[{"_1":2,"_529":-5,"_530":-5},"loaderData",{"_3":4,"_22":23,"_24":25,"_365":366},"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",6160108,"membership","referenceId","0196b3e7-ed4a-7e59-aea7-21be7e2c2a05","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_68":-5,"_69":28,"_29":70,"_283":284,"_364":193},"availableLocales",[28,40,47,54,61],{"_29":30,"_33":37,"_38":39},"mcpServer",{"_31":32,"_33":34,"_35":36},"description","MCP-compatible server that enables AI assistants to interact with Lightdash analytics data, providing tools to list and retrieve projects, spaces, charts, dashboards, and metrics through a standardized interface.","name","Lightdash MCP Server","readme","# lightdash-mcp-server\n[![smithery badge](https://smithery.ai/badge/@syucream/lightdash-mcp-server)](https://smithery.ai/server/@syucream/lightdash-mcp-server)\n[![npm version](https://badge.fury.io/js/lightdash-mcp-server.svg)](https://badge.fury.io/js/lightdash-mcp-server)\n\nA [MCP(Model Context Protocol)](https://www.anthropic.com/news/model-context-protocol) server that accesses to [Lightdash](https://www.lightdash.com/).\n\nThis server provides MCP-compatible access to Lightdash's API, allowing AI assistants to interact with your Lightdash data through a standardized interface.\n\n\n $\"Lightdash$ \n\n\n## Features\n\nAvailable tools:\n\n- `list_projects` - List all projects in the Lightdash organization\n- `get_project` - Get details of a specific project\n- `list_spaces` - List all spaces in a project\n- `list_charts` - List all charts in a project\n- `list_dashboards` - List all dashboards in a project\n- `get_custom_metrics` - Get custom metrics for a project\n- `get_catalog` - Get catalog for a project\n- `get_metrics_catalog` - Get metrics catalog for a project\n- `get_charts_as_code` - Get charts as code for a project\n- `get_dashboards_as_code` - Get dashboards as code for a project\n\n## Quick Start\n\n### Installation\n\n#### Installing via Smithery\n\nTo install Lightdash MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@syucream/lightdash-mcp-server):\n\n```bash\nnpx -y @smithery/cli install lightdash-mcp-server --client claude\n```\n\n#### Manual Installation\n```bash\nnpm install lightdash-mcp-server\n```\n\n### Configuration\n\nCreate a `.env` file with your Lightdash API credentials:\n\n```env\nLIGHTDASH_API_KEY=your_api_key\nLIGHTDASH_API_URL=https://app.lightdash.cloud # or your custom Lightdash instance URL\n```\n\n### Usage\n\n1. Start the MCP server.\n\nDirectly:\n```bash\nnpx lightdash-mcp-server\n```\nOr, run the installed module with node.\n\n2. Edit your MCP configuration json:\n```json\n...\n \"lightdash\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"lightdash-mcp-server\"\n ],\n \"env\": {\n \"LIGHTDASH_API_KEY\": \"\",\n \"LIGHTDASH_API_URL\": \"https://\"\n }\n },\n...\n```\n\n## Development\n\n### Available Scripts\n\n- `npm run dev` - Start the server in development mode with hot reloading\n- `npm run build` - Build the project for production\n- `npm run start` - Start the production server\n- `npm run lint` - Run linting checks (ESLint and Prettier)\n- `npm run fix` - Automatically fix linting issues\n- `npm run examples` - Run the example scripts\n\n### Contributing\n\n1. Fork the repository\n2. Create your feature branch\n3. Run tests and linting: `npm run lint`\n4. Commit your changes\n5. Push to the branch\n6. Create a Pull Request\n","English","tag","en-US",{"_29":41,"_33":45,"_38":46},{"_31":42,"_33":43,"_35":44},"Servidor compatible con MCP que permite a los asistentes de IA interactuar con los datos analíticos de Lightdash, proporcionando herramientas para enumerar y recuperar proyectos, espacios, gráficos, paneles y métricas a través de una interfaz estandarizada.","Servidor MCP de Lightdash","# servidor lightdash-mcp\n\n[![insignia de herrería](https://smithery.ai/badge/@syucream/lightdash-mcp-server)](https://smithery.ai/server/@syucream/lightdash-mcp-server)[![versión npm](https://badge.fury.io/js/lightdash-mcp-server.svg)](https://badge.fury.io/js/lightdash-mcp-server)\n\nUn servidor [MCP (Protocolo de contexto de modelo)](https://www.anthropic.com/news/model-context-protocol) que accede a [Lightdash](https://www.lightdash.com/) .\n\nEste servidor proporciona acceso compatible con MCP a la API de Lightdash, lo que permite que los asistentes de IA interactúen con sus datos de Lightdash a través de una interfaz estandarizada.\n\n## Características\n\nHerramientas disponibles:\n\n* `list_projects` - Lista todos los proyectos en la organización Lightdash\n* `get_project` - Obtener detalles de un proyecto específico\n* `list_spaces` - Lista todos los espacios en un proyecto\n* `list_charts` - Lista todos los gráficos de un proyecto\n* `list_dashboards` - Lista todos los paneles de un proyecto\n* `get_custom_metrics` - Obtener métricas personalizadas para un proyecto\n* `get_catalog` - Obtener el catálogo de un proyecto\n* `get_metrics_catalog` - Obtener el catálogo de métricas de un proyecto\n* `get_charts_as_code` - Obtener gráficos como código para un proyecto\n* `get_dashboards_as_code` - Obtener paneles como código para un proyecto\n\n## Inicio rápido\n\n### Instalación\n\n#### Instalación mediante herrería\n\nPara instalar Lightdash MCP Server para Claude Desktop automáticamente a través de [Smithery](https://smithery.ai/server/@syucream/lightdash-mcp-server) :\n\n```bash\nnpx -y @smithery/cli install lightdash-mcp-server --client claude\n```\n\n#### Instalación manual\n\n```bash\nnpm install lightdash-mcp-server\n```\n\n### Configuración\n\nCree un archivo `.env` con sus credenciales de API de Lightdash:\n\n```env\nLIGHTDASH_API_KEY=your_api_key\nLIGHTDASH_API_URL=https://app.lightdash.cloud # or your custom Lightdash instance URL\n```\n\n### Uso\n\n1. Inicie el servidor MCP.\n\nDirectamente:\n\n```bash\nnpx lightdash-mcp-server\n```\n\nO bien, ejecute el módulo instalado con el nodo.\n\n2. Edite su json de configuración de MCP:\n\n```json\n...\n \"lightdash\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"lightdash-mcp-server\"\n ],\n \"env\": {\n \"LIGHTDASH_API_KEY\": \"\",\n \"LIGHTDASH_API_URL\": \"https://\"\n }\n },\n...\n```\n\n## Desarrollo\n\n### Scripts disponibles\n\n* `npm run dev` - Inicia el servidor en modo de desarrollo con recarga en caliente\n* `npm run build` : compila el proyecto para producción\n* `npm run start` : inicia el servidor de producción\n* `npm run lint` : ejecuta comprobaciones de linting (ESLint y Prettier)\n* `npm run fix` : corrige automáticamente los problemas de pelusa\n* `npm run examples` : ejecutar los scripts de ejemplo\n\n### Contribuyendo\n\n1. Bifurcar el repositorio\n2. Crea tu rama de funciones\n3. Ejecutar pruebas y linting: `npm run lint`\n4. Confirme sus cambios\n5. Empujar hacia la rama\n6. Crear una solicitud de extracción","Spanish","es-ES",{"_29":48,"_33":52,"_38":53},{"_31":49,"_33":50,"_35":51},"AI アシスタントが Lightdash 分析データと対話できるようにし、標準化されたインターフェースを通じてプロジェクト、スペース、チャート、ダッシュボード、メトリックを一覧表示および取得するためのツールを提供する MCP 互換サーバー。","Lightdash MCP サーバー","# lightdash-mcp-server\n\n[![鍛冶屋のバッジ](https://smithery.ai/badge/@syucream/lightdash-mcp-server)](https://smithery.ai/server/@syucream/lightdash-mcp-server)[![npmバージョン](https://badge.fury.io/js/lightdash-mcp-server.svg)](https://badge.fury.io/js/lightdash-mcp-server)\n\n[Lightdash](https://www.lightdash.com/)へアクセスする[MCP(Model Context Protocol)](https://www.anthropic.com/news/model-context-protocol)サーバー。\n\nこのサーバーは、Lightdash の API への MCP 互換アクセスを提供し、AI アシスタントが標準化されたインターフェースを通じて Lightdash データと対話できるようにします。\n\n## 特徴\n\n利用可能なツール:\n\n* `list_projects` - Lightdash組織内のすべてのプロジェクトを一覧表示します\n* `get_project` - 特定のプロジェクトの詳細を取得する\n* `list_spaces` - プロジェクト内のすべてのスペースを一覧表示する\n* `list_charts` - プロジェクト内のすべてのチャートを一覧表示する\n* `list_dashboards` - プロジェクト内のすべてのダッシュボードを一覧表示する\n* `get_custom_metrics` - プロジェクトのカスタムメトリックを取得する\n* `get_catalog` - プロジェクトのカタログを取得する\n* `get_metrics_catalog` - プロジェクトのメトリクスカタログを取得する\n* `get_charts_as_code` - プロジェクトのチャートをコードとして取得する\n* `get_dashboards_as_code` - プロジェクトのダッシュボードをコードとして取得する\n\n## クイックスタート\n\n### インストール\n\n#### Smithery経由でインストール\n\n[Smithery](https://smithery.ai/server/@syucream/lightdash-mcp-server)経由で Claude Desktop 用の Lightdash MCP Server を自動的にインストールするには:\n\n```bash\nnpx -y @smithery/cli install lightdash-mcp-server --client claude\n```\n\n#### 手動インストール\n\n```bash\nnpm install lightdash-mcp-server\n```\n\n### 構成\n\nLightdash API 認証情報を使用して`.env`ファイルを作成します。\n\n```env\nLIGHTDASH_API_KEY=your_api_key\nLIGHTDASH_API_URL=https://app.lightdash.cloud # or your custom Lightdash instance URL\n```\n\n### 使用法\n\n1. MCP サーバーを起動します。\n\n直接：\n\n```bash\nnpx lightdash-mcp-server\n```\n\nまたは、インストールされたモジュールを node で実行します。\n\n2. MCP 構成 json を編集します。\n\n```json\n...\n \"lightdash\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"lightdash-mcp-server\"\n ],\n \"env\": {\n \"LIGHTDASH_API_KEY\": \"\",\n \"LIGHTDASH_API_URL\": \"https://\"\n }\n },\n...\n```\n\n## 発達\n\n### 利用可能なスクリプト\n\n* `npm run dev` - ホットリロードで開発モードでサーバーを起動します\n* `npm run build` - 本番環境用にプロジェクトをビルドする\n* `npm run start` - 本番サーバーを起動する\n* `npm run lint` - リンティングチェックを実行する（ESLint と Prettier）\n* `npm run fix` - リンティングの問題を自動的に修正する\n* `npm run examples` - サンプルスクリプトを実行する\n\n### 貢献\n\n1. リポジトリをフォークする\n2. 機能ブランチを作成する\n3. テストとリンティングを実行する: `npm run lint`\n4. 変更をコミットする\n5. ブランチにプッシュする\n6. プルリクエストを作成する","Japanese","ja-JP",{"_29":55,"_33":59,"_38":60},{"_31":56,"_33":57,"_35":58},"AI 어시스턴트가 Lightdash 분석 데이터와 상호 작용할 수 있도록 하는 MCP 호환 서버로, 표준화된 인터페이스를 통해 프로젝트, 공간, 차트, 대시보드 및 지표를 나열하고 검색할 수 있는 도구를 제공합니다.","Lightdash MCP 서버","# lightdash-mcp-서버\n\n[![대장간 배지](https://smithery.ai/badge/@syucream/lightdash-mcp-server)](https://smithery.ai/server/@syucream/lightdash-mcp-server) [![npm 버전](https://badge.fury.io/js/lightdash-mcp-server.svg)](https://badge.fury.io/js/lightdash-mcp-server)\n\n[Lightdash](https://www.lightdash.com/) 에 접근하는 [MCP(Model Context Protocol)](https://www.anthropic.com/news/model-context-protocol) 서버입니다.\n\n이 서버는 Lightdash API에 대한 MCP 호환 액세스를 제공하여 AI 어시스턴트가 표준화된 인터페이스를 통해 Lightdash 데이터와 상호 작용할 수 있도록 합니다.\n\n## 특징\n\n사용 가능한 도구:\n\n* `list_projects` - Lightdash 조직의 모든 프로젝트를 나열합니다.\n* `get_project` - 특정 프로젝트의 세부 정보를 가져옵니다\n* `list_spaces` - 프로젝트의 모든 공간 나열\n* `list_charts` - 프로젝트의 모든 차트를 나열합니다\n* `list_dashboards` - 프로젝트의 모든 대시보드 나열\n* `get_custom_metrics` - 프로젝트에 대한 사용자 정의 메트릭 가져오기\n* `get_catalog` - 프로젝트에 대한 카탈로그 가져오기\n* `get_metrics_catalog` - 프로젝트에 대한 메트릭 카탈로그 가져오기\n* `get_charts_as_code` - 프로젝트에 대한 코드로 차트 가져오기\n* `get_dashboards_as_code` - 프로젝트의 대시보드를 코드로 가져오기\n\n## 빠른 시작\n\n### 설치\n\n#### Smithery를 통해 설치\n\n[Smithery를](https://smithery.ai/server/@syucream/lightdash-mcp-server) 통해 Claude Desktop에 Lightdash MCP 서버를 자동으로 설치하려면:\n\n지엑스피1\n\n#### 수동 설치\n\n```bash\nnpm install lightdash-mcp-server\n```\n\n### 구성\n\nLightdash API 자격 증명으로 `.env` 파일을 만듭니다.\n\n```env\nLIGHTDASH_API_KEY=your_api_key\nLIGHTDASH_API_URL=https://app.lightdash.cloud # or your custom Lightdash instance URL\n```\n\n### 용법\n\n1. MCP 서버를 시작합니다.\n\n곧장:\n\n```bash\nnpx lightdash-mcp-server\n```\n\n또는 설치된 모듈을 node로 실행합니다.\n\n2. MCP 구성 JSON을 편집하세요.\n\n```json\n...\n \"lightdash\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"lightdash-mcp-server\"\n ],\n \"env\": {\n \"LIGHTDASH_API_KEY\": \"\",\n \"LIGHTDASH_API_URL\": \"https://\"\n }\n },\n...\n```\n\n## 개발\n\n### 사용 가능한 스크립트\n\n* `npm run dev` - 핫 리로딩을 사용하여 개발 모드로 서버 시작\n* `npm run build` - 프로덕션을 위한 프로젝트 빌드\n* `npm run start` - 프로덕션 서버 시작\n* `npm run lint` - 린팅 검사 실행(ESLint 및 Prettier)\n* `npm run fix` - 린팅 문제를 자동으로 수정합니다\n* `npm run examples` - 예제 스크립트 실행\n\n### 기여하다\n\n1. 저장소를 포크하세요\n2. 기능 브랜치를 생성하세요\n3. 테스트 및 린팅 실행: `npm run lint`\n4. 변경 사항을 커밋하세요\n5. 지점으로 밀어 넣기\n6. 풀 리퀘스트 만들기","Korean","ko-KR",{"_29":62,"_33":66,"_38":67},{"_31":63,"_33":64,"_35":65},"MCP 兼容服务器，使 AI 助手能够与 Lightdash 分析数据进行交互，提供通过标准化界面列出和检索项目、空间、图表、仪表板和指标的工具。","Lightdash MCP 服务器","# lightdash-mcp-服务器\n\n[![铁匠徽章](https://smithery.ai/badge/@syucream/lightdash-mcp-server)](https://smithery.ai/server/@syucream/lightdash-mcp-server) [![npm 版本](https://badge.fury.io/js/lightdash-mcp-server.svg)](https://badge.fury.io/js/lightdash-mcp-server)\n\n访问[Lightdash](https://www.lightdash.com/)的[MCP（模型上下文协议）](https://www.anthropic.com/news/model-context-protocol)服务器。\n\n该服务器提供与 MCP 兼容的 Lightdash API 访问，允许 AI 助手通过标准化界面与您的 Lightdash 数据进行交互。\n\n## 特征\n\n可用工具：\n\n* `list_projects` - 列出 Lightdash 组织中的所有项目\n* `get_project` - 获取特定项目的详细信息\n* `list_spaces` - 列出项目中的所有空间\n* `list_charts` - 列出项目中的所有图表\n* `list_dashboards` - 列出项目中的所有仪表板\n* `get_custom_metrics` - 获取项目的自定义指标\n* `get_catalog` - 获取项目目录\n* `get_metrics_catalog` - 获取项目的指标目录\n* `get_charts_as_code` - 获取项目图表的代码\n* `get_dashboards_as_code` - 获取项目仪表板的代码\n\n## 快速入门\n\n### 安装\n\n#### 通过 Smithery 安装\n\n要通过[Smithery](https://smithery.ai/server/@syucream/lightdash-mcp-server)自动为 Claude Desktop 安装 Lightdash MCP 服务器：\n\n```bash\nnpx -y @smithery/cli install lightdash-mcp-server --client claude\n```\n\n#### 手动安装\n\n```bash\nnpm install lightdash-mcp-server\n```\n\n### 配置\n\n使用您的 Lightdash API 凭证创建一个`.env`文件：\n\n```env\nLIGHTDASH_API_KEY=your_api_key\nLIGHTDASH_API_URL=https://app.lightdash.cloud # or your custom Lightdash instance URL\n```\n\n### 用法\n\n1. 启动 MCP 服务器。\n\n直接地：\n\n```bash\nnpx lightdash-mcp-server\n```\n\n或者，使用节点运行已安装的模块。\n\n2. 编辑您的 MCP 配置 json：\n\n```json\n...\n \"lightdash\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"lightdash-mcp-server\"\n ],\n \"env\": {\n \"LIGHTDASH_API_KEY\": \"\",\n \"LIGHTDASH_API_URL\": \"https://\"\n }\n },\n...\n```\n\n## 发展\n\n### 可用脚本\n\n* `npm run dev` - 使用热重载以开发模式启动服务器\n* `npm run build`构建生产项目\n* `npm run start`启动生产服务器\n* `npm run lint` - 运行 linting 检查（ESLint 和 Prettier）\n* `npm run fix` - 自动修复 linting 问题\n* `npm run examples` - 运行示例脚本\n\n### 贡献\n\n1. 分叉存储库\n2. 创建你的功能分支\n3. 运行测试和 lint： `npm run lint`\n4. 提交你的更改\n5. 推送到分支\n6. 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