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SolarWinds Logs MCP Server

by jakenuts
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Servidor MCP de registros de SolarWinds

Un servidor de Protocolo de contexto de modelo (MCP) para acceder y visualizar los registros de SolarWinds Observability.

Nota -

Este servidor está incompleto actualmente, ya que no admite la búsqueda de datos estructurados (¿una limitación de la API REST?). No estoy seguro de si también necesita aceptar un centro de datos para usar en las llamadas a los puntos finales de la API. Abordaré ambos problemas cuando el tiempo lo permita (lo necesité para un problema real de trabajo; primero tengo que solucionarlo).

Herramientas

registros de búsqueda

Busque registros de SolarWinds Observability con filtrado opcional

  • Toma parámetros de búsqueda que incluyen filtro, rango de tiempo y opciones de paginación.

  • Devuelve entradas de registro formateadas con marcas de tiempo, nombres de host y mensajes.

  • Admite filtrado avanzado por grupo, entidad y más

  • El rango de búsqueda predeterminado son las últimas 24 horas

visualizar_registros

Generar una respuesta json de histograma para eventos de registro

  • Formateado para representaciones de Claude y Canvas

  • Intervalos de tiempo configurables (minuto, hora, día)

  • Admite zonas horarias UTC o locales

  • Filtros de consulta y rangos de tiempo personalizables

  • El rango de visualización predeterminado son las últimas 24 horas

Recursos

Búsqueda de registros de SolarWinds

  • Plantilla de URI: solarwinds://{query}/search

  • Devuelve entradas de registro que coinciden con la consulta especificada

  • Ejemplo: solarwinds://error/search

Related MCP server: Log Analyzer MCP Server

Instalación

Opcionalmente instalar desde npm:

npm install -g mcp-solarwinds

O clonar y compilar desde la fuente:

git clone https://github.com/@jakenuts/mcp-solarwinds.git
cd mcp-solarwinds
npm install
npm run build

O simplemente usa npx en tus configuraciones

Para la extensión Cline VSCode

Agregar a %APPDATA%/Code - Insiders/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json :

{
  "mcpServers": {
    "solarwinds": {
      "command": "npx",
      "args": ["-y", "mcp-solarwinds"],
      "env": {
        "SOLARWINDS_API_TOKEN": "your-api-token"
      },
      "autoApprove": ["search_logs", "visualize_logs"]
    }
  }
}

Para Claude Desktop

Agregue al archivo de configuración apropiado:

Windows: %APPDATA%/Claude/claude_desktop_config.json MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "solarwinds": {
      "command": "npx",
      "args": ["-y", "mcp-solarwinds"],
      "env": {
        "SOLARWINDS_API_TOKEN": "your-api-token"
      }
    }
  }
}

Configuración especial de Windows

Si encuentra el problema de surgimiento de ENOENT npx en Windows, use esta configuración alternativa que especifica las rutas completas:

{
  "mcpServers": {
    "solarwinds": {
      "command": "C:\\Users\\[username]\\AppData\\Roaming\\nvm\\[node-version]\\node.exe",
      "args": [
        "C:\\Users\\[username]\\AppData\\Roaming\\npm\\node_modules\\npm\\bin\\npx-cli.js",
        "-y",
        "mcp-solarwinds"
      ],
      "env": {
        "SOLARWINDS_API_TOKEN": "your-api-token"
      }
    }
  }
}

Configuración

El servidor MCP de SolarWinds Observability requiere un token de API para autenticarse con la API de SolarWinds Observability.

Métodos de configuración

Hay varias formas de proporcionar el token API:

  1. Configuración de ajustes de MCP (recomendado) : Configure el token en su archivo de configuración de MCP

  2. Variable de entorno : Establezca la variable de entorno SOLARWINDS_API_TOKEN

  3. Archivo .env local (para pruebas) : cree un archivo .env en la raíz del proyecto con SOLARWINDS_API_TOKEN=your-token

Para realizar pruebas locales, puede:

  1. Copia .env.example a .env y agrega tu token

  2. Ejecute el script de ejemplo: node examples/local-test.js

Ejemplos de uso de herramientas

registros de búsqueda

Búsqueda básica:

{
  "filter": "error"
}

Búsqueda avanzada con rango de tiempo y paginación:

{
  "filter": "error",
  "entityId": "web-server",
  "startTime": "2025-03-01T00:00:00Z",
  "endTime": "2025-03-05T23:59:59Z",
  "pageSize": 100,
  "direction": "backward"
}

visualizar_registros

Histograma básico (gráfico ASCII):

{
  "filter": "error",
  "interval": "hour"
}

Visualización avanzada (gráfico ASCII):

{
  "filter": "error",
  "entityId": "web-server",
  "startTime": "2025-03-01T00:00:00Z",
  "endTime": "2025-03-05T23:59:59Z",
  "interval": "day",
  "use_utc": true
}

Visualización de Claude (formato JSON):

{
  "filter": "error",
  "interval": "hour",
  "format": "json"
}

El formato JSON devuelve datos que Claude puede visualizar como un gráfico:

{
  "timeRanges": ["12:02", "12:03", "12:04", "12:05", "12:06", "12:07", "12:08", "12:09"],
  "counts": [261, 47, 48, 48, 31, 262, 270, 33],
  "total": 1000,
  "queryParams": {
    "query": "error",
    "startTime": "2025-03-05T00:00:00.000Z",
    "endTime": "2025-03-05T23:59:59.000Z"
  }
}

Desarrollo

Instalar dependencias:

npm install

Construir el servidor:

npm run build

Depuración

Dado que los servidores MCP se comunican a través de stdio, la depuración puede ser un desafío. El Inspector MCP ofrece útiles herramientas de depuración:

npm run debug:inspector

Esto le proporcionará una URL para acceder al inspector en su navegador, donde podrá:

  • Ver todos los mensajes de MCP

  • Inspeccionar las cargas útiles de solicitud/respuesta

  • Pruebe herramientas de forma interactiva

  • Supervisar el estado del servidor

Para pruebas locales sin el marco MCP:

# Create a .env file with your token
cp .env.example .env
# Edit .env to add your token
# Run the example script
node examples/local-test.js

Detalles técnicos

  • Desarrollado con TypeScript y el SDK de MCP

  • Utiliza axios para la comunicación API

  • Admite formatos de fecha ISO 8601 para rangos de tiempo

  • Genera histogramas ASCII para visualización de registros

  • Rango de búsqueda predeterminado: últimas 24 horas

  • Tamaño de página predeterminado: 50 registros

  • Admite múltiples métodos de autenticación

Available Tools

2 tools
search_logsC

Search SolarWinds Observability logs with optional filtering

ParametersJSON Schema
NameRequiredDescriptionDefault
filterNoA search query string. Use AND/OR operators to combine terms (e.g., "error AND timeout"). The search is performed across all fields including message, hostname, program, and nested JSON fields like Context.CorrelationId. Field-specific queries like "field:value" are not supported.
groupNoFilter logs by a specific group name
entityIdNoFilter logs by a specific entity ID
startTimeNoUTC start time (ISO 8601 format)
endTimeNoUTC end time (ISO 8601 format)
directionNoSort order: backward (oldest to newest), forward (newest to oldest), or tail (oldest to newest)backward
pageSizeNoMaximum messages to return per page
skipTokenNoToken to skip to the next page of results

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches logs with filtering, but lacks details on permissions, rate limits, pagination behavior (beyond schema hints), or what the response looks like (e.g., format, error handling). For a search tool with 8 parameters and no annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose ('search SolarWinds Observability logs') and adds a key feature ('optional filtering'). There's no wasted text, and it's appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (8 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like pagination, error handling, or response format, which are crucial for effective use. The schema covers parameters well, but the description fails to provide necessary context for a search operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond the schema, such as examples of complex queries or practical usage tips. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate with extra insights.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('search') and resource ('SolarWinds Observability logs'), making the purpose evident. However, it doesn't explicitly differentiate from the sibling 'visualize_logs' tool, which likely serves a different purpose (visualization vs. searching). The mention of 'optional filtering' adds some specificity but could be more distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as the sibling 'visualize_logs'. It mentions 'optional filtering' but doesn't specify scenarios where filtering is necessary or when other tools might be more appropriate. There's no explicit when/when-not or alternative usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

visualize_logsC

Generate a histogram visualization of log events

ParametersJSON Schema
NameRequiredDescriptionDefault
intervalNoTime interval for histogram bucketshour
startTimeNoUTC start time (ISO 8601 format), defaults to 24 hours ago
endTimeNoUTC end time (ISO 8601 format), defaults to current time
filterNoA search query string
groupNoFilter logs by a specific group name
entityIdNoFilter logs by a specific entity ID
pageSizeNoMaximum messages to analyze
use_utcNoUse UTC time instead of local time
formatNoOutput format: text for ASCII chart, json for Claude visualizationtext

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates a visualization but doesn't describe what happens during execution (e.g., whether it processes data in real-time, accesses a database, has rate limits, requires specific permissions, or returns an image or text). For a tool with 9 parameters and no annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, achieving optimal conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (9 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like execution details, return format (beyond implied visualization), or error handling. For a visualization tool with multiple filtering options, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds no parameter-specific information beyond what's already in the input schema, which has 100% description coverage. It doesn't explain how parameters like 'filter' or 'group' affect the histogram or provide context for their usage. With high schema coverage, the baseline is 3, as the schema does the heavy lifting without additional value from the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate a histogram visualization of log events.' It specifies the action (generate), resource (histogram visualization), and target (log events). However, it doesn't explicitly differentiate from its sibling tool 'search_logs' beyond the visualization aspect, which is why it doesn't reach a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its sibling 'search_logs' or any alternatives. There's no mention of use cases, prerequisites, or exclusions. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.0
    • First observedsearch_logs
    • First observedvisualize_logs
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one searches logs with filtering, and the other generates visualizations. There is no overlap in functionality, making it impossible to confuse them.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (search_logs, visualize_logs) with the same naming style. This predictability helps agents understand the tool set structure easily.

Tool Count2/5

With only two tools, the server feels too thin for a logs domain, lacking essential operations like creating, updating, or deleting logs. This minimal set limits agent capabilities significantly.

Completeness2/5

The tool set is severely incomplete for logs management, missing core CRUD operations and lifecycle coverage. Agents can only search and visualize, with no ability to modify or manage log data, leading to dead ends in workflows.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

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