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Datadog MCP Server

Name: Datadog MCP Server
Author: tuno-dev

by tuno-dev

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Servidor MCP de Datadog

Inglés (Este documento) |日本語

Servidor MCP para API de Datadog, que permite funcionalidades de búsqueda de registros, búsqueda de tramos de seguimiento y agregación de tramos de seguimiento.

Características

Búsqueda de registros : busque y recupere registros de Datadog con opciones de consulta flexibles
Búsqueda de tramos de seguimiento : busque tramos de seguimiento distribuidos con varias opciones de filtrado
Agregación de tramos de seguimiento : agregue tramos de seguimiento por diferentes dimensiones para su análisis

Related MCP server: Datadog MCP Server

Herramientas

search_logs
- Buscar registros en Datadog
- Entradas:
  - filterQuery (cadena opcional): cadena de consulta para buscar registros (valor predeterminado: "*")
  - filterFrom (número opcional): hora de inicio de la búsqueda como marca de tiempo UNIX en segundos (valor predeterminado: hace 15 minutos)
  - filterTo (número opcional): hora de finalización de la búsqueda como marca de tiempo UNIX en segundos (valor predeterminado: hora actual)
  - pageLimit (número opcional): Número máximo de registros a recuperar (predeterminado: 25, máximo: 1000)
  - pageCursor (cadena opcional): cursor de paginación para recuperar resultados adicionales
- Devuelve: Texto formateado que contiene:
  - Condiciones de búsqueda (consulta y rango de tiempo)
  - Número de registros encontrados
  - Cursor de página siguiente (si está disponible)
  - Detalles del registro que incluyen:
    - Nombre del servicio
    - Etiquetas
    - Marca de tiempo
    - Estado
    - Mensaje (truncado a 300 caracteres)
    - Anfitrión
    - Atributos importantes (http.method, http.url, http.status_code, error)
search_spans
- Búsqueda de tramos de seguimiento en Datadog
- Entradas:
  - filterQuery (cadena opcional): cadena de consulta para buscar intervalos (valor predeterminado: "*")
  - filterFrom (número opcional): hora de inicio de la búsqueda como marca de tiempo UNIX en segundos (valor predeterminado: hace 15 minutos)
  - filterTo (número opcional): hora de finalización de la búsqueda como marca de tiempo UNIX en segundos (valor predeterminado: hora actual)
  - pageLimit (número opcional): Número máximo de intervalos a recuperar (predeterminado: 25, máximo: 1000)
  - pageCursor (cadena opcional): cursor de paginación para recuperar resultados adicionales
- Devuelve: Texto formateado que contiene:
  - Condiciones de búsqueda (consulta y rango de tiempo)
  - Número de tramos encontrados
  - Cursor de página siguiente (si está disponible)
  - Detalles del tramo que incluyen:
    - Nombre del servicio
    - Marca de tiempo
    - Nombre del recurso
    - Duración (en segundos)
    - Anfitrión
    - Ambiente
    - Tipo
    - Atributos importantes (http.method, http.url, http.status_code, error)
aggregate_spans
- Agrupar intervalos de seguimiento en Datadog según dimensiones específicas
- Entradas:
  - filterQuery (cadena opcional): cadena de consulta para filtrar intervalos para la agregación (valor predeterminado: "*")
  - filterFrom (número opcional): hora de inicio como marca de tiempo UNIX en segundos (valor predeterminado: hace 15 minutos)
  - filterTo (número opcional): hora de finalización como marca de tiempo UNIX en segundos (predeterminado: hora actual)
  - groupBy (cadena opcional[]): Dimensiones por las que agrupar (por ejemplo, ["servicio", "nombre_del_recurso", "estado"])
  - aggregation (cadena opcional): Método de agregación: "count", "avg", "sum", "min", "max", "pct" (predeterminado: "count")
  - interval (cadena opcional): intervalo de tiempo para datos de series temporales (solo cuando el tipo es "series temporales")
  - type (cadena opcional): Tipo de resultado, ya sea "serie temporal" o "total" (predeterminado: "serie temporal")
- Devuelve: Texto formateado que contiene:
  - La agregación da como resultado grupos, cada uno de los cuales incluye:
    - Identificación del depósito
    - Agrupar por valores (si se especifica groupBy)
    - Valores calculados según el método de agregación
  - Metadatos adicionales:
    - Tiempo de procesamiento (transcurrido)
    - ID de solicitud
    - Estado
    - Advertencias (si las hay)

Configuración

Debe configurar la API de Datadog y las claves de aplicación:

Obtenga su clave API y clave de aplicación desde la página Claves API de Datadog
Instalar dependencias en el proyecto datadog-mcp:
```
npm install
# or
pnpm install
```
Construya el proyecto TypeScript:
```
npm run build
# or
pnpm run build
```

Configuración de Docker

Puedes construir usando Docker con el siguiente comando:

docker build -t datadog-mcp .

Uso con Claude Desktop

Para usar esto con Claude Desktop, agregue lo siguiente a su claude_desktop_config.json :

{
  "mcpServers": {
    "datadog": {
      "command": "node",
      "args": [
        "/path/to/datadog-mcp/build/index.js"
      ],
      "env": {
        "DD_API_KEY": "<YOUR_DATADOG_API_KEY>",
        "DD_APP_KEY": "<YOUR_DATADOG_APP_KEY>"
      }
    }
  }
}

Si estás usando Docker, puedes configurarlo de la siguiente manera:

{
  "mcpServers": {
    "datadog": {
      "command": "docker",
        "args": [
          "run",
          "-i",
          "--rm",
          "-e",
          "DD_API_KEY",
          "-e",
          "DD_APP_KEY",
          "datadog-mcp"
        ],
      "env": {
        "DD_API_KEY": "<YOUR_DATADOG_API_KEY>",
        "DD_APP_KEY": "<YOUR_DATADOG_APP_KEY>"
      }
    }
  }
}

Uso con VS Code

Para una instalación rápida en VS Code, configure sus ajustes:

Abrir configuración de usuario (JSON) en VS Code ( Ctrl+Shift+P → Preferences: Open User Settings (JSON) )
Agregue la siguiente configuración:

{
  "mcp": {
    "servers": {
      "datadog": {
        "command": "node",
        "args": [
          "/path/to/datadog-mcp/build/index.js"
        ],
        "env": {
          "DD_API_KEY": "<YOUR_DATADOG_API_KEY>",
          "DD_APP_KEY": "<YOUR_DATADOG_APP_KEY>"
        }
      }
    }
  }
}

Si estás usando Docker, puedes configurarlo de la siguiente manera:

{
  "mcp": {
    "servers": {
      "datadog": {
        "command": "docker",
          "args": [
          "run",
          "-i",
          "--rm",
          "-e",
          "DD_API_KEY",
          "-e",
          "DD_APP_KEY",
          "datadog-mcp"
        ],
        "env": {
          "DD_API_KEY": "<YOUR_DATADOG_API_KEY>",
          "DD_APP_KEY": "<YOUR_DATADOG_APP_KEY>"
        }
      }
    }
  }
}

Alternativamente, puede agregar esto a un archivo .vscode/mcp.json en su espacio de trabajo (sin la clave mcp ):

{
  "servers": {
    "datadog": {
      "command": "node",
      "args": [
        "/path/to/datadog-mcp/build/index.js"
      ],
      "env": {
        "DD_API_KEY": "<YOUR_DATADOG_API_KEY>",
        "DD_APP_KEY": "<YOUR_DATADOG_APP_KEY>"
      }
    }
  }
}

Si estás usando Docker, puedes configurarlo de la siguiente manera:

{
  "servers": {
    "datadog": {
      "command": "docker",
        "args": [
          "run",
          "-i",
          "--rm",
          "-e",
          "DD_API_KEY",
          "-e",
          "DD_APP_KEY",
          "datadog-mcp"
        ],
      "env": {
        "DD_API_KEY": "<YOUR_DATADOG_API_KEY>",
        "DD_APP_KEY": "<YOUR_DATADOG_APP_KEY>"
      }
    }
  }
}

Available Tools

3 tools

aggregate_spansC

Tool for aggregating Datadog trace spans

ParametersJSON Schema

Name	Required	Description	Default
`filterQuery`	No	Query string to search for (optional, default is '*')	*
`filterFrom`	No	Search start time (UNIX timestamp in seconds, optional, default is 15 minutes ago)
`filterTo`	No	Search end time (UNIX timestamp in seconds, optional, default is current time)
`groupBy`	No	Attributes to group by (example: ['service', 'resource_name'])
`interval`	No	Time interval to group results by (optional, only used when type is timeseries)
`type`	No	Result type - timeseries or total (optional, default is 'timeseries')	timeseries

TDQS

C2.6/5.0

Behavior2/5

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

With no annotations, the description carries full burden but provides minimal behavioral insight. It doesn't disclose whether this is a read-only operation, its performance characteristics, rate limits, or what the aggregation output looks like. The description only states the tool's purpose without behavioral traits.

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

Conciseness4/5

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

The description is a single, efficient sentence with no wasted words. It's appropriately sized for a tool with a clear name and detailed schema. However, it could be more front-loaded with key details like aggregation type.

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?

For a 6-parameter aggregation tool with no annotations and no output schema, the description is incomplete. It doesn't explain the return format, aggregation metrics, or how results are structured. The schema handles parameters well, but behavioral and output context is missing.

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?

Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds no parameter-specific information beyond what's in the schema. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate or add value.

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

Purpose3/5

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

The description states the tool aggregates Datadog trace spans, which is a clear purpose. However, it doesn't specify what aggregation means (e.g., counting, averaging, summing) or how it differs from sibling tools like search_spans. The verb 'aggregating' is specific but lacks operational detail.

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?

No guidance is provided on when to use this tool versus alternatives like search_spans. The description doesn't mention any prerequisites, exclusions, or contextual cues for selection. Usage is implied only by the tool name and basic purpose.

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

search_logsC

Tool for searching Datadog logs

ParametersJSON Schema

Name	Required	Description	Default
`filterQuery`	No	Query string to search logs (optional, default is '*')	*
`filterFrom`	No	Search start time (UNIX timestamp in seconds, optional, default is 15 minutes ago)
`filterTo`	No	Search end time (UNIX timestamp in seconds, optional, default is current time)
`pageLimit`	No	Maximum number of logs to retrieve (optional, default is 25)
`pageCursor`	No	Cursor to retrieve the next page (optional)

TDQS

C2.6/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but only states the basic function. It doesn't mention authentication needs, rate limits, pagination behavior beyond the cursor parameter, error handling, or what the output looks like (especially critical since there's no output schema). For a search tool with 5 parameters and no annotations, this is inadequate.

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

Conciseness4/5

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

The description is a single, efficient sentence with no wasted words. It's appropriately sized for a basic tool description, though it could be more informative while remaining concise.

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 (5 parameters, no annotations, no output schema, and sibling tools), the description is incomplete. It doesn't help the agent understand what the tool returns, how to interpret results, or how it differs from similar tools. For a search operation with multiple parameters and no output schema, more context is needed.

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?

Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional meaning about parameters beyond implying a search function. It doesn't explain query syntax, time format nuances, or how pagination works in practice. Baseline 3 is appropriate when the schema does the heavy lifting.

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

Purpose3/5

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

The description 'Tool for searching Datadog logs' states the basic action (searching) and resource (Datadog logs), but it's vague about scope and doesn't differentiate from sibling tools like 'search_spans'. It doesn't specify what kind of logs or what search capabilities exist beyond the basic verb+resource.

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?

No guidance is provided on when to use this tool versus alternatives like 'search_spans' or 'aggregate_spans'. The description doesn't mention any context, prerequisites, or exclusions for usage, leaving the agent with no comparative information.

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

search_spansC

Tool for searching Datadog trace spans

ParametersJSON Schema

Name	Required	Description	Default
`filterQuery`	No	Query string to search for (optional, default is '*')	*
`filterFrom`	No	Search start time (UNIX timestamp in seconds, optional, default is 15 minutes ago)
`filterTo`	No	Search end time (UNIX timestamp in seconds, optional, default is current time)
`pageLimit`	No	Maximum number of spans to retrieve (optional, default is 25)
`pageCursor`	No	Cursor to retrieve the next page (optional)

TDQS

C2.6/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states it's a search tool without mentioning whether it's read-only, what permissions are needed, rate limits, pagination behavior (though schema hints at it), or what the output looks like. For a search tool with 5 parameters, this leaves significant behavioral gaps.

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

Conciseness4/5

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

The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a search tool, though it could be more informative by adding context about when to use it versus siblings.

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 has 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the search returns, how results are structured, or behavioral aspects like pagination. For a search tool with moderate complexity, this leaves too much unspecified.

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?

Schema description coverage is 100%, so the schema fully documents all 5 parameters with their types, defaults, and descriptions. The description adds no additional parameter information beyond what's in the schema. This meets the baseline of 3 for high schema coverage.

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

Purpose3/5

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

The description states the tool searches Datadog trace spans, which is a clear verb+resource combination. However, it doesn't differentiate from sibling tools like 'aggregate_spans' (which likely aggregates rather than searches) or 'search_logs' (which searches logs rather than spans). The purpose is understandable but lacks sibling differentiation.

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. There's no mention of when to choose search_spans over aggregate_spans or search_logs, nor any context about prerequisites or typical use cases. The user must infer usage from the tool name alone.

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. Dates show when Glama detected each change.

3 tool updatesv1.0.0Jan 3, 2026

Changedaggregate_spans11 fields changed

removedInput schema / properties / aggregation

Removed value: -{
-  "default": "count",
-  "description": "集計関数（オプション、デフォルトは「count」）",
-  "enum": [
-    "count",
-    "avg",
-    "sum",
-    "min",
-    "max",
-    "pct"
-  ],
-  "type": "string"
-}

changedInput schema / properties / filterFrom / default

Previous value: -1745805204.363New value: +1767414583.907

changedInput schema / properties / filterFrom / description

Previous value: -"検索開始時間（UNIXタイムスタンプ、秒単位、オプション、デフォルトは15分前）"New value: +"Search start time (UNIX timestamp in seconds, optional, default is 15 minutes ago)"

changedInput schema / properties / filterQuery / description

Previous value: -"検索するためのクエリ文字列（オプション、デフォルトは「*」）"New value: +"Query string to search for (optional, default is '*')"

changedInput schema / properties / filterTo / default

Previous value: -1745806104.363New value: +1767415483.907

changedInput schema / properties / filterTo / description

Previous value: -"検索終了時間（UNIXタイムスタンプ、秒単位、オプション、デフォルトは現在時刻）"New value: +"Search end time (UNIX timestamp in seconds, optional, default is current time)"

changedInput schema / properties / groupBy / description

Previous value: -"グループ化するための属性（例: ['service', 'resource_name']）"New value: +"Attributes to group by (example: ['service', 'resource_name'])"

addedInput schema / properties / groupBy / items / enum

Added value: +[
+  "service",
+  "resource_name",
+  "env",
+  "status",
+  "operation_name",
+  "type",
+  "@version",
+  "@http.status_code",
+  "@http.client_ip",
+  "@http.url",
+  "@http.method",
+  "@http.host",
+  "@http.user_agent",
+  "@http.path_group",
+  "@http.route"
+]

removedInput schema / properties / interval / default
```
Removed value: -"5m"
```

changedInput schema / properties / interval / description

Previous value: -"結果をグループ化する時間間隔（オプション、デフォルト '5m'）"New value: +"Time interval to group results by (optional, only used when type is timeseries)"

changedInput schema / properties / type / description

Previous value: -"結果タイプ - timeseries または total（オプション、デフォルトは「timeseries」）"New value: +"Result type - timeseries or total (optional, default is 'timeseries')"

Changedsearch_logs7 fields changed

changedInput schema / properties / filterFrom / default

Previous value: -1745805204.363New value: +1767414583.907

changedInput schema / properties / filterFrom / description

Previous value: -"検索開始時間（UNIXタイムスタンプ、秒単位、オプション、デフォルトは15分前）"New value: +"Search start time (UNIX timestamp in seconds, optional, default is 15 minutes ago)"

changedInput schema / properties / filterQuery / description

Previous value: -"ログを検索するためのクエリ文字列（オプション、デフォルトは「*」）"New value: +"Query string to search logs (optional, default is '*')"

changedInput schema / properties / filterTo / default

Previous value: -1745806104.363New value: +1767415483.907

changedInput schema / properties / filterTo / description

Previous value: -"検索終了時間（UNIXタイムスタンプ、秒単位、オプション、デフォルトは現在時刻）"New value: +"Search end time (UNIX timestamp in seconds, optional, default is current time)"

changedInput schema / properties / pageCursor / description

Previous value: -"次のページを取得するためのカーソル（オプション）"New value: +"Cursor to retrieve the next page (optional)"

changedInput schema / properties / pageLimit / description

Previous value: -"取得するログの最大数（オプション、デフォルトは25）"New value: +"Maximum number of logs to retrieve (optional, default is 25)"

Changedsearch_spans7 fields changed

changedInput schema / properties / filterFrom / default

Previous value: -1745805204.364New value: +1767414583.907

changedInput schema / properties / filterFrom / description

Previous value: -"検索開始時間（UNIXタイムスタンプ、秒単位、オプション、デフォルトは15分前）"New value: +"Search start time (UNIX timestamp in seconds, optional, default is 15 minutes ago)"

changedInput schema / properties / filterQuery / description

Previous value: -"検索するためのクエリ文字列（オプション、デフォルトは「*」）"New value: +"Query string to search for (optional, default is '*')"

changedInput schema / properties / filterTo / default

Previous value: -1745806104.364New value: +1767415483.907

changedInput schema / properties / filterTo / description

Previous value: -"検索終了時間（UNIXタイムスタンプ、秒単位、オプション、デフォルトは現在時刻）"New value: +"Search end time (UNIX timestamp in seconds, optional, default is current time)"

changedInput schema / properties / pageCursor / description

Previous value: -"次のページを取得するためのカーソル（オプション）"New value: +"Cursor to retrieve the next page (optional)"

changedInput schema / properties / pageLimit / description

Previous value: -"取得するスパンの最大数（オプション、デフォルトは25）"New value: +"Maximum number of spans to retrieve (optional, default is 25)"

3 tool updatesApr 28, 2025
- First observedaggregate_spans
- First observedsearch_logs
- First observedsearch_spans

Install Server

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TDQS

C2.9/5.0

Disambiguation4/5

The three tools are mostly distinct, with 'aggregate_spans' focusing on aggregation of trace data, 'search_logs' targeting log data, and 'search_spans' targeting trace data. However, 'aggregate_spans' and 'search_spans' both operate on spans and could be confused for overlapping purposes, though their descriptions clarify one aggregates and the other searches.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case, using 'aggregate' and 'search' as verbs paired with specific nouns like 'spans' and 'logs'. There are no deviations in naming style or convention.

Tool Count3/5

With only 3 tools, the server feels thin for a Datadog integration, which typically involves monitoring, metrics, dashboards, and alerts. While the tools cover traces and logs, the scope is limited, making it borderline appropriate for the apparent domain.

Completeness2/5

The tool surface has significant gaps for a Datadog server, missing core operations like querying metrics, managing dashboards, setting alerts, or accessing APM data beyond spans. Agents will struggle with incomplete coverage for common monitoring workflows.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

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