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

Chroma MCP Server

Chroma MCP servers enable AI models to interact with the Chroma embedding database for vector search, collection management, and document operations.

Git MCP Server

Git MCP servers enable AI models to interact with Git version control systems, providing capabilities for repository management, branch operations, commit handling, and collaborative development workflows.

MongoDB MCP Server

MongoDB MCP servers enable AI models to interact with MongoDB databases and MongoDB Atlas, providing capabilities for document operations, aggregation pipelines, cloud database management, and natural language queries.

Datadog MCP Server