[{"_1":2,"_358":-5,"_359":-5},"loaderData",{"_3":4,"_29":30,"_269":270},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","019a22e4-a985-7321-bc7b-fd3735feeb68","title","You need to sign in to access this page","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":24,"_25":-5,"_26":27,"_28":-5},"attributes",[22],"bot","id",21005410,"membership","referenceId","019a17bf-377c-71fd-947e-fd17d1ca8e82","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_31":32,"_87":-5,"_88":45,"_34":89,"_186":187,"_268":171},"availableLocales",[33,45,52,59,66,73,80],{"_34":35,"_38":42,"_43":44},"mcpServer",{"_36":37,"_38":39,"_40":41},"description","Zugriff auf Monitor- und Cluster-Protokolle von Datadog bereitstellen","name","Datenhund","readme","# Datadog Model Context Protocol (MCP) 🔍\n\n[![Schmiedeabzeichen](https://smithery.ai/badge/@didlawowo/mcp-collection)](https://smithery.ai/server/@didlawowo/mcp-collection)\n\nEin Python-basiertes Tool zur Interaktion mit der Datadog-API und zum Abrufen von Überwachungsdaten aus Ihrer Infrastruktur. Dieses MCP bietet einfachen Zugriff auf Monitorzustände und Kubernetes-Protokolle über eine einfache Schnittstelle.\n\n## Datadog-Funktionen 🌟\n\n* **Überwachungsstatusverfolgung** : Abrufen und Analysieren bestimmter Monitorzustände\n* **Kubernetes-Protokollanalyse** : Extrahieren und formatieren Sie Fehlerprotokolle aus Kubernetes-Clustern\n\n## Voraussetzungen 📋\n\n* Python 3.11+\n* Datadog-API- und Anwendungsschlüssel (mit den richtigen Berechtigungen)\n* Zugriff auf die Datadog-Site\n\n## Installation 🔧\n\n### Installation über Smithery\n\nSo installieren Sie Datadog für Claude Desktop automatisch über [Smithery](https://smithery.ai/server/@didlawowo/mcp-collection) :\n\n```bash\nnpx -y @smithery/cli install @didlawowo/mcp-collection --client claude\n```\n\nBenötigte Pakete:\n\n```text\ndatadog-api-client\nfastmcp\nloguru\nicecream\npython-dotenv\nuv\n```\n\n## Umgebungseinrichtung 🔑\n\nErstellen Sie eine `.env` Datei mit Ihren Datadog-Anmeldeinformationen:\n\n```env\nDD_API_KEY=your_api_key\nDD_APP_KEY=your_app_key\n```\n\n## Claude Desktop Setup für MCP einrichten 🖥️\n\n1. Installieren Sie Claude Desktop\n\n```bash\n# Assuming you're on macOS\nbrew install claude-desktop\n\n# Or download from official website\nhttps://claude.ai/desktop\n```\n\n2. Richten Sie die Datadog MCP-Konfiguration ein:\n\n````bash\n# on mac is \n~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\n\n# Add this to your claude config json\n```json\n \"Datadog-MCP-Server\": {\n \"command\": \"uv\",\n \"args\": [\n \"run\",\n \"--with\",\n \"datadog-api-client\",\n \"--with\",\n \"fastmcp\",\n \"--with\",\n \"icecream\",\n \"--with\",\n \"loguru\",\n \"--with\",\n \"python-dotenv\",\n \"fastmcp\",\n \"run\",\n \"/your-path/mcp-collection/datadog/main.py\"\n ],\n \"env\": {\n \"DD_API_KEY\": \"xxxx\",\n \"DD_APP_KEY\": \"xxx\"\n }\n },\n````\n\n## Verwendung 💻\n\n![Protokolle abrufen](assets/logs.gif)\n\n![Monitor erhalten](assets/monitor.gif)\n\n## Architektur 🏗\n\n* **FastMCP Base** : Nutzt das FastMCP-Framework für die Werkzeugverwaltung\n* **Modulares Design** : Separate Funktionen für Monitore und Protokolle\n* **Typsicherheit** : Vollständige Typisierungsunterstützung mit Python-Typhinweisen\n* **API-Abstraktion** : Umschlossene Datadog-API-Aufrufe mit Fehlerbehandlung\n\nIch werde einen Abschnitt über die Einrichtung von MCP und Claude Desktop hinzufügen:\n\n# Einführung in das Model Context Protocol (MCP) 🤖\n\n## Was ist MCP?\n\nDas Model Context Protocol (MCP) ist ein Framework, das KI-Modellen die standardisierte Interaktion mit externen Tools und APIs ermöglicht. Es ermöglicht Modellen wie Claude:\n\n* Zugriff auf externe Daten\n* Befehle ausführen\n* Interagieren Sie mit APIs\n* Behalten Sie den Kontext über Gespräche hinweg bei\n\n## einige Beispiele für MCP-Server\n\n\n\n## Tutorial zur Einrichtung von MCP\n\n\n\n## So funktioniert es - Verfügbare Funktionen 🛠️\n\nDas LLM verwendet die bereitgestellte Funktion, um die Daten abzurufen und zu verwenden\n\n### 1. Monitorzustände abrufen\n\n```python\nget_monitor_states(\n name: str, # Monitor name to search\n timeframe: int = 1 # Hours to look back\n)\n```\n\nBeispiel:\n\n```python\n\nresponse = get_monitor_states(name=\"traefik\")\n\n# Sample Output\n{\n \"id\": \"12345678\",\n \"name\": \"traefik\",\n \"status\": \"OK\",\n \"query\": \"avg(last_5m):avg:traefik.response_time{*} > 1000\",\n \"message\": \"Response time is too high\",\n \"type\": \"metric alert\",\n \"created\": \"2024-01-14T10:00:00Z\",\n \"modified\": \"2024-01-14T15:30:00Z\"\n}\n```\n\n### 2. Kubernetes-Protokolle abrufen\n\n```python\nget_k8s_logs(\n cluster: str, # Kubernetes cluster name\n timeframe: int = 5, # Hours to look back\n namespace: str = None # Optional namespace filter\n)\n```\n\nBeispiel:\n\n```python\nlogs = get_k8s_logs(\n cluster=\"prod-cluster\",\n timeframe=3,\n namespace=\"default\"\n)\n\n# Sample Output\n{\n \"timestamp\": \"2024-01-14T22:00:00Z\",\n \"host\": \"worker-1\",\n \"service\": \"nginx-ingress\",\n \"pod_name\": \"nginx-ingress-controller-abc123\",\n \"namespace\": \"default\",\n \"container_name\": \"controller\",\n \"message\": \"Connection refused\",\n \"status\": \"error\"\n}\n```\n\n```bash\n# Install as MCP extension\ncd datadog\ntask install-mcp\n```\n\n## 4. Installation überprüfen\n\n### Im Claude-Chat-Desktop\n\nÜberprüfen Sie die Datadog-Verbindung in Claude\n\n![Setup-Claude](assets/config.png)\n\n## 5. Verwenden Sie Datadog MCP Tools\n\n## Sicherheitsüberlegungen 🔒\n\n* Speichern Sie API-Schlüssel in `.env`\n* MCP läuft in isolierter Umgebung\n* Jedes Tool verfügt über definierte Berechtigungen\n* Ratenbegrenzung ist implementiert\n\n## Fehlerbehebung 🔧\n\n### Verwenden des MCP Inspector\n\n```bash\n# Launch MCP Inspector for debugging\ntask run-mcp-inspector\n```\n\nDer MCP Inspector bietet:\n\n* Echtzeitansicht des MCP-Serverstatus\n* Funktionsaufrufprotokolle\n* Fehlerverfolgung\n* API-Antwortüberwachung\n\n### Häufige Probleme und Lösungen\n\n1. **API-Authentifizierungsfehler**\n\n ```bash\n Error: (403) Forbidden\n ```\n\n ➡️ Überprüfen Sie Ihren DD\\_API\\_KEY und DD\\_APP\\_KEY in .env\n\n2. **MCP-Verbindungsprobleme**\n\n ```bash\n Error: Failed to connect to MCP server\n ```\n\n ➡️ Überprüfen Sie den Pfad und Inhalt Ihrer claude\\_desktop\\_config.json\n\n3. **Monitor nicht gefunden**\n\n ```bash\n Error: No monitor found with name 'xxx'\n ```\n\n ➡️ Überprüfen Sie die Rechtschreibung und Groß-/Kleinschreibung des Monitornamens\n\n4. **Protokolle finden Sie hier**\n\n![Alternativtext](assets/logs.png)\n\n## Mitwirken 🤝\n\nFühlen Sie sich frei:\n\n1. Offene Probleme aufgrund von Fehlern\n2. Senden Sie PRs für Verbesserungen\n3. Neue Funktionen hinzufügen\n\n## Notizen 📝\n\n* API-Aufrufe erfolgen an die EU-Site von Datadog\n* Der Standardzeitrahmen für Monitorzustände beträgt 1 Stunde\n* Die Seitengrößenbeschränkungen sind so eingestellt, dass sie die meisten Anwendungsfälle abdecken","German","tag","de-DE",{"_34":46,"_38":50,"_43":51},{"_36":47,"_38":48,"_40":49},"provide access to monitor and cluster logs from datadog","datadog","# Datadog Model Context Protocol (MCP) 🔍\n\n[![smithery badge](https://smithery.ai/badge/@didlawowo/mcp-collection)](https://smithery.ai/server/@didlawowo/mcp-collection)\n\nA Python-based tool to interact with Datadog API and fetch monitoring data from your infrastructure. This MCP provides easy access to monitor states and Kubernetes logs through a simple interface.\n\n## Datadog Features 🌟\n\n- **Monitor State Tracking**: Fetch and analyze specific monitor states\n- **Kubernetes Log Analysis**: Extract and format error logs from Kubernetes clusters\n\n## Prerequisites 📋\n\n- Python 3.11+\n- Datadog API and Application keys (with correct permissions)\n- Access to Datadog site\n\n## Installation 🔧\n\n### Installing via Smithery\n\nTo install Datadog for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@didlawowo/mcp-collection):\n\n```bash\nnpx -y @smithery/cli install @didlawowo/mcp-collection --client claude\n```\n\nRequired packages:\n\n```text\ndatadog-api-client\nfastmcp\nloguru\nicecream\npython-dotenv\nuv\n```\n\n## Environment Setup 🔑\n\nCreate a `.env` file with your Datadog credentials:\n\n```env\nDD_API_KEY=your_api_key\nDD_APP_KEY=your_app_key\n```\n\n## Setup Claude Desktop Setup for MCP 🖥️\n\n1. Install Claude Desktop\n\n```bash\n# Assuming you're on macOS\nbrew install claude-desktop\n\n# Or download from official website\nhttps://claude.ai/desktop\n```\n\n2. Set up Datadog MCP config:\n\n```bash\n# on mac is \n~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\n\n# Add this to your claude config json\n```json\n \"Datadog-MCP-Server\": {\n \"command\": \"uv\",\n \"args\": [\n \"run\",\n \"--with\",\n \"datadog-api-client\",\n \"--with\",\n \"fastmcp\",\n \"--with\",\n \"icecream\",\n \"--with\",\n \"loguru\",\n \"--with\",\n \"python-dotenv\",\n \"fastmcp\",\n \"run\",\n \"/your-path/mcp-collection/datadog/main.py\"\n ],\n \"env\": {\n \"DD_API_KEY\": \"xxxx\",\n \"DD_APP_KEY\": \"xxx\"\n }\n },\n```\n\n## Usage 💻\n\n![get logs](assets/logs.gif)\n\n![get monitor](assets/monitor.gif)\n\n## Architecture 🏗\n\n- **FastMCP Base**: Utilizes FastMCP framework for tool management\n- **Modular Design**: Separate functions for monitors and logs\n- **Type Safety**: Full typing support with Python type hints\n- **API Abstraction**: Wrapped Datadog API calls with error handling\n\nI'll add a section about MCP and Claude Desktop setup:\n\n# Model Context Protocol (MCP) Introduction 🤖\n\n## What is MCP?\n\nModel Context Protocol (MCP) is a framework allowing AI models to interact with external tools and APIs in a standardized way. It enables models like Claude to:\n\n- Access external data\n- Execute commands\n- Interact with APIs\n- Maintain context across conversations\n\n## some examples of MCP servers\n\n\n\n## Tutorial for setup MCP\n\n\n\n## How it works - Available Functions 🛠️\n\nthe LLM use provided function to get the data and use it\n\n### 1. Get Monitor States\n\n```python\nget_monitor_states(\n name: str, # Monitor name to search\n timeframe: int = 1 # Hours to look back\n)\n```\n\nExample:\n\n```python\n\nresponse = get_monitor_states(name=\"traefik\")\n\n# Sample Output\n{\n \"id\": \"12345678\",\n \"name\": \"traefik\",\n \"status\": \"OK\",\n \"query\": \"avg(last_5m):avg:traefik.response_time{*} > 1000\",\n \"message\": \"Response time is too high\",\n \"type\": \"metric alert\",\n \"created\": \"2024-01-14T10:00:00Z\",\n \"modified\": \"2024-01-14T15:30:00Z\"\n}\n```\n\n### 2. Get Kubernetes Logs\n\n```python\nget_k8s_logs(\n cluster: str, # Kubernetes cluster name\n timeframe: int = 5, # Hours to look back\n namespace: str = None # Optional namespace filter\n)\n```\n\nExample:\n\n```python\nlogs = get_k8s_logs(\n cluster=\"prod-cluster\",\n timeframe=3,\n namespace=\"default\"\n)\n\n# Sample Output\n{\n \"timestamp\": \"2024-01-14T22:00:00Z\",\n \"host\": \"worker-1\",\n \"service\": \"nginx-ingress\",\n \"pod_name\": \"nginx-ingress-controller-abc123\",\n \"namespace\": \"default\",\n \"container_name\": \"controller\",\n \"message\": \"Connection refused\",\n \"status\": \"error\"\n}\n```\n\n```bash\n# Install as MCP extension\ncd datadog\ntask install-mcp\n```\n\n## 4. Verify Installation\n\n### In Claude chat desktop\n\n check datadog connection in claude\n\n![setup claude](assets/config.png)\n\n## 5. Use Datadog MCP Tools\n\n## Security Considerations 🔒\n\n- Store API keys in `.env`\n- MCP runs in isolated environment\n- Each tool has defined permissions\n- Rate limiting is implemented\n\n## Troubleshooting 🔧\n\n### Using MCP Inspector\n\n```bash\n# Launch MCP Inspector for debugging\ntask run-mcp-inspector\n```\n\nThe MCP Inspector provides:\n\n- Real-time view of MCP server status\n- Function call logs\n- Error tracing\n- API response monitoring\n\n### Common issues and solutions\n\n1. **API Authentication Errors**\n\n ```bash\n Error: (403) Forbidden\n ```\n\n ➡️ Check your DD_API_KEY and DD_APP_KEY in .env\n\n2. **MCP Connection Issues**\n\n ```bash\n Error: Failed to connect to MCP server\n ```\n\n ➡️ Verify your claude_desktop_config.json path and content\n\n3. **Monitor Not Found**\n\n ```bash\n Error: No monitor found with name 'xxx'\n ```\n\n ➡️ Check monitor name spelling and case sensitivity\n\n4. **logs can be found here**\n\n![alt text](assets/logs.png)\n\n## Contributing 🤝\n\nFeel free to:\n\n1. Open issues for bugs\n2. Submit PRs for improvements\n3. Add new features\n\n## Notes 📝\n\n- API calls are made to Datadog EU site\n- Default timeframe is 1 hour for monitor states\n- Page size limits are set to handle most use cases\n","English","en-US",{"_34":53,"_38":57,"_43":58},{"_36":54,"_38":55,"_40":56},"Proporcionar acceso a los registros del monitor y del clúster desde Datadog","perro de datos","# Protocolo de contexto del modelo (MCP) de Datadog\n\n[![insignia de herrería](https://smithery.ai/badge/@didlawowo/mcp-collection)](https://smithery.ai/server/@didlawowo/mcp-collection)\n\nUna herramienta basada en Python para interactuar con la API de Datadog y obtener datos de monitorización de su infraestructura. Este MCP facilita el acceso a los estados de monitorización y los registros de Kubernetes mediante una interfaz sencilla.\n\n## Características de Datadog 🌟\n\n* **Seguimiento del estado del monitor** : obtenga y analice estados de monitores específicos\n* **Análisis de registros de Kubernetes** : extraiga y formatee registros de errores de clústeres de Kubernetes\n\n## Prerrequisitos 📋\n\n* Python 3.11+\n* Claves de API y aplicación de Datadog (con permisos correctos)\n* Acceso al sitio de Datadog\n\n## Instalación 🔧\n\n### Instalación mediante herrería\n\nPara instalar Datadog para Claude Desktop automáticamente a través de [Smithery](https://smithery.ai/server/@didlawowo/mcp-collection) :\n\n```bash\nnpx -y @smithery/cli install @didlawowo/mcp-collection --client claude\n```\n\nPaquetes requeridos:\n\n```text\ndatadog-api-client\nfastmcp\nloguru\nicecream\npython-dotenv\nuv\n```\n\n## Configuración del entorno 🔑\n\nCree un archivo `.env` con sus credenciales de Datadog:\n\n```env\nDD_API_KEY=your_api_key\nDD_APP_KEY=your_app_key\n```\n\n## Configuración de Claude Desktop para MCP 🖥️\n\n1. Instalar Claude Desktop\n\n```bash\n# Assuming you're on macOS\nbrew install claude-desktop\n\n# Or download from official website\nhttps://claude.ai/desktop\n```\n\n2. Configurar la configuración MCP de Datadog:\n\n````bash\n# on mac is \n~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\n\n# Add this to your claude config json\n```json\n \"Datadog-MCP-Server\": {\n \"command\": \"uv\",\n \"args\": [\n \"run\",\n \"--with\",\n \"datadog-api-client\",\n \"--with\",\n \"fastmcp\",\n \"--with\",\n \"icecream\",\n \"--with\",\n \"loguru\",\n \"--with\",\n \"python-dotenv\",\n \"fastmcp\",\n \"run\",\n \"/your-path/mcp-collection/datadog/main.py\"\n ],\n \"env\": {\n \"DD_API_KEY\": \"xxxx\",\n \"DD_APP_KEY\": \"xxx\"\n }\n },\n````\n\n## Uso 💻\n\n![obtener registros](assets/logs.gif)\n\n![obtener monitor](assets/monitor.gif)\n\n## Arquitectura 🏗\n\n* **FastMCP Base** : utiliza el marco FastMCP para la gestión de herramientas\n* **Diseño modular** : funciones separadas para monitores y registros\n* **Seguridad de tipos** : compatibilidad total con sugerencias de tipos de Python\n* **Abstracción de API** : llamadas a la API de Datadog encapsuladas con manejo de errores\n\nAgregaré una sección sobre la configuración de MCP y Claude Desktop:\n\n# Introducción al Protocolo de Contexto Modelo (MCP) 🤖\n\n## ¿Qué es MCP?\n\nEl Protocolo de Contexto de Modelo (MCP) es un marco que permite a los modelos de IA interactuar con herramientas externas y API de forma estandarizada. Permite a modelos como Claude:\n\n* Acceder a datos externos\n* Ejecutar comandos\n* Interactuar con las API\n* Mantener el contexto en todas las conversaciones\n\n## Algunos ejemplos de servidores MCP\n\n\n\n## Tutorial para configurar MCP\n\n\n\n## Cómo funciona - Funciones disponibles 🛠️\n\nEl LLM utiliza la función proporcionada para obtener los datos y utilizarlos.\n\n### 1. Obtener estados del monitor\n\n```python\nget_monitor_states(\n name: str, # Monitor name to search\n timeframe: int = 1 # Hours to look back\n)\n```\n\nEjemplo:\n\n```python\n\nresponse = get_monitor_states(name=\"traefik\")\n\n# Sample Output\n{\n \"id\": \"12345678\",\n \"name\": \"traefik\",\n \"status\": \"OK\",\n \"query\": \"avg(last_5m):avg:traefik.response_time{*} > 1000\",\n \"message\": \"Response time is too high\",\n \"type\": \"metric alert\",\n \"created\": \"2024-01-14T10:00:00Z\",\n \"modified\": \"2024-01-14T15:30:00Z\"\n}\n```\n\n### 2. Obtener registros de Kubernetes\n\n```python\nget_k8s_logs(\n cluster: str, # Kubernetes cluster name\n timeframe: int = 5, # Hours to look back\n namespace: str = None # Optional namespace filter\n)\n```\n\nEjemplo:\n\n```python\nlogs = get_k8s_logs(\n cluster=\"prod-cluster\",\n timeframe=3,\n namespace=\"default\"\n)\n\n# Sample Output\n{\n \"timestamp\": \"2024-01-14T22:00:00Z\",\n \"host\": \"worker-1\",\n \"service\": \"nginx-ingress\",\n \"pod_name\": \"nginx-ingress-controller-abc123\",\n \"namespace\": \"default\",\n \"container_name\": \"controller\",\n \"message\": \"Connection refused\",\n \"status\": \"error\"\n}\n```\n\n```bash\n# Install as MCP extension\ncd datadog\ntask install-mcp\n```\n\n## 4. Verificar la instalación\n\n### En el escritorio de chat de Claude\n\ncomprobar la conexión de datadog en Claude\n\n![configuración claude](assets/config.png)\n\n## 5. Utilice las herramientas MCP de Datadog\n\n## Consideraciones de seguridad 🔒\n\n* Almacenar claves API en `.env`\n* MCP se ejecuta en un entorno aislado\n* Cada herramienta tiene permisos definidos\n* Se implementa la limitación de velocidad\n\n## Solución de problemas 🔧\n\n### Uso del inspector MCP\n\n```bash\n# Launch MCP Inspector for debugging\ntask run-mcp-inspector\n```\n\nEl Inspector MCP proporciona:\n\n* Vista en tiempo real del estado del servidor MCP\n* Registros de llamadas de función\n* Seguimiento de errores\n* Monitoreo de respuesta de API\n\n### Problemas comunes y soluciones\n\n1. **Errores de autenticación de API**\n\n ```bash\n Error: (403) Forbidden\n ```\n\n ➡️ Verifique su DD\\_API\\_KEY y DD\\_APP\\_KEY en .env\n\n2. **Problemas de conexión de MCP**\n\n ```bash\n Error: Failed to connect to MCP server\n ```\n\n ➡️ Verifique la ruta y el contenido de claude\\_desktop\\_config.json\n\n3. **Monitor no encontrado**\n\n ```bash\n Error: No monitor found with name 'xxx'\n ```\n\n ➡️ Verifique la ortografía del nombre del monitor y la distinción entre mayúsculas y minúsculas\n\n4. **Los registros se pueden encontrar aquí**\n\n![texto alternativo](assets/logs.png)\n\n## Contribuyendo 🤝\n\nSiéntete libre de:\n\n1. Problemas abiertos por errores\n2. Presentar solicitudes de mejora\n3. Añadir nuevas funciones\n\n## Notas 📝\n\n* Las llamadas API se realizan al sitio de Datadog EU\n* El período de tiempo predeterminado es de 1 hora para los estados del monitor.\n* Los límites de tamaño de página se establecen para manejar la mayoría de los casos de uso.","Spanish","es-ES",{"_34":60,"_38":64,"_43":65},{"_36":61,"_38":62,"_40":63},"Datadog からのモニターおよびクラスターログへのアクセスを提供する","データドッグ","# Datadog モデルコンテキストプロトコル (MCP) 🔍\n\n[![鍛冶屋のバッジ](https://smithery.ai/badge/@didlawowo/mcp-collection)](https://smithery.ai/server/@didlawowo/mcp-collection)\n\nDatadog APIと連携し、インフラストラクチャから監視データを取得するためのPythonベースのツールです。このMCPは、シンプルなインターフェースから監視状態やKubernetesログに簡単にアクセスできます。\n\n## Datadog の機能 🌟\n\n* **モニター状態追跡**: 特定のモニター状態を取得して分析する\n* **Kubernetes ログ分析**: Kubernetes クラスターからエラーログを抽出してフォーマットする\n\n## 前提条件 📋\n\n* Python 3.11以上\n* Datadog API およびアプリケーションキー (適切な権限付き)\n* Datadogサイトへのアクセス\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/@didlawowo/mcp-collection)経由で Claude Desktop 用の Datadog を自動的にインストールするには:\n\n```bash\nnpx -y @smithery/cli install @didlawowo/mcp-collection --client claude\n```\n\n必要なパッケージ:\n\n```text\ndatadog-api-client\nfastmcp\nloguru\nicecream\npython-dotenv\nuv\n```\n\n## 環境設定 🔑\n\nDatadog の認証情報を使用して`.env`ファイルを作成します。\n\n```env\nDD_API_KEY=your_api_key\nDD_APP_KEY=your_app_key\n```\n\n## MCP 用の Claude デスクトップセットアップをセットアップする 🖥️\n\n1. Claude Desktopをインストールする\n\n```bash\n# Assuming you're on macOS\nbrew install claude-desktop\n\n# Or download from official website\nhttps://claude.ai/desktop\n```\n\n2. Datadog MCP 構成をセットアップします。\n\n````bash\n# on mac is \n~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\n\n# Add this to your claude config json\n```json\n \"Datadog-MCP-Server\": {\n \"command\": \"uv\",\n \"args\": [\n \"run\",\n \"--with\",\n \"datadog-api-client\",\n \"--with\",\n \"fastmcp\",\n \"--with\",\n \"icecream\",\n \"--with\",\n \"loguru\",\n \"--with\",\n \"python-dotenv\",\n \"fastmcp\",\n \"run\",\n \"/your-path/mcp-collection/datadog/main.py\"\n ],\n \"env\": {\n \"DD_API_KEY\": \"xxxx\",\n \"DD_APP_KEY\": \"xxx\"\n }\n },\n````\n\n## 使用方法💻\n\n![ログを取得する](assets/logs.gif)\n\n![モニターを取得する](assets/monitor.gif)\n\n## 建築 🏗\n\n* **FastMCP Base** : ツール管理にFastMCPフレームワークを活用\n* **モジュラー設計**：モニターとログの機能を分離\n* **型安全性**: Python 型ヒントによる完全な型指定のサポート\n* **API 抽象化**: エラー処理を伴うラップされた Datadog API 呼び出し\n\nMCP と Claude Desktop のセットアップに関するセクションを追加します。\n\n# モデルコンテキストプロトコル (MCP) の紹介 🤖\n\n## MCPとは何ですか?\n\nモデルコンテキストプロトコル（MCP）は、AIモデルが外部ツールやAPIと標準化された方法で連携できるようにするフレームワークです。これにより、Claudeのようなモデルは以下が可能になります。\n\n* 外部データにアクセスする\n* コマンドを実行する\n* APIを操作する\n* 会話全体にわたって文脈を維持する\n\n## MCPサーバーの例\n\n\n\n## MCP セットアップのチュートリアル\n\n\n\n## 仕組み - 利用可能な機能 🛠️\n\nLLMは提供された関数を使用してデータを取得し、それを使用します\n\n### 1. モニターの状態を取得する\n\n```python\nget_monitor_states(\n name: str, # Monitor name to search\n timeframe: int = 1 # Hours to look back\n)\n```\n\n例：\n\n```python\n\nresponse = get_monitor_states(name=\"traefik\")\n\n# Sample Output\n{\n \"id\": \"12345678\",\n \"name\": \"traefik\",\n \"status\": \"OK\",\n \"query\": \"avg(last_5m):avg:traefik.response_time{*} > 1000\",\n \"message\": \"Response time is too high\",\n \"type\": \"metric alert\",\n \"created\": \"2024-01-14T10:00:00Z\",\n \"modified\": \"2024-01-14T15:30:00Z\"\n}\n```\n\n### 2. Kubernetesログを取得する\n\n```python\nget_k8s_logs(\n cluster: str, # Kubernetes cluster name\n timeframe: int = 5, # Hours to look back\n namespace: str = None # Optional namespace filter\n)\n```\n\n例：\n\n```python\nlogs = get_k8s_logs(\n cluster=\"prod-cluster\",\n timeframe=3,\n namespace=\"default\"\n)\n\n# Sample Output\n{\n \"timestamp\": \"2024-01-14T22:00:00Z\",\n \"host\": \"worker-1\",\n \"service\": \"nginx-ingress\",\n \"pod_name\": \"nginx-ingress-controller-abc123\",\n \"namespace\": \"default\",\n \"container_name\": \"controller\",\n \"message\": \"Connection refused\",\n \"status\": \"error\"\n}\n```\n\n```bash\n# Install as MCP extension\ncd datadog\ntask install-mcp\n```\n\n## 4. インストールの確認\n\n### クロードチャットデスクトップ\n\nクロードでデータドッグの接続を確認する\n\n![セットアップクロード](assets/config.png)\n\n## 5. Datadog MCPツールを使用する\n\n## セキュリティに関する考慮事項 🔒\n\n* APIキーを`.env`に保存する\n* MCPは隔離された環境で実行される\n* 各ツールには定義された権限があります\n* レート制限が実装されています\n\n## トラブルシューティング🔧\n\n### MCPインスペクターの使用\n\n```bash\n# Launch MCP Inspector for debugging\ntask run-mcp-inspector\n```\n\nMCP インスペクターは以下を提供します。\n\n* MCP サーバーのステータスをリアルタイムで表示\n* 関数呼び出しログ\n* エラートレース\n* API応答監視\n\n### よくある問題と解決策\n\n1. **API認証エラー**\n\n ```bash\n Error: (403) Forbidden\n ```\n\n ➡️ .env で DD\\_API\\_KEY と DD\\_APP\\_KEY を確認してください\n\n2. **MCP 接続の問題**\n\n ```bash\n Error: Failed to connect to MCP server\n ```\n\n ➡️ claude\\_desktop\\_config.json のパスと内容を確認する\n\n3. **モニターが見つかりません**\n\n ```bash\n Error: No monitor found with name 'xxx'\n ```\n\n ➡️ モニター名のスペルと大文字と小文字の区別を確認する\n\n4. **ログはここにあります**\n\n![代替テキスト](assets/logs.png)\n\n## 貢献中🤝\n\nお気軽にどうぞ:\n\n1. バグに関する未解決の問題\n2. 改善のためのPRを送信する\n3. 新機能の追加\n\n## ノート📝\n\n* API 呼び出しは Datadog EU サイトに対して行われます\n* モニター状態のデフォルトの時間枠は1時間です\n* ほとんどのユースケースに対応できるようにページサイズの制限が設定されています","Japanese","ja-JP",{"_34":67,"_38":71,"_43":72},{"_36":68,"_38":69,"_40":70},"datadog에서 모니터 및 클러스터 로그에 대한 액세스를 제공합니다.","데이터독","# Datadog 모델 컨텍스트 프로토콜(MCP) 🔍\n\n[![대장간 배지](https://smithery.ai/badge/@didlawowo/mcp-collection)](https://smithery.ai/server/@didlawowo/mcp-collection)\n\nDatadog API와 상호 작용하고 인프라에서 모니터링 데이터를 가져오는 Python 기반 도구입니다. 이 MCP는 간단한 인터페이스를 통해 모니터링 상태 및 쿠버네티스 로그에 쉽게 액세스할 수 있도록 합니다.\n\n## Datadog 기능 🌟\n\n* **모니터 상태 추적** : 특정 모니터 상태를 가져오고 분석합니다.\n* **Kubernetes 로그 분석** : Kubernetes 클러스터에서 오류 로그 추출 및 포맷\n\n## 필수 조건 📋\n\n* 파이썬 3.11+\n* Datadog API 및 애플리케이션 키(올바른 권한 포함)\n* Datadog 사이트 접속\n\n## 설치 🔧\n\n### Smithery를 통해 설치\n\n[Smithery를](https://smithery.ai/server/@didlawowo/mcp-collection) 통해 Claude Desktop용 Datadog를 자동으로 설치하려면:\n\n지엑스피1\n\n필요한 패키지:\n\n```text\ndatadog-api-client\nfastmcp\nloguru\nicecream\npython-dotenv\nuv\n```\n\n## 환경 설정 🔑\n\nDatadog 자격 증명으로 `.env` 파일을 만듭니다.\n\n```env\nDD_API_KEY=your_api_key\nDD_APP_KEY=your_app_key\n```\n\n## MCP용 Claude Desktop 설정 🖥️\n\n1. Claude Desktop 설치\n\n```bash\n# Assuming you're on macOS\nbrew install claude-desktop\n\n# Or download from official website\nhttps://claude.ai/desktop\n```\n\n2. Datadog MCP 구성을 설정합니다.\n\n````bash\n# on mac is \n~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\n\n# Add this to your claude config json\n```json\n \"Datadog-MCP-Server\": {\n \"command\": \"uv\",\n \"args\": [\n \"run\",\n \"--with\",\n \"datadog-api-client\",\n \"--with\",\n \"fastmcp\",\n \"--with\",\n \"icecream\",\n \"--with\",\n \"loguru\",\n \"--with\",\n \"python-dotenv\",\n \"fastmcp\",\n \"run\",\n \"/your-path/mcp-collection/datadog/main.py\"\n ],\n \"env\": {\n \"DD_API_KEY\": \"xxxx\",\n \"DD_APP_KEY\": \"xxx\"\n }\n },\n````\n\n## 사용법 💻\n\n![로그를 가져오다](assets/logs.gif)\n\n![모니터를 얻다](assets/monitor.gif)\n\n## 건축 🏗\n\n* **FastMCP 기반** : 도구 관리를 위한 FastMCP 프레임워크 활용\n* **모듈형 디자인** : 모니터와 로그에 대한 별도 기능\n* **유형 안전성** : Python 유형 힌트를 통한 전체 유형 지원\n* **API 추상화** : 오류 처리를 포함한 래핑된 Datadog API 호출\n\nMCP와 Claude Desktop 설정에 대한 섹션을 추가하겠습니다.\n\n# 모델 컨텍스트 프로토콜(MCP) 소개 🤖\n\n## MCP란 무엇인가요?\n\n모델 컨텍스트 프로토콜(MCP)은 AI 모델이 외부 도구 및 API와 표준화된 방식으로 상호 작용할 수 있도록 하는 프레임워크입니다. Claude와 같은 모델은 MCP를 통해 다음과 같은 작업을 수행할 수 있습니다.\n\n* 외부 데이터 접근\n* 명령 실행\n* API와 상호 작용\n* 대화 전반에 걸쳐 맥락 유지\n\n## MCP 서버의 몇 가지 예\n\n\n\n## MCP 설정을 위한 튜토리얼\n\n\n\n## 작동 원리 - 사용 가능한 기능 🛠️\n\nLLM은 제공된 함수를 사용하여 데이터를 가져와 사용합니다.\n\n### 1. 모니터 상태 가져오기\n\n```python\nget_monitor_states(\n name: str, # Monitor name to search\n timeframe: int = 1 # Hours to look back\n)\n```\n\n예:\n\n```python\n\nresponse = get_monitor_states(name=\"traefik\")\n\n# Sample Output\n{\n \"id\": \"12345678\",\n \"name\": \"traefik\",\n \"status\": \"OK\",\n \"query\": \"avg(last_5m):avg:traefik.response_time{*} > 1000\",\n \"message\": \"Response time is too high\",\n \"type\": \"metric alert\",\n \"created\": \"2024-01-14T10:00:00Z\",\n \"modified\": \"2024-01-14T15:30:00Z\"\n}\n```\n\n### 2. 쿠버네티스 로그 가져오기\n\n```python\nget_k8s_logs(\n cluster: str, # Kubernetes cluster name\n timeframe: int = 5, # Hours to look back\n namespace: str = None # Optional namespace filter\n)\n```\n\n예:\n\n```python\nlogs = get_k8s_logs(\n cluster=\"prod-cluster\",\n timeframe=3,\n namespace=\"default\"\n)\n\n# Sample Output\n{\n \"timestamp\": \"2024-01-14T22:00:00Z\",\n \"host\": \"worker-1\",\n \"service\": \"nginx-ingress\",\n \"pod_name\": \"nginx-ingress-controller-abc123\",\n \"namespace\": \"default\",\n \"container_name\": \"controller\",\n \"message\": \"Connection refused\",\n \"status\": \"error\"\n}\n```\n\n```bash\n# Install as MCP extension\ncd datadog\ntask install-mcp\n```\n\n## 4. 설치 확인\n\n### 클로드 채팅 데스크톱에서\n\n클로드에서 datadog 연결 확인\n\n![클로드 설정](assets/config.png)\n\n## 5. Datadog MCP 도구 사용\n\n## 보안 고려 사항 🔒\n\n* API 키를 `.env` 에 저장합니다.\n* MCP는 격리된 환경에서 실행됩니다.\n* 각 도구에는 정의된 권한이 있습니다.\n* 속도 제한이 구현되었습니다\n\n## 문제 해결 🔧\n\n### MCP Inspector 사용\n\n```bash\n# Launch MCP Inspector for debugging\ntask run-mcp-inspector\n```\n\nMCP 검사관은 다음을 제공합니다.\n\n* MCP 서버 상태 실시간 보기\n* 함수 호출 로그\n* 오류 추적\n* API 응답 모니터링\n\n### 일반적인 문제 및 해결책\n\n1. **API 인증 오류**\n\n ```bash\n Error: (403) Forbidden\n ```\n\n ➡️ .env에서 DD\\_API\\_KEY와 DD\\_APP\\_KEY를 확인하세요.\n\n2. **MCP 연결 문제**\n\n ```bash\n Error: Failed to connect to MCP server\n ```\n\n ➡️ claude\\_desktop\\_config.json 경로와 내용을 확인하세요.\n\n3. **모니터를 찾을 수 없습니다**\n\n ```bash\n Error: No monitor found with name 'xxx'\n ```\n\n ➡️ 모니터 이름의 철자와 대소문자 구분을 확인하세요\n\n4. **로그는 여기에서 찾을 수 있습니다**\n\n![대체 텍스트](assets/logs.png)\n\n## 기여하기 🤝\n\n자유롭게 하세요:\n\n1. 버그에 대한 공개 문제\n2. 개선을 위한 PR 제출\n3. 새로운 기능 추가\n\n## 노트 📝\n\n* Datadog EU 사이트에 대한 API 호출이 이루어집니다.\n* 모니터 상태의 기본 시간 프레임은 1시간입니다.\n* 페이지 크기 제한은 대부분의 사용 사례를 처리하도록 설정됩니다.","Korean","ko-KR",{"_34":74,"_38":78,"_43":79},{"_36":75,"_38":76,"_40":77},"предоставить доступ к журналам мониторинга и кластера из datadog","датадог","# Протокол контекста модели Datadog (MCP) 🔍\n\n[![значок кузнеца](https://smithery.ai/badge/@didlawowo/mcp-collection)](https://smithery.ai/server/@didlawowo/mcp-collection)\n\nИнструмент на основе Python для взаимодействия с API Datadog и извлечения данных мониторинга из вашей инфраструктуры. Этот MCP обеспечивает легкий доступ к состояниям мониторинга и журналам Kubernetes через простой интерфейс.\n\n## Возможности Datadog 🌟\n\n* **Отслеживание состояния монитора** : выборка и анализ определенных состояний монитора.\n* **Анализ журналов Kubernetes** : извлечение и форматирование журналов ошибок из кластеров Kubernetes\n\n## Предварительные условия 📋\n\n* Питон 3.11+\n* API Datadog и ключи приложений (с правильными разрешениями)\n* Доступ к сайту Datadog\n\n## Установка 🔧\n\n### Установка через Smithery\n\nЧтобы установить Datadog для Claude Desktop автоматически через [Smithery](https://smithery.ai/server/@didlawowo/mcp-collection) :\n\n```bash\nnpx -y @smithery/cli install @didlawowo/mcp-collection --client claude\n```\n\nНеобходимые пакеты:\n\n```text\ndatadog-api-client\nfastmcp\nloguru\nicecream\npython-dotenv\nuv\n```\n\n## Настройка среды 🔑\n\nСоздайте файл `.env` с вашими учетными данными Datadog:\n\n```env\nDD_API_KEY=your_api_key\nDD_APP_KEY=your_app_key\n```\n\n## Настройка Claude Desktop для MCP 🖥️\n\n1. Установить Клод Десктоп\n\n```bash\n# Assuming you're on macOS\nbrew install claude-desktop\n\n# Or download from official website\nhttps://claude.ai/desktop\n```\n\n2. Настройте конфигурацию Datadog MCP:\n\n````bash\n# on mac is \n~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\n\n# Add this to your claude config json\n```json\n \"Datadog-MCP-Server\": {\n \"command\": \"uv\",\n \"args\": [\n \"run\",\n \"--with\",\n \"datadog-api-client\",\n \"--with\",\n \"fastmcp\",\n \"--with\",\n \"icecream\",\n \"--with\",\n \"loguru\",\n \"--with\",\n \"python-dotenv\",\n \"fastmcp\",\n \"run\",\n \"/your-path/mcp-collection/datadog/main.py\"\n ],\n \"env\": {\n \"DD_API_KEY\": \"xxxx\",\n \"DD_APP_KEY\": \"xxx\"\n }\n },\n````\n\n## Использование 💻\n\n![получить журналы](assets/logs.gif)\n\n![получить монитор](assets/monitor.gif)\n\n## Архитектура 🏗\n\n* **FastMCP Base** : использует фреймворк FastMCP для управления инструментами\n* **Модульная конструкция** : отдельные функции для мониторов и журналов\n* **Безопасность типов** : полная поддержка ввода с подсказками типов Python\n* **Абстракция API** : обернутые вызовы API Datadog с обработкой ошибок\n\nЯ добавлю раздел о настройке MCP и Claude Desktop:\n\n# Введение в протокол контекста модели (MCP) 🤖\n\n## Что такое МКП?\n\nModel Context Protocol (MCP) — это фреймворк, позволяющий моделям ИИ взаимодействовать с внешними инструментами и API стандартизированным образом. Он позволяет таким моделям, как Клод:\n\n* Доступ к внешним данным\n* Выполнять команды\n* Взаимодействие с API\n* Сохраняйте контекст во время разговоров\n\n## некоторые примеры MCP-серверов\n\n\n\n## Учебник по настройке MCP\n\n\n\n## Как это работает - Доступные функции 🛠️\n\nLLM использует предоставленную функцию для получения данных и их использования\n\n### 1. Получить состояния монитора\n\n```python\nget_monitor_states(\n name: str, # Monitor name to search\n timeframe: int = 1 # Hours to look back\n)\n```\n\nПример:\n\n```python\n\nresponse = get_monitor_states(name=\"traefik\")\n\n# Sample Output\n{\n \"id\": \"12345678\",\n \"name\": \"traefik\",\n \"status\": \"OK\",\n \"query\": \"avg(last_5m):avg:traefik.response_time{*} > 1000\",\n \"message\": \"Response time is too high\",\n \"type\": \"metric alert\",\n \"created\": \"2024-01-14T10:00:00Z\",\n \"modified\": \"2024-01-14T15:30:00Z\"\n}\n```\n\n### 2. Получите логи Kubernetes\n\n```python\nget_k8s_logs(\n cluster: str, # Kubernetes cluster name\n timeframe: int = 5, # Hours to look back\n namespace: str = None # Optional namespace filter\n)\n```\n\nПример:\n\n```python\nlogs = get_k8s_logs(\n cluster=\"prod-cluster\",\n timeframe=3,\n namespace=\"default\"\n)\n\n# Sample Output\n{\n \"timestamp\": \"2024-01-14T22:00:00Z\",\n \"host\": \"worker-1\",\n \"service\": \"nginx-ingress\",\n \"pod_name\": \"nginx-ingress-controller-abc123\",\n \"namespace\": \"default\",\n \"container_name\": \"controller\",\n \"message\": \"Connection refused\",\n \"status\": \"error\"\n}\n```\n\n```bash\n# Install as MCP extension\ncd datadog\ntask install-mcp\n```\n\n## 4. Проверка установки\n\n### В чате Клода на рабочем столе\n\nпроверьте соединение datadog в claude\n\n![установка Клода](assets/config.png)\n\n## 5. Используйте инструменты Datadog MCP\n\n## Вопросы безопасности 🔒\n\n* Сохраните ключи API в `.env`\n* MCP работает в изолированной среде\n* Каждый инструмент имеет определенные разрешения.\n* Реализовано ограничение скорости\n\n## Устранение неполадок 🔧\n\n### Использование MCP-инспектора\n\n```bash\n# Launch MCP Inspector for debugging\ntask run-mcp-inspector\n```\n\nИнспектор MCP обеспечивает:\n\n* Просмотр состояния сервера MCP в реальном времени\n* Журналы вызовов функций\n* Отслеживание ошибок\n* Мониторинг ответа API\n\n### Распространенные проблемы и решения\n\n1. **Ошибки аутентификации API**\n\n ```bash\n Error: (403) Forbidden\n ```\n\n ➡️ Проверьте DD\\_API\\_KEY и DD\\_APP\\_KEY в .env\n\n2. **Проблемы с подключением MCP**\n\n ```bash\n Error: Failed to connect to MCP server\n ```\n\n ➡️ Проверьте путь и содержимое claude\\_desktop\\_config.json\n\n3. **Монитор не найден**\n\n ```bash\n Error: No monitor found with name 'xxx'\n ```\n\n ➡️ Проверьте правильность написания имени монитора и чувствительность к регистру\n\n4. **Журналы можно найти здесь**\n\n![альтернативный текст](assets/logs.png)\n\n## Вношу свой вклад 🤝\n\nНе стесняйтесь:\n\n1. Открытые вопросы по ошибкам\n2. Подавайте PR-заявки на улучшения\n3. Добавить новые функции\n\n## Заметки 📝\n\n* Вызовы API выполняются на сайт Datadog EU.\n* Временной интервал по умолчанию составляет 1 час для состояний монитора.\n* Ограничения на размер страницы установлены для большинства случаев использования.","Russian","ru-RU",{"_34":81,"_38":85,"_43":86},{"_36":82,"_38":83,"_40":84},"提供对来自 datadog 的监视器和集群日志的访问","数据狗","# Datadog 模型上下文协议 (MCP) 🔍\n\n[![铁匠徽章](https://smithery.ai/badge/@didlawowo/mcp-collection)](https://smithery.ai/server/@didlawowo/mcp-collection)\n\n一个基于 Python 的工具，用于与 Datadog API 交互并从您的基础架构获取监控数据。此 MCP 通过简单的界面提供对监控状态和 Kubernetes 日志的轻松访问。\n\n## Datadog 功能🌟\n\n* **监控状态跟踪**：获取并分析特定的监控状态\n* **Kubernetes 日志分析**：从 Kubernetes 集群中提取并格式化错误日志\n\n## 先决条件📋\n\n* Python 3.11+\n* Datadog API 和应用程序密钥（具有正确的权限）\n* 访问 Datadog 网站\n\n## 安装🔧\n\n### 通过 Smithery 安装\n\n要通过[Smithery](https://smithery.ai/server/@didlawowo/mcp-collection)自动为 Claude Desktop 安装 Datadog：\n\n```bash\nnpx -y @smithery/cli install @didlawowo/mcp-collection --client claude\n```\n\n所需软件包：\n\n```text\ndatadog-api-client\nfastmcp\nloguru\nicecream\npython-dotenv\nuv\n```\n\n## 环境设置🔑\n\n使用您的 Datadog 凭证创建一个`.env`文件：\n\n```env\nDD_API_KEY=your_api_key\nDD_APP_KEY=your_app_key\n```\n\n## 为 MCP 设置 Claude 桌面设置 🖥️\n\n1. 安装 Claude Desktop\n\n```bash\n# Assuming you're on macOS\nbrew install claude-desktop\n\n# Or download from official website\nhttps://claude.ai/desktop\n```\n\n2. 设置 Datadog MCP 配置：\n\n````bash\n# on mac is \n~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\n\n# Add this to your claude config json\n```json\n \"Datadog-MCP-Server\": {\n \"command\": \"uv\",\n \"args\": [\n \"run\",\n \"--with\",\n \"datadog-api-client\",\n \"--with\",\n \"fastmcp\",\n \"--with\",\n \"icecream\",\n \"--with\",\n \"loguru\",\n \"--with\",\n \"python-dotenv\",\n \"fastmcp\",\n \"run\",\n \"/your-path/mcp-collection/datadog/main.py\"\n ],\n \"env\": {\n \"DD_API_KEY\": \"xxxx\",\n \"DD_APP_KEY\": \"xxx\"\n }\n },\n````\n\n## 用法💻\n\n![获取日志](assets/logs.gif)\n\n![获取监视器](assets/monitor.gif)\n\n## 建筑🏗\n\n* **FastMCP Base** ：利用 FastMCP 框架进行工具管理\n* **模块化设计**：监视器和日志的独立功能\n* **类型安全**：通过 Python 类型提示提供全面的类型支持\n* **API 抽象**：包装 Datadog API 调用并进行错误处理\n\n我将添加有关 MCP 和 Claude Desktop 设置的部分：\n\n# 模型上下文协议（MCP）简介🤖\n\n## 什么是 MCP？\n\n模型上下文协议 (MCP) 是一个框架，允许 AI 模型以标准化的方式与外部工具和 API 交互。它使 Claude 等模型能够：\n\n* 访问外部数据\n* 执行命令\n* 与 API 交互\n* 在对话中保持上下文\n\n## MCP 服务器的一些示例\n\n\n\n## MCP 设置教程\n\n\n\n## 工作原理 - 可用功能🛠️\n\nLLM 使用提供的函数来获取数据并使用它\n\n### 1. 获取监控状态\n\n```python\nget_monitor_states(\n name: str, # Monitor name to search\n timeframe: int = 1 # Hours to look back\n)\n```\n\n例子：\n\n```python\n\nresponse = get_monitor_states(name=\"traefik\")\n\n# Sample Output\n{\n \"id\": \"12345678\",\n \"name\": \"traefik\",\n \"status\": \"OK\",\n \"query\": \"avg(last_5m):avg:traefik.response_time{*} > 1000\",\n \"message\": \"Response time is too high\",\n \"type\": \"metric alert\",\n \"created\": \"2024-01-14T10:00:00Z\",\n \"modified\": \"2024-01-14T15:30:00Z\"\n}\n```\n\n### 2. 获取 Kubernetes 日志\n\n```python\nget_k8s_logs(\n cluster: str, # Kubernetes cluster name\n timeframe: int = 5, # Hours to look back\n namespace: str = None # Optional namespace filter\n)\n```\n\n例子：\n\n```python\nlogs = get_k8s_logs(\n cluster=\"prod-cluster\",\n timeframe=3,\n namespace=\"default\"\n)\n\n# Sample Output\n{\n \"timestamp\": \"2024-01-14T22:00:00Z\",\n \"host\": \"worker-1\",\n \"service\": \"nginx-ingress\",\n \"pod_name\": \"nginx-ingress-controller-abc123\",\n \"namespace\": \"default\",\n \"container_name\": \"controller\",\n \"message\": \"Connection refused\",\n \"status\": \"error\"\n}\n```\n\n```bash\n# Install as MCP extension\ncd datadog\ntask install-mcp\n```\n\n## 4.验证安装\n\n### 在克劳德的聊天桌面\n\n检查 Claude 中的 Datadog 连接\n\n![设置克劳德](assets/config.png)\n\n## 5. 使用 Datadog MCP 工具\n\n## 安全注意事项\n\n* 将 API 密钥存储在`.env`中\n* MCP 在隔离环境中运行\n* 每个工具都有定义的权限\n* 实施速率限制\n\n## 故障排除\n\n### 使用 MCP 检查器\n\n```bash\n# Launch MCP Inspector for debugging\ntask run-mcp-inspector\n```\n\nMCP 检查器提供：\n\n* 实时查看MCP服务器状态\n* 函数调用日志\n* 错误追踪\n* API响应监控\n\n### 常见问题和解决方案\n\n1. **API 身份验证错误**\n\n ```bash\n Error: (403) Forbidden\n ```\n\n ➡️ 检查 .env 中的 DD\\_API\\_KEY 和 DD\\_APP\\_KEY\n\n2. **MCP 连接问题**\n\n ```bash\n Error: Failed to connect to MCP server\n ```\n\n ➡️ 验证你的 claude\\_desktop\\_config.json 路径和内容\n\n3. **未找到监视器**\n\n ```bash\n Error: No monitor found with name 'xxx'\n ```\n\n ➡️ 检查监视器名称拼写和大小写\n\n4. **日志可以在这里找到**\n\n![替代文本](assets/logs.png)\n\n## 贡献🤝\n\n请随意：\n\n1. 未解决的错误问题\n2. 提交 PR 以进行改进\n3. 添加新功能\n\n## 注释📝\n\n* 对 Datadog EU 站点进行 API 调用\n* 监控状态的默认时间范围为 1 小时\n* 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MCP directory API