[{"_1":2,"_944":-5,"_945":-5},"loaderData",{"_3":4,"_22":23,"_24":25,"_676":677},"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",7058884,"membership","referenceId","01971406-43e3-74e8-b880-2f000258007a","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_64":-5,"_65":28,"_29":66,"_595":596,"_675":210},"availableLocales",[28,40,46,52,58],{"_29":30,"_33":37,"_38":39},"mcpServer",{"_31":32,"_33":34,"_35":36},"description","Server for interacting with Honeycomb observability data. This server enables LLMs like Claude to directly analyze and query your Honeycomb datasets.","name","hny-mcp","readme","# Honeycomb MCP\n\nA [Model Context Protocol](https://modelcontextprotocol.io) server for interacting with Honeycomb observability data. This server enables LLMs like Claude to directly analyze and query your Honeycomb datasets across multiple environments.\n\n![Honeycomb MCP Logo](/img/logo.png)\n\n## Requirements\n\n- Node.js 18+\n- Honeycomb API key with full permissions:\n - Query access for analytics\n - Read access for SLOs and Triggers\n - Environment-level access for dataset operations\n\n Honeycomb MCP is effectively a complete alternative interface to Honeycomb, and thus you need broad permissions for the API.\n\n## Honeycomb Enterprise Only\n\nCurrently, this is only available for Honeycomb Enterprise customers.\n\n## How it works\n\nToday, this is a single server process **that you must run on your own computer**. It is not authenticated. All information uses STDIO between your client and the server.\n\n## Installation\n\n```bash\npnpm install\npnpm run build\n```\n\nThe build artifact goes into the `/build` folder.\n\n## Configuration\n\nTo use this MCP server, you need to provide Honeycomb API keys via environment variables in your MCP config.\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_API_KEY\": \"your_api_key\"\n }\n }\n }\n}\n```\n\nFor multiple environments:\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_ENV_PROD_API_KEY\": \"your_prod_api_key\",\n \"HONEYCOMB_ENV_STAGING_API_KEY\": \"your_staging_api_key\"\n }\n }\n }\n}\n```\n\n**Important:** These environment variables **must** bet set in the `env` block of your MCP config.\n\n### EU Configuration\n\nEU customers must also set a `HONEYCOMB_API_ENDPOINT` configuration, since the MCP defaults to the non-EU instance.\n\n```bash\n# Optional custom API endpoint (defaults to https://api.honeycomb.io)\nHONEYCOMB_API_ENDPOINT=https://api.eu1.honeycomb.io/\n```\n\n### Caching Configuration\n\nThe MCP server implements caching for all non-query Honeycomb API calls to improve performance and reduce API usage. Caching can be configured using these environment variables:\n\n```bash\n# Enable/disable caching (default: true)\nHONEYCOMB_CACHE_ENABLED=true\n\n# Default TTL in seconds (default: 300)\nHONEYCOMB_CACHE_DEFAULT_TTL=300\n\n# Resource-specific TTL values in seconds (defaults shown)\nHONEYCOMB_CACHE_DATASET_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_COLUMN_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_BOARD_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_SLO_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_TRIGGER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_MARKER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_RECIPIENT_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_AUTH_TTL=3600 # 1 hour\n\n# Maximum cache size (items per resource type)\nHONEYCOMB_CACHE_MAX_SIZE=1000\n```\n\n## Client compatibility\n\nHoneycomb MCP has been tested with the following clients:\n\n- [Claude Desktop](https://modelcontextprotocol.io/quickstart/user)\n- [Claude Code](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp)\n- [Cursor](https://docs.cursor.com/context/model-context-protocol)\n- [Windsurf](https://docs.codeium.com/windsurf/mcp)\n- [Goose](https://block.github.io/goose/docs/getting-started/using-extensions#mcp-servers)\n\nIt will likely work with other clients.\n\n## Features\n\n- Query Honeycomb datasets across multiple environments\n- Run analytics queries with support for:\n - Multiple calculation types (COUNT, AVG, P95, etc.)\n - Breakdowns and filters\n - Time-based analysis\n- Monitor SLOs and their status (Enterprise only)\n- Analyze columns and data patterns\n- View and analyze Triggers\n- Access dataset metadata and schema information\n- Optimized performance with TTL-based caching for all non-query API calls\n\n#### Resources\n\nAccess Honeycomb datasets using URIs in the format:\n`honeycomb://{environment}/{dataset}`\n\nFor example:\n- `honeycomb://production/api-requests`\n- `honeycomb://staging/backend-services`\n\nThe resource response includes:\n- Dataset name\n- Column information (name, type, description)\n- Schema details\n\n#### Tools\n\n- `list_datasets`: List all datasets in an environment\n ```json\n { \"environment\": \"production\" }\n ```\n\n- `get_columns`: Get column information for a dataset\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n- `run_query`: Run analytics queries with rich options\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n { \"op\": \"COUNT\" },\n { \"op\": \"P95\", \"column\": \"duration_ms\" }\n ],\n \"breakdowns\": [\"service.name\"],\n \"time_range\": 3600\n }\n ```\n\n- `analyze_columns`: Analyzes specific columns in a dataset by running statistical queries and returning computed metrics.\n\n- `list_slos`: List all SLOs for a dataset\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n- `get_slo`: Get detailed SLO information\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"sloId\": \"abc123\"\n }\n ```\n\n- `list_triggers`: List all triggers for a dataset\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n- `get_trigger`: Get detailed trigger information\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"triggerId\": \"xyz789\"\n }\n ```\n\n- `get_trace_link`: Generate a deep link to a specific trace in the Honeycomb UI\n \n- `get_instrumentation_help`: Provides OpenTelemetry instrumentation guidance\n ```json\n {\n \"language\": \"python\",\n \"filepath\": \"app/services/payment_processor.py\"\n }\n ```\n\n### Example Queries with Claude\n\nAsk Claude things like:\n\n- \"What datasets are available in the production environment?\"\n- \"Show me the P95 latency for the API service over the last hour\"\n- \"What's the error rate broken down by service name?\"\n- \"Are there any SLOs close to breaching their budget?\"\n- \"Show me all active triggers in the staging environment\"\n- \"What columns are available in the production API dataset?\"\n\n### Optimized Tool Responses\n\nAll tool responses are optimized to reduce context window usage while maintaining essential information:\n\n- **List datasets**: Returns only name, slug, and description\n- **Get columns**: Returns streamlined column information focusing on name, type, and description\n- **Run query**: \n - Includes actual results and necessary metadata\n - Adds automatically calculated summary statistics\n - Only includes series data for heatmap queries\n - Omits verbose metadata, links and execution details\n- **Analyze column**: \n - Returns top values, counts, and key statistics\n - Automatically calculates numeric metrics when appropriate\n- **SLO information**: Streamlined to key status indicators and performance metrics\n- **Trigger information**: Focused on trigger status, conditions, and notification targets\n\nThis optimization ensures that responses are concise but complete, allowing LLMs to process more data within context limitations.\n\n### Query Specification for `run_query`\n\nThe `run_query` tool supports a comprehensive query specification:\n\n- **calculations**: Array of operations to perform\n - Supported operations: COUNT, CONCURRENCY, COUNT_DISTINCT, HEATMAP, SUM, AVG, MAX, MIN, P001, P01, P05, P10, P25, P50, P75, P90, P95, P99, P999, RATE_AVG, RATE_SUM, RATE_MAX\n - Some operations like COUNT and CONCURRENCY don't require a column\n - Example: `{\"op\": \"HEATMAP\", \"column\": \"duration_ms\"}`\n\n- **filters**: Array of filter conditions\n - Supported operators: =, !=, >, >=, <, <=, starts-with, does-not-start-with, exists, does-not-exist, contains, does-not-contain, in, not-in\n - Example: `{\"column\": \"error\", \"op\": \"=\", \"value\": true}`\n\n- **filter_combination**: \"AND\" or \"OR\" (default is \"AND\")\n\n- **breakdowns**: Array of columns to group results by\n - Example: `[\"service.name\", \"http.status_code\"]`\n\n- **orders**: Array specifying how to sort results\n - Must reference columns from breakdowns or calculations\n - HEATMAP operation cannot be used in orders\n - Example: `{\"op\": \"COUNT\", \"order\": \"descending\"}`\n\n- **time_range**: Relative time range in seconds (e.g., 3600 for last hour)\n - Can be combined with either start_time or end_time but not both\n\n- **start_time** and **end_time**: UNIX timestamps for absolute time ranges\n\n- **having**: Filter results based on calculation values\n - Example: `{\"calculate_op\": \"COUNT\", \"op\": \">\", \"value\": 100}`\n\n### Example Queries\n\nHere are some real-world example queries:\n\n#### Find Slow API Calls\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"},\n {\"column\": \"duration_ms\", \"op\": \"MAX\"}\n ],\n \"filters\": [\n {\"column\": \"trace.parent_id\", \"op\": \"does-not-exist\"}\n ],\n \"breakdowns\": [\"http.target\", \"name\"],\n \"orders\": [\n {\"column\": \"duration_ms\", \"op\": \"MAX\", \"order\": \"descending\"}\n ]\n}\n```\n\n#### Distribution of DB Calls (Last Week)\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"}\n ],\n \"filters\": [\n {\"column\": \"db.statement\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"db.statement\"],\n \"time_range\": 604800\n}\n```\n\n#### Exception Count by Exception and Caller\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"op\": \"COUNT\"}\n ],\n \"filters\": [\n {\"column\": \"exception.message\", \"op\": \"exists\"},\n {\"column\": \"parent_name\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"exception.message\", \"parent_name\"],\n \"orders\": [\n {\"op\": \"COUNT\", \"order\": \"descending\"}\n ]\n}\n```\n\n## Development\n\n```bash\npnpm install\npnpm run build\n```\n\n## License\n\nMIT\n","English","tag","en-US",{"_29":41,"_33":44,"_38":45},{"_31":42,"_33":34,"_35":43},"Servidor para interactuar con los datos de observabilidad de Honeycomb. Este servidor permite a los LLM como Claude analizar y consultar directamente sus conjuntos de datos de Honeycomb.","# MCP de panal\n\nUn servidor [de Protocolo de Contexto de Modelo](https://modelcontextprotocol.io) para interactuar con los datos de observabilidad de Honeycomb. Este servidor permite a los LLM como Claude analizar y consultar directamente sus conjuntos de datos de Honeycomb en múltiples entornos.\n\n![Logotipo de MCP en forma de panal](/img/logo.png)\n\n## Requisitos\n\n* Node.js 18+\n* Clave API de Honeycomb con permisos completos:\n * Acceso a consultas para análisis\n * Acceso de lectura para SLO y activadores\n * Acceso a nivel de entorno para operaciones de conjuntos de datos\n\nHoneycomb MCP es efectivamente una interfaz alternativa completa a Honeycomb, por lo que necesita permisos amplios para la API.\n\n## Solo Honeycomb Enterprise\n\nActualmente, esto solo está disponible para clientes de Honeycomb Enterprise.\n\n## Cómo funciona\n\nActualmente, este es un proceso de servidor único **que debe ejecutar en su propia computadora** . No está autenticado. Toda la información entre su cliente y el servidor utiliza STDIO.\n\n## Instalación\n\n```bash\npnpm install\npnpm run build\n```\n\nEl artefacto de compilación va a la carpeta `/build` .\n\n## Configuración\n\nPara utilizar este servidor MCP, debe proporcionar claves de API de Honeycomb a través de variables de entorno en su configuración de MCP.\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_API_KEY\": \"your_api_key\"\n }\n }\n }\n}\n```\n\nPara múltiples entornos:\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_ENV_PROD_API_KEY\": \"your_prod_api_key\",\n \"HONEYCOMB_ENV_STAGING_API_KEY\": \"your_staging_api_key\"\n }\n }\n }\n}\n```\n\n**Importante:** Estas variables de entorno **deben** configurarse en el bloque `env` de su configuración de MCP.\n\n### Configuración de la UE\n\nLos clientes de la UE también deben establecer una configuración `HONEYCOMB_API_ENDPOINT` , ya que el MCP tiene como opción predeterminada la instancia que no es de la UE.\n\n```bash\n# Optional custom API endpoint (defaults to https://api.honeycomb.io)\nHONEYCOMB_API_ENDPOINT=https://api.eu1.honeycomb.io/\n```\n\n### Configuración de almacenamiento en caché\n\nEl servidor MCP implementa el almacenamiento en caché para todas las llamadas a la API de Honeycomb que no sean de consulta para mejorar el rendimiento y reducir el uso de la API. El almacenamiento en caché se puede configurar mediante estas variables de entorno:\n\n```bash\n# Enable/disable caching (default: true)\nHONEYCOMB_CACHE_ENABLED=true\n\n# Default TTL in seconds (default: 300)\nHONEYCOMB_CACHE_DEFAULT_TTL=300\n\n# Resource-specific TTL values in seconds (defaults shown)\nHONEYCOMB_CACHE_DATASET_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_COLUMN_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_BOARD_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_SLO_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_TRIGGER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_MARKER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_RECIPIENT_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_AUTH_TTL=3600 # 1 hour\n\n# Maximum cache size (items per resource type)\nHONEYCOMB_CACHE_MAX_SIZE=1000\n```\n\n## Compatibilidad del cliente\n\nHoneycomb MCP se ha probado con los siguientes clientes:\n\n* [Escritorio de Claude](https://modelcontextprotocol.io/quickstart/user)\n* [Claude Code](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp)\n* [Cursor](https://docs.cursor.com/context/model-context-protocol)\n* [Windsurf](https://docs.codeium.com/windsurf/mcp)\n* [Ganso](https://block.github.io/goose/docs/getting-started/using-extensions#mcp-servers)\n\nProbablemente funcionará con otros clientes.\n\n## Características\n\n* Consultar conjuntos de datos de Honeycomb en múltiples entornos\n* Ejecute consultas analíticas con soporte para:\n * Múltiples tipos de cálculo (COUNT, AVG, P95, etc.)\n * Averías y filtros\n * Análisis basado en el tiempo\n* Supervisar los SLO y su estado (solo Enterprise)\n* Analizar columnas y patrones de datos\n* Ver y analizar activadores\n* Acceder a los metadatos del conjunto de datos y a la información del esquema\n* Rendimiento optimizado con almacenamiento en caché basado en TTL para todas las llamadas API que no sean de consulta\n\n#### Recursos\n\nAcceda a los conjuntos de datos de Honeycomb mediante URI en el formato: `honeycomb://{environment}/{dataset}`\n\nPor ejemplo:\n\n* `honeycomb://production/api-requests`\n* `honeycomb://staging/backend-services`\n\nLa respuesta de recursos incluye:\n\n* Nombre del conjunto de datos\n* Información de la columna (nombre, tipo, descripción)\n* Detalles del esquema\n\n#### Herramientas\n\n* `list_datasets` : enumera todos los conjuntos de datos en un entorno\n\n ```json\n { \"environment\": \"production\" }\n ```\n\n* `get_columns` : Obtener información de la columna para un conjunto de datos\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `run_query` : Ejecuta consultas analíticas con opciones enriquecidas\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n { \"op\": \"COUNT\" },\n { \"op\": \"P95\", \"column\": \"duration_ms\" }\n ],\n \"breakdowns\": [\"service.name\"],\n \"time_range\": 3600\n }\n ```\n\n* `analyze_columns` : analiza columnas específicas en un conjunto de datos ejecutando consultas estadísticas y devolviendo métricas calculadas.\n\n* `list_slos` : enumera todos los SLO para un conjunto de datos\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `get_slo` : Obtener información detallada de SLO\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"sloId\": \"abc123\"\n }\n ```\n\n* `list_triggers` : enumera todos los activadores de un conjunto de datos\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `get_trigger` : Obtener información detallada del disparador\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"triggerId\": \"xyz789\"\n }\n ```\n\n* `get_trace_link` : genera un enlace profundo a un seguimiento específico en la interfaz de usuario de Honeycomb\n\n* `get_instrumentation_help` : Proporciona orientación sobre la instrumentación de OpenTelemetry\n\n ```json\n {\n \"language\": \"python\",\n \"filepath\": \"app/services/payment_processor.py\"\n }\n ```\n\n### Ejemplos de consultas con Claude\n\nPregúntale a Claude cosas como:\n\n* \"¿Qué conjuntos de datos están disponibles en el entorno de producción?\"\n* \"Muéstrame la latencia P95 del servicio API durante la última hora\"\n* \"¿Cuál es la tasa de error desglosada por nombre de servicio?\"\n* ¿Hay algún SLO que esté a punto de superar su presupuesto?\n* \"Muéstrame todos los activadores activos en el entorno de prueba\"\n* \"¿Qué columnas están disponibles en el conjunto de datos de la API de producción?\"\n\n### Respuestas optimizadas de la herramienta\n\nTodas las respuestas de la herramienta están optimizadas para reducir el uso de la ventana de contexto y al mismo tiempo mantener la información esencial:\n\n* **Lista de conjuntos de datos** : devuelve solo nombre, slug y descripción\n* **Obtener columnas** : devuelve información de columna simplificada centrándose en el nombre, el tipo y la descripción\n* **Ejecutar consulta** :\n * Incluye resultados reales y metadatos necesarios\n * Agrega estadísticas de resumen calculadas automáticamente\n * Solo incluye datos de series para consultas de mapas de calor\n * Omite metadatos detallados, enlaces y detalles de ejecución.\n* **Analizar columna** :\n * Devuelve valores superiores, recuentos y estadísticas clave\n * Calcula automáticamente métricas numéricas cuando corresponde\n* **Información de SLO** : optimizada para indicadores de estado clave y métricas de rendimiento\n* **Información del disparador** : centrada en el estado del disparador, las condiciones y los objetivos de notificación\n\nEsta optimización garantiza que las respuestas sean concisas pero completas, lo que permite a los LLM procesar más datos dentro de las limitaciones del contexto.\n\n### Especificación de consulta para `run_query`\n\nLa herramienta `run_query` admite una especificación de consulta integral:\n\n* **cálculos** : Matriz de operaciones a realizar\n\n * Operaciones admitidas: CONTAR, CONCURRENCIA, CONTAR\\_DISTINTOS, MAPA DE CALOR, SUMA, PROMEDIO, MÁXIMO, MÍNIMO, P001, P01, P05, P10, P25, P50, P75, P90, P95, P99, P999, TASA\\_PROMEDIO, TASA\\_SUMA, TASA\\_MÁXIMA\n * Algunas operaciones como COUNT y CONCURRENCY no requieren una columna\n * Ejemplo: `{\"op\": \"HEATMAP\", \"column\": \"duration_ms\"}`\n\n* **filtros** : Matriz de condiciones de filtro\n\n * Operadores admitidos: =, !=, >, >=, <, <=, empieza por, no empieza por, existe, no existe, contiene, no contiene, en, no en\n * Ejemplo: `{\"column\": \"error\", \"op\": \"=\", \"value\": true}`\n\n* **filter\\_combination** : \"AND\" o \"OR\" (el valor predeterminado es \"AND\")\n\n* **Desgloses** : Matriz de columnas para agrupar los resultados por\n\n * Ejemplo: `[\"service.name\", \"http.status_code\"]`\n\n* **pedidos** : Matriz que especifica cómo ordenar los resultados\n\n * Debe hacer referencia a columnas de desgloses o cálculos\n * La operación HEATMAP no se puede utilizar en pedidos\n * Ejemplo: `{\"op\": \"COUNT\", \"order\": \"descending\"}`\n\n* **time\\_range** : rango de tiempo relativo en segundos (por ejemplo, 3600 para la última hora)\n\n * Se puede combinar con start\\_time o end\\_time, pero no con ambos.\n\n* **start\\_time** y **end\\_time** : marcas de tiempo UNIX para rangos de tiempo absolutos\n\n* **tener** : Filtrar resultados según valores de cálculo\n\n * Ejemplo: `{\"calculate_op\": \"COUNT\", \"op\": \">\", \"value\": 100}`\n\n### Consultas de ejemplo\n\nA continuación se muestran algunos ejemplos de consultas del mundo real:\n\n#### Detectar llamadas API lentas\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"},\n {\"column\": \"duration_ms\", \"op\": \"MAX\"}\n ],\n \"filters\": [\n {\"column\": \"trace.parent_id\", \"op\": \"does-not-exist\"}\n ],\n \"breakdowns\": [\"http.target\", \"name\"],\n \"orders\": [\n {\"column\": \"duration_ms\", \"op\": \"MAX\", \"order\": \"descending\"}\n ]\n}\n```\n\n#### Distribución de llamadas a bases de datos (semana pasada)\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"}\n ],\n \"filters\": [\n {\"column\": \"db.statement\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"db.statement\"],\n \"time_range\": 604800\n}\n```\n\n#### Recuento de excepciones por excepción y llamador\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"op\": \"COUNT\"}\n ],\n \"filters\": [\n {\"column\": \"exception.message\", \"op\": \"exists\"},\n {\"column\": \"parent_name\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"exception.message\", \"parent_name\"],\n \"orders\": [\n {\"op\": \"COUNT\", \"order\": \"descending\"}\n ]\n}\n```\n\n## Desarrollo\n\n```bash\npnpm install\npnpm run build\n```\n\n## Licencia\n\nInstituto Tecnológico de Massachusetts (MIT)","Spanish","es-ES",{"_29":47,"_33":50,"_38":51},{"_31":48,"_33":34,"_35":49},"Honeycombの可観測性データとやり取りするためのサーバー。このサーバーにより、ClaudeのようなLLMはHoneycombデータセットを直接分析およびクエリできるようになります。","# ハニカムMCP\n\nHoneycombの可観測性データとやり取りするための[モデルコンテキストプロトコル](https://modelcontextprotocol.io)サーバー。このサーバーにより、ClaudeのようなLLMは、複数の環境にまたがるHoneycombデータセットを直接分析およびクエリできるようになります。\n\n![ハニカムMCPロゴ](/img/logo.png)\n\n## 要件\n\n* Node.js 18歳以上\n* 完全な権限を持つ Honeycomb API キー:\n * 分析のためのクエリアクセス\n * SLO とトリガーの読み取りアクセス\n * データセット操作の環境レベルのアクセス\n\nHoneycomb MCP は実質的に Honeycomb の完全な代替インターフェースであるため、API に対する広範な権限が必要です。\n\n## ハニカムエンタープライズのみ\n\n現在、これは Honeycomb Enterprise の顧客のみが利用できます。\n\n## 仕組み\n\n現在、これは単一のサーバープロセスであり\\*\\*、ご自身のコンピュータ上で実行する必要があります\\*\\*。認証は不要です。クライアントとサーバー間のすべての情報はSTDIOを介して送信されます。\n\n## インストール\n\n```bash\npnpm install\npnpm run build\n```\n\nビルド成果物は`/build`フォルダーに保存されます。\n\n## 構成\n\nこの MCP サーバーを使用するには、MCP 構成の環境変数を介して Honeycomb API キーを提供する必要があります。\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_API_KEY\": \"your_api_key\"\n }\n }\n }\n}\n```\n\n複数の環境の場合:\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_ENV_PROD_API_KEY\": \"your_prod_api_key\",\n \"HONEYCOMB_ENV_STAGING_API_KEY\": \"your_staging_api_key\"\n }\n }\n }\n}\n```\n\n**重要:**これらの環境変数は、MCP 構成の`env`ブロックで設定する**必要があります**。\n\n### EU構成\n\nMCP はデフォルトで非 EU インスタンスに設定されるため、EU のお客様は`HONEYCOMB_API_ENDPOINT`構成も設定する必要があります。\n\n```bash\n# Optional custom API endpoint (defaults to https://api.honeycomb.io)\nHONEYCOMB_API_ENDPOINT=https://api.eu1.honeycomb.io/\n```\n\n### キャッシュ構成\n\nMCPサーバーは、パフォーマンスの向上とAPI使用量の削減のため、クエリ以外のすべてのHoneycomb API呼び出しにキャッシュを実装しています。キャッシュは以下の環境変数で設定できます。\n\n```bash\n# Enable/disable caching (default: true)\nHONEYCOMB_CACHE_ENABLED=true\n\n# Default TTL in seconds (default: 300)\nHONEYCOMB_CACHE_DEFAULT_TTL=300\n\n# Resource-specific TTL values in seconds (defaults shown)\nHONEYCOMB_CACHE_DATASET_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_COLUMN_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_BOARD_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_SLO_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_TRIGGER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_MARKER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_RECIPIENT_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_AUTH_TTL=3600 # 1 hour\n\n# Maximum cache size (items per resource type)\nHONEYCOMB_CACHE_MAX_SIZE=1000\n```\n\n## クライアントの互換性\n\nHoneycomb MCP は次のクライアントでテストされています。\n\n* [クロードデスクトップ](https://modelcontextprotocol.io/quickstart/user)\n* [クロード・コード](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp)\n* [カーソル](https://docs.cursor.com/context/model-context-protocol)\n* [ウィンドサーフィン](https://docs.codeium.com/windsurf/mcp)\n* [ガチョウ](https://block.github.io/goose/docs/getting-started/using-extensions#mcp-servers)\n\n他のクライアントでも動作する可能性が高いです。\n\n## 特徴\n\n* 複数の環境にまたがる Honeycomb データセットのクエリ\n* 以下をサポートする分析クエリを実行します。\n * 複数の計算タイプ（COUNT、AVG、P95など）\n * 内訳とフィルター\n * 時間ベースの分析\n* SLO とそのステータスを監視する (エンタープライズのみ)\n* 列とデータパターンを分析する\n* トリガーの表示と分析\n* データセットのメタデータとスキーマ情報にアクセスする\n* すべての非クエリ API 呼び出しに対して TTL ベースのキャッシュによるパフォーマンスの最適化\n\n#### リソース\n\nHoneycomb データセットには、次の形式の URI を使用してアクセスします: `honeycomb://{environment}/{dataset}`\n\n例えば：\n\n* `honeycomb://production/api-requests`\n* `honeycomb://staging/backend-services`\n\nリソース応答には次のものが含まれます。\n\n* データセット名\n* 列情報（名前、タイプ、説明）\n* スキーマの詳細\n\n#### ツール\n\n* `list_datasets` : 環境内のすべてのデータセットを一覧表示する\n\n ```json\n { \"environment\": \"production\" }\n ```\n\n* `get_columns` : データセットの列情報を取得する\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `run_query` : 豊富なオプションを備えた分析クエリを実行する\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n { \"op\": \"COUNT\" },\n { \"op\": \"P95\", \"column\": \"duration_ms\" }\n ],\n \"breakdowns\": [\"service.name\"],\n \"time_range\": 3600\n }\n ```\n\n* `analyze_columns` : 統計クエリを実行し、計算されたメトリックを返すことで、データセット内の特定の列を分析します。\n\n* `list_slos` : データセットのすべての SLO をリストします。\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `get_slo` : 詳細なSLO情報を取得する\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"sloId\": \"abc123\"\n }\n ```\n\n* `list_triggers` : データセットのすべてのトリガーを一覧表示する\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `get_trigger` : 詳細なトリガー情報を取得する\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"triggerId\": \"xyz789\"\n }\n ```\n\n* `get_trace_link` : Honeycomb UI 内の特定のトレースへのディープリンクを生成します。\n\n* `get_instrumentation_help` : OpenTelemetry計測のガイダンスを提供します\n\n ```json\n {\n \"language\": \"python\",\n \"filepath\": \"app/services/payment_processor.py\"\n }\n ```\n\n### クロードとのクエリ例\n\nクロードに次のような質問をしてください:\n\n* 「本番環境で利用できるデータセットは何ですか？」\n* 「過去 1 時間の API サービスの P95 レイテンシを表示してください」\n* 「サービス名別のエラー率はどれくらいですか？」\n* 「予算を超過しそうな SLO はありますか?」\n* 「ステージング環境内のすべてのアクティブなトリガーを表示する」\n* 「本番環境の API データセットではどのような列が利用できますか？」\n\n### 最適化されたツール応答\n\nすべてのツール応答は、重要な情報を維持しながらコンテキストウィンドウの使用を減らすように最適化されています。\n\n* **データセットの一覧**: 名前、スラッグ、説明のみを返します\n* **列を取得**: 名前、タイプ、説明に焦点を当てた合理化された列情報を返します\n* **クエリを実行**:\n * 実際の結果と必要なメタデータが含まれています\n * 自動的に計算された要約統計を追加します\n * ヒートマップクエリのシリーズデータのみが含まれます\n * 詳細なメタデータ、リンク、実行の詳細を省略します\n* **分析列**:\n * 上位の値、カウント、主要な統計を返します\n * 適切な場合に数値メトリックを自動的に計算します\n* **SLO情報**: 主要なステータス指標とパフォーマンス指標に合理化されています\n* **トリガー情報**: トリガーの状態、条件、通知対象に焦点を当てています\n\nこの最適化により、応答は簡潔でありながら完全となり、LLM はコンテキスト制限内でより多くのデータを処理できるようになります。\n\n### `run_query`のクエリ仕様\n\n`run_query`ツールは包括的なクエリ仕様をサポートします。\n\n* **計算**: 実行する演算の配列\n\n * サポートされている演算: COUNT、CONCURRENCY、COUNT\\_DISTINCT、HEATMAP、SUM、AVG、MAX、MIN、P001、P01、P05、P10、P25、P50、P75、P90、P95、P99、P999、RATE\\_AVG、RATE\\_SUM、RATE\\_MAX\n * COUNTやCONCURRENCYなどの一部の操作では列は必要ありません\n * 例: `{\"op\": \"HEATMAP\", \"column\": \"duration_ms\"}`\n\n* **フィルター**: フィルター条件の配列\n\n * サポートされている演算子: =、!=、>、>=、<、<=、starts-with、does-not-start-with、exists、does-not-exist、contains、does-not-contain、in、not-in\n * 例: `{\"column\": \"error\", \"op\": \"=\", \"value\": true}`\n\n* **filter\\_combination** : 「AND」または「OR」（デフォルトは「AND」）\n\n* **内訳**: 結果をグループ化する列の配列\n\n * 例: `[\"service.name\", \"http.status_code\"]`\n\n* **順序**: 結果の並べ替え方法を指定する配列\n\n * 内訳または計算から列を参照する必要があります\n * HEATMAP操作は注文では使用できません\n * 例: `{\"op\": \"COUNT\", \"order\": \"descending\"}`\n\n* **time\\_range** : 相対的な時間範囲（秒単位）（例：過去1時間の場合は3600）\n\n * start\\_time または end\\_time のいずれかと組み合わせることができますが、両方と組み合わせることはできません。\n\n* **start\\_time**と**end\\_time** : 絶対時間範囲の UNIX タイムスタンプ\n\n* **有**: 計算値に基づいて結果をフィルタリングする\n\n * 例: `{\"calculate_op\": \"COUNT\", \"op\": \">\", \"value\": 100}`\n\n### クエリの例\n\n実際のクエリの例を次に示します。\n\n#### 遅いAPI呼び出しを見つける\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"},\n {\"column\": \"duration_ms\", \"op\": \"MAX\"}\n ],\n \"filters\": [\n {\"column\": \"trace.parent_id\", \"op\": \"does-not-exist\"}\n ],\n \"breakdowns\": [\"http.target\", \"name\"],\n \"orders\": [\n {\"column\": \"duration_ms\", \"op\": \"MAX\", \"order\": \"descending\"}\n ]\n}\n```\n\n#### DBコールの分布（先週）\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"}\n ],\n \"filters\": [\n {\"column\": \"db.statement\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"db.statement\"],\n \"time_range\": 604800\n}\n```\n\n#### 例外と呼び出し元別の例外数\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"op\": \"COUNT\"}\n ],\n \"filters\": [\n {\"column\": \"exception.message\", \"op\": \"exists\"},\n {\"column\": \"parent_name\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"exception.message\", \"parent_name\"],\n \"orders\": [\n {\"op\": \"COUNT\", \"order\": \"descending\"}\n ]\n}\n```\n\n## 発達\n\n```bash\npnpm install\npnpm run build\n```\n\n## ライセンス\n\nマサチューセッツ工科大学","Japanese","ja-JP",{"_29":53,"_33":56,"_38":57},{"_31":54,"_33":34,"_35":55},"Honeycomb 관측 가능성 데이터와 상호 작용하는 서버입니다. 이 서버를 통해 Claude와 같은 LLM이 Honeycomb 데이터 세트를 직접 분석하고 쿼리할 수 있습니다.","# 허니콤 MCP\n\nHoneycomb 관측 데이터와 상호작용하기 위한 [모델 컨텍스트 프로토콜](https://modelcontextprotocol.io) 서버입니다. 이 서버를 통해 Claude와 같은 LLM은 여러 환경에서 Honeycomb 데이터 세트를 직접 분석하고 쿼리할 수 있습니다.\n\n![허니콤 MCP 로고](/img/logo.png)\n\n## 요구 사항\n\n* 노드.js 18+\n* 전체 권한이 있는 Honeycomb API 키:\n * 분석을 위한 쿼리 액세스\n * SLO 및 트리거에 대한 읽기 액세스\n * 데이터 세트 작업을 위한 환경 수준 액세스\n\nHoneycomb MCP는 사실상 Honeycomb에 대한 완벽한 대체 인터페이스이므로 API에 대한 광범위한 권한이 필요합니다.\n\n## 허니컴 엔터프라이즈 전용\n\n현재 이 기능은 Honeycomb Enterprise 고객에게만 제공됩니다.\n\n## 작동 원리\n\n현재 이는 **사용자 컴퓨터에서 실행해야 하는** 단일 서버 프로세스입니다. 인증되지 않습니다. 모든 정보는 클라이언트와 서버 간에 STDIO를 사용합니다.\n\n## 설치\n\n지엑스피1\n\n빌드 아티팩트는 `/build` 폴더로 이동합니다.\n\n## 구성\n\n이 MCP 서버를 사용하려면 MCP 구성의 환경 변수를 통해 Honeycomb API 키를 제공해야 합니다.\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_API_KEY\": \"your_api_key\"\n }\n }\n }\n}\n```\n\n여러 환경의 경우:\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_ENV_PROD_API_KEY\": \"your_prod_api_key\",\n \"HONEYCOMB_ENV_STAGING_API_KEY\": \"your_staging_api_key\"\n }\n }\n }\n}\n```\n\n**중요:** 이러한 환경 변수는 MCP 구성의 `env` 블록에서 설정 **해야 합니다** .\n\n### EU 구성\n\nMCP가 기본적으로 비EU 인스턴스를 사용하므로 EU 고객도 `HONEYCOMB_API_ENDPOINT` 구성을 설정해야 합니다.\n\n```bash\n# Optional custom API endpoint (defaults to https://api.honeycomb.io)\nHONEYCOMB_API_ENDPOINT=https://api.eu1.honeycomb.io/\n```\n\n### 캐싱 구성\n\nMCP 서버는 성능을 향상시키고 API 사용량을 줄이기 위해 모든 비쿼리 Honeycomb API 호출에 대한 캐싱을 구현합니다. 캐싱은 다음 환경 변수를 사용하여 구성할 수 있습니다.\n\n```bash\n# Enable/disable caching (default: true)\nHONEYCOMB_CACHE_ENABLED=true\n\n# Default TTL in seconds (default: 300)\nHONEYCOMB_CACHE_DEFAULT_TTL=300\n\n# Resource-specific TTL values in seconds (defaults shown)\nHONEYCOMB_CACHE_DATASET_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_COLUMN_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_BOARD_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_SLO_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_TRIGGER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_MARKER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_RECIPIENT_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_AUTH_TTL=3600 # 1 hour\n\n# Maximum cache size (items per resource type)\nHONEYCOMB_CACHE_MAX_SIZE=1000\n```\n\n## 클라이언트 호환성\n\nHoneycomb MCP는 다음 클라이언트에서 테스트되었습니다.\n\n* [클로드 데스크탑](https://modelcontextprotocol.io/quickstart/user)\n* [클로드 코드](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp)\n* [커서](https://docs.cursor.com/context/model-context-protocol)\n* [윈드서핑](https://docs.codeium.com/windsurf/mcp)\n* [거위](https://block.github.io/goose/docs/getting-started/using-extensions#mcp-servers)\n\n다른 클라이언트에서도 효과가 있을 가능성이 높습니다.\n\n## 특징\n\n* 여러 환경에서 Honeycomb 데이터 세트 쿼리\n* 다음을 지원하여 분석 쿼리를 실행합니다.\n * 다양한 계산 유형(COUNT, AVG, P95 등)\n * 분석 및 필터\n * 시간 기반 분석\n* SLO 및 상태 모니터링(Enterprise 전용)\n* 열과 데이터 패턴 분석\n* 트리거 보기 및 분석\n* 데이터 세트 메타데이터 및 스키마 정보에 액세스합니다.\n* 모든 비쿼리 API 호출에 대해 TTL 기반 캐싱을 사용하여 성능을 최적화했습니다.\n\n#### 자원\n\n다음 형식의 URI를 사용하여 Honeycomb 데이터 세트에 액세스합니다: `honeycomb://{environment}/{dataset}`\n\n예를 들어:\n\n* `honeycomb://production/api-requests`\n* `honeycomb://staging/backend-services`\n\n리소스 응답에는 다음이 포함됩니다.\n\n* 데이터 세트 이름\n* 열 정보(이름, 유형, 설명)\n* 스키마 세부 정보\n\n#### 도구\n\n* `list_datasets` : 환경의 모든 데이터 세트를 나열합니다.\n\n ```json\n { \"environment\": \"production\" }\n ```\n\n* `get_columns` : 데이터 세트의 열 정보를 가져옵니다.\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `run_query` : 다양한 옵션을 사용하여 분석 쿼리 실행\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n { \"op\": \"COUNT\" },\n { \"op\": \"P95\", \"column\": \"duration_ms\" }\n ],\n \"breakdowns\": [\"service.name\"],\n \"time_range\": 3600\n }\n ```\n\n* `analyze_columns` : 통계 쿼리를 실행하고 계산된 메트릭을 반환하여 데이터 세트의 특정 열을 분석합니다.\n\n* `list_slos` : 데이터세트의 모든 SLO를 나열합니다.\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `get_slo` : 자세한 SLO 정보 가져오기\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"sloId\": \"abc123\"\n }\n ```\n\n* `list_triggers` : 데이터 세트의 모든 트리거를 나열합니다.\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `get_trigger` : 자세한 트리거 정보를 가져옵니다.\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"triggerId\": \"xyz789\"\n }\n ```\n\n* `get_trace_link` : Honeycomb UI에서 특정 추적에 대한 딥 링크를 생성합니다.\n\n* `get_instrumentation_help` : OpenTelemetry 계측 지침을 제공합니다.\n\n ```json\n {\n \"language\": \"python\",\n \"filepath\": \"app/services/payment_processor.py\"\n }\n ```\n\n### Claude를 사용한 예제 쿼리\n\n클로드에게 다음과 같은 질문을 해보세요.\n\n* \"프로덕션 환경에서는 어떤 데이터 세트를 사용할 수 있나요?\"\n* \"지난 1시간 동안 API 서비스에 대한 P95 지연 시간을 보여주세요\"\n* \"서비스 이름별로 오류율은 어떻게 되나요?\"\n* \"예산을 초과할 위기에 처한 SLO가 있나요?\"\n* \"스테이징 환경에서 모든 활성 트리거 표시\"\n* \"프로덕션 API 데이터 세트에서 어떤 열을 사용할 수 있나요?\"\n\n### 최적화된 도구 응답\n\n모든 도구 응답은 필수 정보를 유지하면서 컨텍스트 창 사용을 줄이도록 최적화되었습니다.\n\n* **데이터세트 목록** : 이름, 슬러그, 설명만 반환합니다.\n* **열 가져오기** : 이름, 유형 및 설명에 초점을 맞춘 간소화된 열 정보를 반환합니다.\n* **쿼리 실행** :\n * 실제 결과와 필요한 메타데이터가 포함되어 있습니다.\n * 자동으로 계산된 요약 통계를 추가합니다.\n * 히트맵 쿼리에 대한 시리즈 데이터만 포함합니다.\n * 자세한 메타데이터, 링크 및 실행 세부 정보를 생략합니다.\n* **열 분석** :\n * 상위 값, 개수 및 주요 통계를 반환합니다.\n * 적절한 경우 숫자형 메트릭을 자동으로 계산합니다.\n* **SLO 정보** : 주요 상태 지표 및 성과 측정 항목으로 간소화됨\n* **트리거 정보** : 트리거 상태, 조건, 알림 대상에 초점을 맞춥니다.\n\n이러한 최적화를 통해 응답은 간결하면서도 완전해지며, LLM은 맥락 제한 내에서 더 많은 데이터를 처리할 수 있습니다.\n\n### `run_query` 에 대한 쿼리 사양\n\n`run_query` 도구는 포괄적인 쿼리 사양을 지원합니다.\n\n* **계산** : 수행할 연산의 배열\n\n * 지원되는 작업: COUNT, CONCURRENCY, COUNT\\_DISTINCT, HEATMAP, SUM, AVG, MAX, MIN, P001, P01, P05, P10, P25, P50, P75, P90, P95, P99, P999, RATE\\_AVG, RATE\\_SUM, RATE\\_MAX\n * COUNT 및 CONCURRENCY와 같은 일부 작업에는 열이 필요하지 않습니다.\n * 예: `{\"op\": \"HEATMAP\", \"column\": \"duration_ms\"}`\n\n* **필터** : 필터 조건 배열\n\n * 지원되는 연산자: =, !=, >, >=, <, <=, starts-with, does-not-start-with, exists, does-not-exist, contains, does-not-contain, in, not-in\n * 예: `{\"column\": \"error\", \"op\": \"=\", \"value\": true}`\n\n* **filter\\_combination** : \"AND\" 또는 \"OR\"(기본값은 \"AND\")\n\n* **breakdowns** : 결과를 그룹화하기 위한 열 배열\n\n * 예: `[\"service.name\", \"http.status_code\"]`\n\n* **orders** : 결과를 정렬하는 방법을 지정하는 배열\n\n * 분류 또는 계산의 열을 참조해야 합니다.\n * HEATMAP 작업은 주문에 사용할 수 없습니다.\n * 예: `{\"op\": \"COUNT\", \"order\": \"descending\"}`\n\n* **time\\_range** : 상대적인 시간 범위(초)(예: 마지막 시간의 경우 3600)\n\n * start\\_time 또는 end\\_time과 결합할 수 있지만 둘 다 결합할 수는 없습니다.\n\n* **시작 시간** 및 **종료 시간** : 절대 시간 범위에 대한 UNIX 타임스탬프\n\n* **가지고 있음** : 계산 값을 기준으로 결과 필터링\n\n * 예: `{\"calculate_op\": \"COUNT\", \"op\": \">\", \"value\": 100}`\n\n### 예제 쿼리\n\n다음은 실제 쿼리의 몇 가지 예입니다.\n\n#### 느린 API 호출 찾기\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"},\n {\"column\": \"duration_ms\", \"op\": \"MAX\"}\n ],\n \"filters\": [\n {\"column\": \"trace.parent_id\", \"op\": \"does-not-exist\"}\n ],\n \"breakdowns\": [\"http.target\", \"name\"],\n \"orders\": [\n {\"column\": \"duration_ms\", \"op\": \"MAX\", \"order\": \"descending\"}\n ]\n}\n```\n\n#### DB 호출 분포(지난주)\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"}\n ],\n \"filters\": [\n {\"column\": \"db.statement\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"db.statement\"],\n \"time_range\": 604800\n}\n```\n\n#### 예외 및 호출자별 예외 수\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"op\": \"COUNT\"}\n ],\n \"filters\": [\n {\"column\": \"exception.message\", \"op\": \"exists\"},\n {\"column\": \"parent_name\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"exception.message\", \"parent_name\"],\n \"orders\": [\n {\"op\": \"COUNT\", \"order\": \"descending\"}\n ]\n}\n```\n\n## 개발\n\n```bash\npnpm install\npnpm run build\n```\n\n## 특허\n\nMIT","Korean","ko-KR",{"_29":59,"_33":62,"_38":63},{"_31":60,"_33":34,"_35":61},"用于与 Honeycomb 可观测性数据交互的服务器。该服务器使像 Claude 这样的法学硕士 (LLM) 能够直接分析和查询您的 Honeycomb 数据集。","# 蜂窝微通道板\n\n用于与 Honeycomb 可观测性数据交互的[模型上下文协议](https://modelcontextprotocol.io)服务器。该服务器使像 Claude 这样的法学硕士 (LLM) 能够跨多个环境直接分析和查询 Honeycomb 数据集。\n\n![蜂窝 MCP 徽标](/img/logo.png)\n\n## 要求\n\n* Node.js 18+\n* 具有完全权限的 Honeycomb API 密钥：\n * 查询分析访问权限\n * SLO 和触发器的读取权限\n * 数据集操作的环境级别访问\n\nHoneycomb MCP 实际上是 Honeycomb 的完整替代接口，因此您需要对 API 拥有广泛的权限。\n\n## 仅限 Honeycomb Enterprise\n\n目前，该功能仅适用于 Honeycomb Enterprise 客户。\n\n## 工作原理\n\n目前，这是一个单服务器进程\\*\\*，您必须在自己的计算机上运行\\*\\*。它无需身份验证。所有信息都使用客户端和服务器之间的 STDIO 进行传输。\n\n## 安装\n\n```bash\npnpm install\npnpm run build\n```\n\n构建工件进入`/build`文件夹。\n\n## 配置\n\n要使用此 MCP 服务器，您需要通过 MCP 配置中的环境变量提供 Honeycomb API 密钥。\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_API_KEY\": \"your_api_key\"\n }\n }\n }\n}\n```\n\n对于多种环境：\n\n```json\n{\n \"mcpServers\": {\n \"honeycomb\": {\n \"command\": \"node\",\n \"args\": [\n \"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs\"\n ],\n \"env\": {\n \"HONEYCOMB_ENV_PROD_API_KEY\": \"your_prod_api_key\",\n \"HONEYCOMB_ENV_STAGING_API_KEY\": \"your_staging_api_key\"\n }\n }\n }\n}\n```\n\n**重要提示：**这些环境变量**必须**在 MCP 配置的`env`块中设置。\n\n### 欧盟配置\n\n由于 MCP 默认为非欧盟实例，因此欧盟客户还必须设置`HONEYCOMB_API_ENDPOINT`配置。\n\n```bash\n# Optional custom API endpoint (defaults to https://api.honeycomb.io)\nHONEYCOMB_API_ENDPOINT=https://api.eu1.honeycomb.io/\n```\n\n### 缓存配置\n\nMCP 服务器为所有非查询 Honeycomb API 调用实现了缓存，以提高性能并减少 API 使用率。您可以使用以下环境变量配置缓存：\n\n```bash\n# Enable/disable caching (default: true)\nHONEYCOMB_CACHE_ENABLED=true\n\n# Default TTL in seconds (default: 300)\nHONEYCOMB_CACHE_DEFAULT_TTL=300\n\n# Resource-specific TTL values in seconds (defaults shown)\nHONEYCOMB_CACHE_DATASET_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_COLUMN_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_BOARD_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_SLO_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_TRIGGER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_MARKER_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_RECIPIENT_TTL=900 # 15 minutes\nHONEYCOMB_CACHE_AUTH_TTL=3600 # 1 hour\n\n# Maximum cache size (items per resource type)\nHONEYCOMB_CACHE_MAX_SIZE=1000\n```\n\n## 客户端兼容性\n\nHoneycomb MCP 已通过以下客户端测试：\n\n* [克劳德桌面](https://modelcontextprotocol.io/quickstart/user)\n* [克劳德·科德](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp)\n* [光标](https://docs.cursor.com/context/model-context-protocol)\n* [风帆冲浪](https://docs.codeium.com/windsurf/mcp)\n* [鹅](https://block.github.io/goose/docs/getting-started/using-extensions#mcp-servers)\n\n它可能会与其他客户端合作。\n\n## 特征\n\n* 跨多个环境查询 Honeycomb 数据集\n* 运行分析查询并支持以下功能：\n * 多种计算类型（COUNT、AVG、P95 等）\n * 细分和过滤器\n * 基于时间的分析\n* 监控 SLO 及其状态（仅限企业）\n* 分析列和数据模式\n* 查看和分析触发器\n* 访问数据集元数据和架构信息\n* 通过基于 TTL 的缓存优化所有非查询 API 调用的性能\n\n#### 资源\n\n使用以下格式的 URI 访问 Honeycomb 数据集： `honeycomb://{environment}/{dataset}`\n\n例如：\n\n* `honeycomb://production/api-requests`\n* `honeycomb://staging/backend-services`\n\n资源响应包括：\n\n* 数据集名称\n* 列信息（名称、类型、描述）\n* 架构详细信息\n\n#### 工具\n\n* `list_datasets` ：列出环境中的所有数据集\n\n ```json\n { \"environment\": \"production\" }\n ```\n\n* `get_columns` ：获取数据集的列信息\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `run_query` ：使用丰富的选项运行分析查询\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n { \"op\": \"COUNT\" },\n { \"op\": \"P95\", \"column\": \"duration_ms\" }\n ],\n \"breakdowns\": [\"service.name\"],\n \"time_range\": 3600\n }\n ```\n\n* `analyze_columns` ：通过运行统计查询并返回计算指标来分析数据集中的特定列。\n\n* `list_slos` ：列出数据集的所有 SLO\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `get_slo` ：获取详细的 SLO 信息\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"sloId\": \"abc123\"\n }\n ```\n\n* `list_triggers` ：列出数据集的所有触发器\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\"\n }\n ```\n\n* `get_trigger` ：获取详细的触发器信息\n\n ```json\n {\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"triggerId\": \"xyz789\"\n }\n ```\n\n* `get_trace_link` ：生成指向 Honeycomb UI 中特定跟踪的深层链接\n\n* `get_instrumentation_help` ：提供 OpenTelemetry 检测指导\n\n ```json\n {\n \"language\": \"python\",\n \"filepath\": \"app/services/payment_processor.py\"\n }\n ```\n\n### Claude 的示例查询\n\n向克劳德询问以下问题：\n\n* “生产环境中有哪些可用的数据集？”\n* “显示过去一小时内 API 服务的 P95 延迟”\n* “按服务名称细分的错误率是多少？”\n* “有哪些 SLO 接近超出预算？”\n* “显示暂存环境中的所有活动触发器”\n* “生产 API 数据集中有哪些可用的列？”\n\n### 优化工具响应\n\n所有工具响应都经过优化，以减少上下文窗口的使用，同时保留基本信息：\n\n* **列出数据集**：仅返回名称、slug 和描述\n* **获取列**：返回简化的列信息，重点关注名称、类型和描述\n* **运行查询**：\n * 包括实际结果和必要的元数据\n * 添加自动计算的汇总统计数据\n * 仅包含热图查询的系列数据\n * 省略详细的元数据、链接和执行细节\n* **分析列**：\n * 返回最高值、计数和关键统计数据\n * 在适当的时候自动计算数值指标\n* **SLO 信息**：精简为关键状态指标和绩效指标\n* **触发信息**：重点关注触发状态、条件、通知对象\n\n这种优化确保响应简洁而完整，从而允许 LLM 在上下文限制内处理更多数据。\n\n### `run_query`的查询规范\n\n`run_query`工具支持全面的查询规范：\n\n* **计算**：要执行的操作数组\n\n * 支持的操作：COUNT、CONCURRENCY、COUNT\\_DISTINCT、HEATMAP、SUM、AVG、MAX、MIN、P001、P01、P05、P10、P25、P50、P75、P90、P95、P99、P999、RATE\\_AVG、RATE\\_SUM、RATE\\_MAX\n * 某些操作（例如 COUNT 和 CONCURRENCY）不需要列\n * 例如： `{\"op\": \"HEATMAP\", \"column\": \"duration_ms\"}`\n\n* **filters** ：过滤条件数组\n\n * 支持的运算符：=、!=、>、>=、<、<=、starts-with、does-not-start-with、exists、does-not-exist、contains、does-not-contain、in、not-in\n * 例如： `{\"column\": \"error\", \"op\": \"=\", \"value\": true}`\n\n* **filter\\_combination** ：“AND”或“OR”（默认为“AND”）\n\n* **breakdowns** ：按列对结果进行分组的数组\n\n * 例如： `[\"service.name\", \"http.status_code\"]`\n\n* **orders** ：指定如何对结果进行排序的数组\n\n * 必须引用细分或计算中的列\n * HEATMAP 操作不能用于订单\n * 例如： `{\"op\": \"COUNT\", \"order\": \"descending\"}`\n\n* **time\\_range** ：相对时间范围（以秒为单位）（例如，过去一小时为 3600 秒）\n\n * 可以与 start\\_time 或 end\\_time 结合使用，但不能同时与两者结合使用\n\n* **start\\_time**和**end\\_time** ：绝对时间范围的 UNIX 时间戳\n\n* **having** ：根据计算值过滤结果\n\n * 例如： `{\"calculate_op\": \"COUNT\", \"op\": \">\", \"value\": 100}`\n\n### 示例查询\n\n以下是一些真实示例查询：\n\n#### 查找缓慢的 API 调用\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"},\n {\"column\": \"duration_ms\", \"op\": \"MAX\"}\n ],\n \"filters\": [\n {\"column\": \"trace.parent_id\", \"op\": \"does-not-exist\"}\n ],\n \"breakdowns\": [\"http.target\", \"name\"],\n \"orders\": [\n {\"column\": \"duration_ms\", \"op\": \"MAX\", \"order\": \"descending\"}\n ]\n}\n```\n\n#### DB 调用分布（上周）\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"column\": \"duration_ms\", \"op\": \"HEATMAP\"}\n ],\n \"filters\": [\n {\"column\": \"db.statement\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"db.statement\"],\n \"time_range\": 604800\n}\n```\n\n#### 按异常和调用者统计的异常数量\n\n```json\n{\n \"environment\": \"production\",\n \"dataset\": \"api-requests\",\n \"calculations\": [\n {\"op\": \"COUNT\"}\n ],\n \"filters\": [\n {\"column\": \"exception.message\", \"op\": \"exists\"},\n {\"column\": \"parent_name\", \"op\": \"exists\"}\n ],\n \"breakdowns\": [\"exception.message\", \"parent_name\"],\n \"orders\": [\n {\"op\": \"COUNT\", \"order\": \"descending\"}\n ]\n}\n```\n\n## 发展\n\n```bash\npnpm install\npnpm run build\n```\n\n## 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Default: AND.",{"_33":437,"_31":438,"_232":439},"analyze_columns","Analyzes specific columns in a dataset by running statistical queries and returning computed metrics.\nThis tool allows users to get statistical information about a specific column, including value distribution, top values, and numeric statistics (for numeric columns).\nSupports analyzing up to 10 columns at once by specifying an array of column names in the 'columns' parameter.\nWhen multiple columns are specified, they will be analyzed together as a group, showing the distribution of their combined values.\nUse this tool before running queries to get a better understanding of the data in your dataset.\n",{"_87":136,"_234":235,"_74":440,"_83":442,"_267":210},[237,273,441],"columns",{"_441":443,"_273":449,"_451":452,"_237":454},{"_87":264,"_265":444,"_445":446,"_447":286,"_31":448},{"_87":88},"maxItems",10,"minItems","The names of the columns to analyze",{"_87":88,"_285":286,"_31":450},"The dataset containing the column to analyze","timeRange",{"_87":241,"_31":453,"_278":279},"Time range in seconds to analyze. 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Rate & Review

MCP directory API