Overview

The Datadog MCP Server bridges AI agents with Datadog by providing structured access to observability data and controls. It enables natural-language workflows over metrics, logs, traces, dashboards, monitors, incidents, and infrastructure contexts.

Implementations:

Official preview by Datadog and community servers in Python, Node.js, and Docker.

Key Features

📈

Metrics & Logs

Query timeseries metrics and search logs with filtering and pagination

🔔

Monitors & Alerts

List and inspect monitor states for alerting and SLO overview

📊

Dashboards & Incidents

Discover dashboards and fetch incidents for operational context

🕵️

APM & Traces

Access trace data for latency, dependencies, and service analysis

Available Tools

Quick Reference

Tool	Purpose	Category
`get_metrics`	Query timeseries metrics	Read
`search_logs`	Search logs with filters	Read
`get_monitors`	Retrieve monitor states	Monitoring
`list_dashboards`	List dashboard definitions	Discovery
`get_incidents`	List incidents	Incident

Detailed Usage

get_metrics▶

Query Datadog metrics with flexible time ranges.

use_mcp_tool({
  server_name: "datadog",
  tool_name: "get_metrics",
  arguments: {
    query: "avg:system.cpu.user{*}",
    minutes_back: 30
  }
});

search_logs▶

Search logs with query, time window, pagination, and sorting.

use_mcp_tool({
  server_name: "datadog",
  tool_name: "search_logs",
  arguments: {
    query: "service:api-gateway AND status:error",
    minutes_back: 30,
    limit: 50,
    sort: "-timestamp"
  }
});

get_monitors▶

Retrieve monitor states with optional filters.

use_mcp_tool({
  server_name: "datadog",
  tool_name: "get_monitors",
  arguments: {
    groupStates: ["alert", "warn"]
  }
});

list_dashboards▶

List dashboard definitions for discovery.

use_mcp_tool({
  server_name: "datadog",
  tool_name: "list_dashboards",
  arguments: {}
});

get_incidents▶

List incidents with optional filtering and pagination.

use_mcp_tool({
  server_name: "datadog",
  tool_name: "get_incidents",
  arguments: {
    query: "state:active",
    pageSize: 10
  }
});

Installation

{
  "mcpServers": {
    "datadog": {
      "command": "npx",
      "args": [
        "datadog-mcp-server",
        "--apiKey", "your_api_key",
        "--appKey", "your_app_key",
        "--site", "datadoghq.com"
      ]
    }
  }
}

Regional Sites:

Use your Datadog site, e.g. datadoghq.eu, us3.datadoghq.com, us5.datadoghq.com, ap1.datadoghq.com.

Sources

MCP Implementation Guide

A comprehensive guide to implementing and configuring Model Context Protocol (MCP) servers

Development Tools & DevOps MCP Servers

Streamline your development workflow with our Development Tools & DevOps integrations. Connect with version control systems, CI/CD pipelines, container platforms, and more to build, test, and deploy your AI applications faster and more reliably.

Letta MCP Server

Letta MCP servers enable AI models to interact with the Letta platform, providing capabilities for agent management, memory operations, and tool integration.

Datadog MCP Server