed to ECS/EKS\n- Infrastructure: Managed via Terraform in this repository\n\n## MCP Integration with Cursor\n\n1. Start the backend server\n2. In Cursor settings, add new MCP server:\n - Name: Jessica TTS\n - Type: SSE\n - URL: http://localhost:9020/sse\n\n## Troubleshooting\n\n### Common Issues\n\n1. **API Key Issues**\n - Error: \"Invalid API key\"\n - Solution: Check `.env` file\n\n2. **Connection Problems**\n - Error: \"Cannot connect to MCP server\"\n - Solution: Verify backend is running and ports are correct\n\n3. **Port Conflicts**\n - Error: \"Address already in use\"\n - Solution: Change ports in `.env`\n\n4. **WebSocket Connection Failed**\n - Error: \"WebSocket connection failed\"\n - Solution: Ensure backend is running and WebSocket URL is correct\n\nFor additional help, please open an issue on GitHub.\n\n## License\n\nMIT \n","English","tag","en-US",{"_36":48,"_40":52,"_45":53},{"_38":49,"_40":50,"_42":51},"Integra las capacidades de texto a voz de ElevenLabs con Cursor a través del Protocolo de contexto de modelo, lo que permite a los usuarios convertir texto a voz con voces seleccionables dentro del editor de Cursor.","ElevenLabs MCP de texto a voz","# Proyecto Jessica (ElevenLabs TTS MCP)\n\nEste proyecto integra las capacidades de texto a voz de ElevenLabs con Cursor mediante el Protocolo de Contexto de Modelo (MCP). Consta de un servicio de backend FastAPI y una aplicación frontend React.\n\n## Características\n\n* Conversión de texto a voz mediante la API de ElevenLabs\n* Selección y gestión de voces\n* Integración de MCP para Cursor\n* Interfaz frontend moderna de React\n* Comunicación en tiempo real mediante WebSocket\n* Ganchos de pre-confirmación para la calidad del código\n* Formateo y pelusa de código automático\n\n## Estructura del proyecto\n\n```\njessica/\n├── src/\n│ ├── backend/ # FastAPI backend service\n│ └── frontend/ # React frontend application\n├── terraform/ # Infrastructure as Code\n├── tests/ # Test suites\n└── docs/ # Documentation\n```\n\n## Requisitos\n\n* Python 3.11+\n* Poesía (para la gestión de dependencias del backend)\n* Node.js 18+ (para frontend)\n* Cursor (para integración MCP)\n\n## Configuración de desarrollo local\n\n### Configuración del backend\n\n```bash\n# Clone the repository\ngit clone https://github.com/georgi-io/jessica.git\ncd jessica\n\n# Create Python virtual environment\npython -m venv .venv\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Install backend dependencies\npoetry install\n\n# Configure environment\ncp .env.example .env\n# Edit .env with your ElevenLabs API key\n\n# Install pre-commit hooks\npoetry run pre-commit install\n```\n\n### Configuración de la interfaz\n\n```bash\n# Navigate to frontend directory\ncd src/frontend\n\n# Install dependencies\nnpm install\n```\n\n## Servidores de desarrollo\n\n### Iniciando el backend\n\n```bash\n# Activate virtual environment if not active\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Start the backend\npython -m src.backend\n```\n\nEl backend proporciona:\n\n* API REST: \n* WebSocket: ws://localhost:9020/ws\n* Servidor MCP: (integrado con el servidor API principal)\n\n### Iniciando el Frontend\n\n```bash\n# In src/frontend directory\nnpm run dev\n```\n\nServidor de desarrollo frontend:\n\n* \n\n## Configuración del entorno\n\n### Backend (.env)\n\n```env\n# ElevenLabs API\nELEVENLABS_API_KEY=your-api-key\n\n# Server Configuration\nHOST=127.0.0.1\nPORT=9020\n\n# Development Settings\nDEBUG=false\nRELOAD=true\n```\n\n### Interfaz (.env)\n\n```env\nVITE_API_URL=http://localhost:9020\nVITE_WS_URL=ws://localhost:9020/ws\n```\n\n## Herramientas de calidad del código\n\n### Backend\n\n```bash\n# Run all pre-commit hooks\npoetry run pre-commit run --all-files\n\n# Run specific tools\npoetry run ruff check .\npoetry run ruff format .\npoetry run pytest\n```\n\n### Interfaz\n\n```bash\n# Lint\nnpm run lint\n\n# Type check\nnpm run type-check\n\n# Test\nnpm run test\n```\n\n## Despliegue de producción\n\n### Configuración de acciones de AWS ECR y GitHub\n\nPara habilitar la creación y el envío automáticos de imágenes de Docker a Amazon ECR:\n\n1. Aplique la configuración de Terraform para crear los recursos de AWS necesarios:\n\n ```bash\n cd terraform\n terraform init\n terraform apply\n ```\n\n2. El flujo de trabajo de GitHub Actions realizará automáticamente lo siguiente:\n\n * Lea la configuración necesaria del estado de Terraform en S3\n * Construya la imagen de Docker al enviarla a las ramas `main` o `develop`\n * Envíe la imagen a ECR con etiquetas para `latest` SHA y la confirmación específica\n\n3. ¡No se necesitan variables de repositorio adicionales! El flujo de trabajo obtiene toda la configuración necesaria del estado de Terraform.\n\n### Cómo funciona\n\nEl flujo de trabajo de Acciones de GitHub está configurado para:\n\n1. Asuma inicialmente un rol IAM predefinido con permisos de lectura S3\n2. Obtener y extraer valores de configuración del archivo de estado de Terraform en S3\n3. Vuelva a autenticarse utilizando el rol de implementación real del archivo de estado\n4. Construya y envíe la imagen de Docker al repositorio ECR definido en el estado\n\nEste enfoque elimina la necesidad de configurar manualmente las variables del repositorio de GitHub y garantiza que el proceso de CI/CD siempre utilice la configuración de infraestructura actual.\n\n### Descripción rápida\n\n* Frontend: Servido desde S3 a través de CloudFront en jessica.georgi.io\n* API de backend: disponible en api.georgi.io/jessica\n* WebSocket: se conecta a api.georgi.io/jessica/ws\n* Imagen de Docker: se almacena en AWS ECR y se puede implementar en ECS/EKS\n* Infraestructura: administrada a través de Terraform en este repositorio\n\n## Integración de MCP con Cursor\n\n1. Iniciar el servidor backend\n2. En la configuración del cursor, agregue un nuevo servidor MCP:\n * Nombre: Jessica TTS\n * Tipo: SSE\n * URL: \n\n## Solución de problemas\n\n### Problemas comunes\n\n1. **Problemas con la clave API**\n\n * Error: \"Clave API no válida\"\n * Solución: comprobar el archivo `.env`\n\n2. **Problemas de conexión**\n\n * Error: \"No se puede conectar al servidor MCP\"\n * Solución: Verifique que el backend esté ejecutándose y que los puertos sean correctos\n\n3. **Conflictos portuarios**\n\n * Error: \"Dirección ya en uso\"\n * Solución: Cambiar puertos en `.env`\n\n4. **Conexión WebSocket fallida**\n\n * Error: \"Error en la conexión WebSocket\"\n * Solución: asegúrese de que el backend esté ejecutándose y la URL de WebSocket sea correcta\n\nPara obtener ayuda adicional, abra un problema en GitHub.\n\n## Licencia\n\nInstituto Tecnológico de Massachusetts (MIT)","Spanish","es-ES",{"_36":55,"_40":59,"_45":60},{"_38":56,"_40":57,"_42":58},"モデルコンテキストプロトコルを介して ElevenLabs のテキスト読み上げ機能を Cursor と統合し、ユーザーが Cursor エディター内で選択可能な音声を使用してテキストを音声に変換できるようにします。","ElevenLabs テキスト読み上げ MCP","# プロジェクトジェシカ (ElevenLabs TTS MCP)\n\nこのプロジェクトは、ElevenLabsのテキスト読み上げ機能を、モデルコンテキストプロトコル（MCP）を介してCursorに統合します。FastAPIバックエンドサービスとReactフロントエンドアプリケーションで構成されています。\n\n## 特徴\n\n* ElevenLabs APIを使用したテキスト読み上げ変換\n* 音声の選択と管理\n* カーソルのMCP統合\n* モダンなReactフロントエンドインターフェース\n* WebSocketリアルタイム通信\n* コード品質のための事前コミットフック\n* 自動コードフォーマットとリンティング\n\n## プロジェクト構造\n\n```\njessica/\n├── src/\n│ ├── backend/ # FastAPI backend service\n│ └── frontend/ # React frontend application\n├── terraform/ # Infrastructure as Code\n├── tests/ # Test suites\n└── docs/ # Documentation\n```\n\n## 要件\n\n* Python 3.11以上\n* Poetry（バックエンドの依存関係管理用）\n* Node.js 18+（フロントエンド用）\n* カーソル（MCP統合用）\n\n## ローカル開発のセットアップ\n\n### バックエンドのセットアップ\n\n```bash\n# Clone the repository\ngit clone https://github.com/georgi-io/jessica.git\ncd jessica\n\n# Create Python virtual environment\npython -m venv .venv\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Install backend dependencies\npoetry install\n\n# Configure environment\ncp .env.example .env\n# Edit .env with your ElevenLabs API key\n\n# Install pre-commit hooks\npoetry run pre-commit install\n```\n\n### フロントエンドのセットアップ\n\n```bash\n# Navigate to frontend directory\ncd src/frontend\n\n# Install dependencies\nnpm install\n```\n\n## 開発サーバー\n\n### バックエンドの開始\n\n```bash\n# Activate virtual environment if not active\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Start the backend\npython -m src.backend\n```\n\nバックエンドは以下を提供します:\n\n* REST API: \n* WebSocket: ws://localhost:9020/ws\n* MCP サーバー: (メイン API サーバーと統合)\n\n### フロントエンドの開始\n\n```bash\n# In src/frontend directory\nnpm run dev\n```\n\nフロントエンド開発サーバー:\n\n* \n\n## 環境設定\n\n### バックエンド (.env)\n\n```env\n# ElevenLabs API\nELEVENLABS_API_KEY=your-api-key\n\n# Server Configuration\nHOST=127.0.0.1\nPORT=9020\n\n# Development Settings\nDEBUG=false\nRELOAD=true\n```\n\n### フロントエンド (.env)\n\n```env\nVITE_API_URL=http://localhost:9020\nVITE_WS_URL=ws://localhost:9020/ws\n```\n\n## コード品質ツール\n\n### バックエンド\n\n```bash\n# Run all pre-commit hooks\npoetry run pre-commit run --all-files\n\n# Run specific tools\npoetry run ruff check .\npoetry run ruff format .\npoetry run pytest\n```\n\n### フロントエンド\n\n```bash\n# Lint\nnpm run lint\n\n# Type check\nnpm run type-check\n\n# Test\nnpm run test\n```\n\n## 本番環境への展開\n\n### AWS ECRとGitHub Actionsのセットアップ\n\nDocker イメージの自動ビルドと Amazon ECR へのプッシュを有効にするには:\n\n1. 必要な AWS リソースを作成するには、Terraform 構成を適用します。\n\n ```bash\n cd terraform\n terraform init\n terraform apply\n ```\n\n2. GitHub Actions ワークフローは自動的に次の処理を実行します。\n\n * S3のTerraform状態から必要な構成を読み取ります\n * `main`または`develop`ブランチへのプッシュ時にDockerイメージをビルドする\n * `latest`のタグと特定のコミット SHA を付けてイメージを ECR にプッシュします。\n\n3. 追加のリポジトリ変数は必要ありません。ワークフローは、必要なすべての構成を Terraform 状態から取得します。\n\n### 仕組み\n\nGitHub Actions ワークフローは次のように構成されています。\n\n1. 最初にS3の読み取り権限を持つ定義済みのIAMロールを引き受ける\n2. S3 の Terraform 状態ファイルから設定値を取得して抽出する\n3. 状態ファイルから実際の展開ロールを使用して再認証します\n4. Dockerイメージをビルドし、状態で定義されたECRリポジトリにプッシュします。\n\nこのアプローチにより、GitHub リポジトリ変数を手動で構成する必要がなくなり、CI/CD プロセスで常に現在のインフラストラクチャ構成が使用されるようになります。\n\n### 概要\n\n* フロントエンド: jessica.georgi.io の CloudFront 経由で S3 から提供\n* バックエンド API: api.georgi.io/jessica で利用可能\n* WebSocket: api.georgi.io/jessica/ws に接続します\n* Dockerイメージ: AWS ECRに保存され、ECS/EKSにデプロイ可能\n* インフラストラクチャ: このリポジトリでは Terraform 経由で管理されます\n\n## カーソルを使用したMCP統合\n\n1. バックエンドサーバーを起動する\n2. カーソル設定で、新しい MCP サーバーを追加します。\n * 名前: ジェシカ TTS\n * タイプ: SSE\n * URL: \n\n## トラブルシューティング\n\n### よくある問題\n\n1. **APIキーの問題**\n\n * エラー:「無効な API キー」\n * 解決策: `.env`ファイルを確認する\n\n2. **接続の問題**\n\n * エラー:「MCP サーバーに接続できません」\n * 解決策: バックエンドが実行中であり、ポートが正しいことを確認する\n\n3. **ポート競合**\n\n * エラー:「アドレスはすでに使用されています」\n * 解決策: `.env`のポートを変更する\n\n4. **WebSocket接続に失敗しました**\n\n * エラー:「WebSocket 接続に失敗しました」\n * 解決策: バックエンドが実行中であることと、WebSocket URL が正しいことを確認してください\n\nさらにサポートが必要な場合は、GitHub で問題を報告してください。\n\n## ライセンス\n\nマサチューセッツ工科大学","Japanese","ja-JP",{"_36":62,"_40":66,"_45":67},{"_38":63,"_40":64,"_42":65},"모델 컨텍스트 프로토콜을 통해 ElevenLabs 텍스트-음성 변환 기능을 커서와 통합하여 사용자가 커서 편집기 내에서 선택 가능한 음성으로 텍스트를 음성으로 변환할 수 있도록 합니다.","ElevenLabs 텍스트 음성 변환 MCP","# 프로젝트 제시카(ElevenLabs TTS MCP)\n\n이 프로젝트는 모델 컨텍스트 프로토콜(MCP)을 통해 ElevenLabs의 텍스트-음성 변환 기능을 커서와 통합합니다. FastAPI 백엔드 서비스와 React 프런트엔드 애플리케이션으로 구성됩니다.\n\n## 특징\n\n* ElevenLabs API를 사용한 텍스트-음성 변환\n* 음성 선택 및 관리\n* 커서를 위한 MCP 통합\n* 최신 React 프런트엔드 인터페이스\n* WebSocket 실시간 통신\n* 코드 품질을 위한 사전 커밋 후크\n* 자동 코드 포맷팅 및 린팅\n\n## 프로젝트 구조\n\n지엑스피1\n\n## 요구 사항\n\n* 파이썬 3.11+\n* Poetry(백엔드 종속성 관리용)\n* Node.js 18+(프론트엔드용)\n* 커서(MCP 통합용)\n\n## 지역 개발 설정\n\n### 백엔드 설정\n\n```bash\n# Clone the repository\ngit clone https://github.com/georgi-io/jessica.git\ncd jessica\n\n# Create Python virtual environment\npython -m venv .venv\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Install backend dependencies\npoetry install\n\n# Configure environment\ncp .env.example .env\n# Edit .env with your ElevenLabs API key\n\n# Install pre-commit hooks\npoetry run pre-commit install\n```\n\n### 프런트엔드 설정\n\n```bash\n# Navigate to frontend directory\ncd src/frontend\n\n# Install dependencies\nnpm install\n```\n\n## 개발 서버\n\n### 백엔드 시작하기\n\n```bash\n# Activate virtual environment if not active\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Start the backend\npython -m src.backend\n```\n\n백엔드는 다음을 제공합니다.\n\n* REST API: \n* 웹소켓: ws://localhost:9020/ws\n* MCP 서버: (주 API 서버와 통합)\n\n### 프런트엔드 시작하기\n\n```bash\n# In src/frontend directory\nnpm run dev\n```\n\n프런트엔드 개발 서버:\n\n* \n\n## 환경 구성\n\n### 백엔드(.env)\n\n```env\n# ElevenLabs API\nELEVENLABS_API_KEY=your-api-key\n\n# Server Configuration\nHOST=127.0.0.1\nPORT=9020\n\n# Development Settings\nDEBUG=false\nRELOAD=true\n```\n\n### 프런트엔드(.env)\n\n```env\nVITE_API_URL=http://localhost:9020\nVITE_WS_URL=ws://localhost:9020/ws\n```\n\n## 코드 품질 도구\n\n### 백엔드\n\n```bash\n# Run all pre-commit hooks\npoetry run pre-commit run --all-files\n\n# Run specific tools\npoetry run ruff check .\npoetry run ruff format .\npoetry run pytest\n```\n\n### 프런트엔드\n\n```bash\n# Lint\nnpm run lint\n\n# Type check\nnpm run type-check\n\n# Test\nnpm run test\n```\n\n## 프로덕션 배포\n\n### AWS ECR 및 GitHub 작업 설정\n\nAmazon ECR에 Docker 이미지를 자동으로 빌드하고 푸시하는 기능을 활성화하려면:\n\n1. Terraform 구성을 적용하여 필요한 AWS 리소스를 생성합니다.\n\n ```bash\n cd terraform\n terraform init\n terraform apply\n ```\n\n2. GitHub Actions 워크플로는 자동으로 다음을 수행합니다.\n\n * S3의 Terraform 상태에서 필요한 구성을 읽습니다.\n * `main` 또는 `develop` 브랜치에 푸시하여 Docker 이미지를 빌드합니다.\n * `latest` 및 특정 커밋 SHA에 대한 태그와 함께 이미지를 ECR에 푸시합니다.\n\n3. 추가 저장소 변수가 필요하지 않습니다! 워크플로는 Terraform 상태에서 필요한 모든 구성을 가져옵니다.\n\n### 작동 원리\n\nGitHub Actions 워크플로는 다음과 같이 구성됩니다.\n\n1. 처음에는 S3 읽기 권한이 있는 미리 정의된 IAM 역할을 가정합니다.\n2. S3의 Terraform 상태 파일에서 구성 값을 가져오고 추출합니다.\n3. 상태 파일의 실제 배포 역할을 사용하여 다시 인증합니다.\n4. 상태에 정의된 ECR 저장소에 Docker 이미지를 빌드하고 푸시합니다.\n\n이 접근 방식을 사용하면 GitHub 저장소 변수를 수동으로 구성할 필요가 없고 CI/CD 프로세스가 항상 현재 인프라 구성을 사용하게 됩니다.\n\n### 빠른 개요\n\n* 프런트엔드: jessica.georgi.io의 CloudFront를 통해 S3에서 제공됨\n* 백엔드 API: api.georgi.io/jessica에서 사용 가능\n* WebSocket: api.georgi.io/jessica/ws에 연결\n* Docker 이미지: AWS ECR에 저장되며 ECS/EKS에 배포 가능\n* 인프라: 이 저장소의 Terraform을 통해 관리됨\n\n## 커서와 MCP 통합\n\n1. 백엔드 서버를 시작합니다\n2. 커서 설정에서 새 MCP 서버를 추가합니다.\n * 이름: 제시카 TTS\n * 유형: SSE\n * URL: \n\n## 문제 해결\n\n### 일반적인 문제\n\n1. **API 키 문제**\n\n * 오류: \"잘못된 API 키\"\n * 해결 방법: `.env` 파일을 확인하세요\n\n2. **연결 문제**\n\n * 오류: \"MCP 서버에 연결할 수 없습니다\"\n * 해결 방법: 백엔드가 실행 중이고 포트가 올바른지 확인하세요.\n\n3. **항구 갈등**\n\n * 오류: \"주소가 이미 사용 중입니다\"\n * 해결 방법: `.env` 에서 포트를 변경하세요.\n\n4. **WebSocket 연결에 실패했습니다**\n\n * 오류: \"WebSocket 연결에 실패했습니다\"\n * 해결 방법: 백엔드가 실행 중이고 WebSocket URL이 올바른지 확인하세요.\n\n추가 도움이 필요하면 GitHub에서 이슈를 열어주세요.\n\n## 특허\n\nMIT","Korean","ko-KR",{"_36":69,"_40":73,"_45":74},{"_38":70,"_40":71,"_42":72},"通过模型上下文协议将 ElevenLabs 文本转语音功能与 Cursor 集成，允许用户在 Cursor 编辑器中将文本转换为可选择声音的语音。","ElevenLabs 文本转语音 MCP","# 杰西卡项目（ElevenLabs TTS MCP）\n\n该项目通过模型上下文协议 (MCP) 将 ElevenLabs 的文本转语音功能与 Cursor 集成。它由一个 FastAPI 后端服务和一个 React 前端应用程序组成。\n\n## 特征\n\n* 使用 ElevenLabs API 进行文本到语音的转换\n* 语音选择和管理\n* Cursor 的 MCP 集成\n* 现代 React 前端界面\n* WebSocket实时通信\n* 代码质量的预提交钩子\n* 自动代码格式化和 linting\n\n## 项目结构\n\n```\njessica/\n├── src/\n│ ├── backend/ # FastAPI backend service\n│ └── frontend/ # React frontend application\n├── terraform/ # Infrastructure as Code\n├── tests/ # Test suites\n└── docs/ # Documentation\n```\n\n## 要求\n\n* Python 3.11+\n* Poetry（用于后端依赖管理）\n* Node.js 18+（用于前端）\n* 光标（用于 MCP 集成）\n\n## 本地开发设置\n\n### 后端设置\n\n```bash\n# Clone the repository\ngit clone https://github.com/georgi-io/jessica.git\ncd jessica\n\n# Create Python virtual environment\npython -m venv .venv\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Install backend dependencies\npoetry install\n\n# Configure environment\ncp .env.example .env\n# Edit .env with your ElevenLabs API key\n\n# Install pre-commit hooks\npoetry run pre-commit install\n```\n\n### 前端设置\n\n```bash\n# Navigate to frontend directory\ncd src/frontend\n\n# Install dependencies\nnpm install\n```\n\n## 开发服务器\n\n### 启动后端\n\n```bash\n# Activate virtual environment if not active\nsource .venv/bin/activate # On Windows: .venv\\Scripts\\activate\n\n# Start the backend\npython -m src.backend\n```\n\n后端提供：\n\n* REST API： \n* WebSocket：ws://localhost:9020/ws\n* MCP 服务器：（与主 API 服务器集成）\n\n### 启动前端\n\n```bash\n# In src/frontend directory\nnpm run dev\n```\n\n前端开发服务器：\n\n* \n\n## 环境配置\n\n### 后端（.env）\n\n```env\n# ElevenLabs API\nELEVENLABS_API_KEY=your-api-key\n\n# Server Configuration\nHOST=127.0.0.1\nPORT=9020\n\n# Development Settings\nDEBUG=false\nRELOAD=true\n```\n\n### 前端（.env）\n\n```env\nVITE_API_URL=http://localhost:9020\nVITE_WS_URL=ws://localhost:9020/ws\n```\n\n## 代码质量工具\n\n### 后端\n\n```bash\n# Run all pre-commit hooks\npoetry run pre-commit run --all-files\n\n# Run specific tools\npoetry run ruff check .\npoetry run ruff format .\npoetry run pytest\n```\n\n### 前端\n\n```bash\n# Lint\nnpm run lint\n\n# Type check\nnpm run type-check\n\n# Test\nnpm run test\n```\n\n## 生产部署\n\n### AWS ECR 和 GitHub Actions 设置\n\n要启用自动构建并将 Docker 镜像推送到 Amazon ECR：\n\n1. 应用 Terraform 配置来创建所需的 AWS 资源：\n\n ```bash\n cd terraform\n terraform init\n terraform apply\n ```\n\n2. GitHub Actions 工作流程将自动：\n\n * 从 S3 中的 Terraform 状态读取必要的配置\n * 在推送到`main`或`develop`分支时构建 Docker 镜像\n * 将镜像推送到 ECR，并标记`latest`和特定提交 SHA\n\n3. 无需额外的存储库变量！工作流从 Terraform 状态中获取所有必需的配置。\n\n### 工作原理\n\nGitHub Actions 工作流程配置为：\n\n1. 首先假设一个具有 S3 读取权限的预定义 IAM 角色\n2. 从 S3 中的 Terraform 状态文件中获取并提取配置值\n3. 使用状态文件中的实际部署角色重新进行身份验证\n4. 构建 Docker 镜像并将其推送到状态中定义的 ECR 存储库\n\n这种方法无需手动配置 GitHub 存储库变量，并确保 CI/CD 流程始终使用当前基础设施配置。\n\n### 快速概览\n\n* 前端：通过 jessica.georgi.io 上的 CloudFront 由 S3 提供服务\n* 后端 API：api.georgi.io/jessica\n* WebSocket：连接到 api.georgi.io/jessica/ws\n* Docker 镜像：存储在 AWS ECR 中，可以部署到 ECS/EKS\n* 基础设施：通过此存储库中的 Terraform 进行管理\n\n## MCP 与 Cursor 集成\n\n1. 启动后端服务器\n2. 在 Cursor 设置中，添加新的 MCP 服务器：\n * 姓名：Jessica TTS\n * 类型：SSE\n * 网址： \n\n## 故障排除\n\n### 常见问题\n\n1. **API 密钥问题**\n\n * 错误：“无效的 API 密钥”\n * 解决方案：检查`.env`文件\n\n2. **连接问题**\n\n * 错误：“无法连接到 MCP 服务器”\n * 解决方案：验证后端是否正在运行并且端口是否正确\n\n3. **端口冲突**\n\n * 错误：“地址已被使用”\n * 解决方案：更改`.env`中的端口\n\n4. **WebSocket 连接失败**\n\n * 错误：“WebSocket 连接失败”\n * 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