Datadog MCP Server

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MCP Server for Datadog API, enabling log search, trace span search, and trace span aggregation functionalities.

<a href="https://glama.ai/mcp/servers/@Nozomuts/datadog-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@Nozomuts/datadog-mcp/badge" alt="Datadog Server MCP server" /> </a>

Features

• Log Search: Search and retrieve logs from Datadog with flexible query options
• Trace Span Search: Search for distributed trace spans with various filtering options
• Trace Span Aggregation: Aggregate trace spans by different dimensions for analysis

Tools

1. search_logs

   • Search for logs in Datadog
   • Inputs:
     • filterQuery (optional string): Query string to search logs (default: "*")
     • filterFrom (optional number): Search start time as UNIX timestamp in seconds (default: 15 minutes ago)
     • filterTo (optional number): Search end time as UNIX timestamp in seconds (default: current time)
     • pageLimit (optional number): Maximum number of logs to retrieve (default: 25, max: 1000)
     • pageCursor (optional string): Pagination cursor for retrieving additional results
   • Returns: Formatted text containing:
     • Search conditions (query and time range)
     • Number of logs found
     • Next page cursor (if available)
     • Log details including:
       • Service name
       • Tags
       • Timestamp
       • Status
       • Message (truncated to 300 characters)
       • Host
       • Important attributes (http.method, http.url, http.status_code, error)

2. search_spans

   • Search for trace spans in Datadog
   • Inputs:
     • filterQuery (optional string): Query string to search spans (default: "*")
     • filterFrom (optional number): Search start time as UNIX timestamp in seconds (default: 15 minutes ago)
     • filterTo (optional number): Search end time as UNIX timestamp in seconds (default: current time)
     • pageLimit (optional number): Maximum number of spans to retrieve (default: 25, max: 1000)
     • pageCursor (optional string): Pagination cursor for retrieving additional results
   • Returns: Formatted text containing:
     • Search conditions (query and time range)
     • Number of spans found
     • Next page cursor (if available)
     • Span details including:
       • Service name
       • Timestamp
       • Resource name
       • Duration (in seconds)
       • Host
       • Environment
       • Type
       • Important attributes (http.method, http.url, http.status_code, error)

3. aggregate_spans

   • Aggregate trace spans in Datadog by specified dimensions
   • Inputs:
     • filterQuery (optional string): Query string to filter spans for aggregation (default: "*")
     • filterFrom (optional number): Start time as UNIX timestamp in seconds (default: 15 minutes ago)
     • filterTo (optional number): End time as UNIX timestamp in seconds (default: current time)
     • groupBy (optional string[]): Dimensions to group by (e.g., ["service", "resource_name", "status"])
     • aggregation (optional string): Aggregation method - "count", "avg", "sum", "min", "max", "pct" (default: "count")
     • interval (optional string): Time interval for time series data (only when type is "timeseries")
     • type (optional string): Result type, either "timeseries" or "total" (default: "timeseries")
   • Returns: Formatted text containing:
     • Aggregation results in buckets, each including:
       • Bucket ID
       • Group by values (if groupBy is specified)
       • Computed values based on the aggregation method
     • Additional metadata:
       • Processing time (elapsed)
       • Request ID
       • Status
       • Warnings (if any)

Setup

You need to set up Datadog API and application keys:

1. Get your API key and application key from the Datadog API Keys page
2. Install dependencies in the datadog-mcp project:

   npm install
   # or
   pnpm install
   

3. Build the TypeScript project:

   npm run build
   # or
   pnpm run build
   

Docker Setup

You can build using Docker with the following command:

docker build -t datadog-mcp .

Usage with Claude Desktop

To use this with Claude Desktop, add the following to your 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>"
      }
    }
  }
}

If you're using Docker, you can configure it like this:

{
  "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>"
      }
    }
  }
}

Usage with VS Code

For quick installation in VS Code, configure your settings:

1. Open User Settings (JSON) in VS Code (Ctrl+Shift+P → Preferences: Open User Settings (JSON))
2. Add the following configuration:

{
  "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>"
        }
      }
    }
  }
}

If you're using Docker, you can configure it like this:

{
  "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>"
        }
      }
    }
  }
}

Alternatively, you can add this to a .vscode/mcp.json file in your workspace (without the mcp key):

{
  "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>"
      }
    }
  }
}

If you're using Docker, you can configure it like this:

{
  "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>"
      }
    }
  }
}