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PopNx MCPMCP servers for people who use AI · explained in plain words

Datadog MCP

by tantiopeversion 1.0.9i★ –iRuns on your computer
Registry nameio.github.TANTIOPE/datadog-mcp

Install

Overview

Description from the maker (original English)

Full Datadog API access: monitors, logs, metrics, traces, dashboards, and observability tools

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Developer tools
  • You need to fill in: DD_API_KEY, DD_APP_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: DD_API_KEY, DD_APP_KEY, DD_SITE

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "datadog": {
      "command": "npx",
      "args": [
        "-y",
        "datadog-mcp"
      ],
      "env": {
        "DD_API_KEY": "your-value",
        "DD_APP_KEY": "your-value",
        "DD_SITE": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add datadog --env DD_API_KEY=your-value --env DD_APP_KEY=your-value --env DD_SITE=your-value -- npx -y datadog-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "datadog": {
      "command": "npx",
      "args": [
        "-y",
        "datadog-mcp"
      ],
      "env": {
        "DD_API_KEY": "your-value",
        "DD_APP_KEY": "your-value",
        "DD_SITE": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "datadog": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "datadog-mcp"
      ],
      "env": {
        "DD_API_KEY": "your-value",
        "DD_APP_KEY": "your-value",
        "DD_SITE": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • DD_API_KEY secret required env
    Datadog API key
  • DD_APP_KEY secret required env
    Datadog Application key
  • DD_SITE env
    Datadog site (e.g., datadoghq.com, datadoghq.eu, us3.datadoghq.com)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account tantiope, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

  • More stars than 0% of the other 5541 servers in this category
  • Published to the registry more recently than 2% of the other 5541 servers in this category
  • Basic checks equal to or better than 100% of the other 5541 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Datadog MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: DD_API_KEY, DD_APP_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.9) reached the registry 11 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Datadog MCP Server

DISCLAIMER: This is a community-maintained project and is not officially affiliated with, endorsed by, or supported by Datadog, Inc. This MCP server utilizes the Datadog API but is developed independently.

MCP server providing AI assistants with full Datadog observability access. Features grep-like log search, APM trace filtering with duration/status/error queries, smart sampling modes for token efficiency, and cross-correlation between logs, traces, and metrics. Supports both stdio (local) and http (remote/Kubernetes) transports.

Quick Start

Minimal Claude Desktop / VS Code / Cursor config — just the two required keys:

{

"mcpServers": { "datadog": { "command": "npx", "args": ["-y", "datadog-mcp"], "env": { "DD_API_KEY": "your-api-key", "DD_APP_KEY": "your-app-key" } } } }

With optional tuning (EU site, custom default limits, longer log windows):

{

"mcpServers": { "datadog": { "command": "npx", "args": ["-y", "datadog-mcp"], "env": { "DD_API_KEY": "your-api-key", "DD_APP_KEY": "your-app-key", "DD_SITE": "datadoghq.eu", "MCP_DEFAULT_LIMIT": "50", "MCP_DEFAULT_LOG_LINES": "200", "MCP_DEFAULT_METRIC_POINTS": "1000", "MCP_DEFAULT_TIME_RANGE": "24" } } } }

To run as an HTTP server (e.g. inside a container or Kubernetes pod), add transport variables to the same env block:

"env": {

"DD_API_KEY": "your-api-key", "DD_APP_KEY": "your-app-key", "MCP_TRANSPORT": "http", "MCP_PORT": "3000", "MCP_HOST": "0.0.0.0" }

Configuration

Required environment variables
DD_API_KEY=your-api-key

DD_APP_KEY=your-app-key

Optional environment variables
DD_SITE=datadoghq.com  # Default. Use datadoghq.eu for EU, etc.

Limit defaults (fallbacks when the AI doesn't specify)

MCP_DEFAULT_LIMIT=50 # General tools default limit MCP_DEFAULT_LOG_LINES=200 # Logs tool default limit MCP_DEFAULT_METRIC_POINTS=1000 # Metrics timeseries data points MCP_DEFAULT_TIME_RANGE=24 # Default time range in hours

Transport (alternative to CLI flags — useful in Kubernetes)

MCP_TRANSPORT=stdio # stdio | http MCP_PORT=3000 # HTTP port MCP_HOST=0.0.0.0 # HTTP host

Optional flags
--site=datadoghq.com     # Datadog site (overrides DD_SITE)

--transport=stdio|http # Transport mode (default: stdio) --port=3000 # HTTP port when using http transport --host=0.0.0.0 # HTTP host when using http transport --read-only # Block all write operations --disable-tools=synthetics,rum,security # Comma-separated list of tools to disable

Transports

TransportWhen to useEndpoints
stdio (default)Local MCP clients — Claude Desktop, Cursor, VS Coden/a (process stdin/stdout)
httpRemote / container / KubernetesPOST /mcp · GET /mcp (SSE) · DELETE /mcp · GET /health

Select with --transport=http or MCP_TRANSPORT=http.

Deployment

Docker
{

"mcpServers": { "datadog": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "DD_API_KEY", "-e", "DD_APP_KEY", "-e", "DD_SITE", "ghcr.io/tantiope/datadog-mcp" ], "env": { "DD_API_KEY": "your-api-key", "DD_APP_KEY": "your-app-key", "DD_SITE": "datadoghq.com" } } } }

Kubernetes

Use environment variables — not container args — for transport configuration:

env:
  • name: DD_API_KEY value: "your-api-key"
  • name: DD_APP_KEY value: "your-app-key"
  • name: MCP_TRANSPORT value: "http"
  • name: MCP_PORT value: "3000"
  • name: MCP_HOST value: "0.0.0.0"

Note: Kubernetes args: replaces the entire Dockerfile CMD, causing Node.js to receive the flags instead of your application. Environment variables avoid this issue.

Tools

ToolActionCategoryDescriptionRequired Scopes
monitorslistAlertingList monitors with optional filtersmonitors_read
monitorsgetAlertingGet monitor by IDmonitors_read
monitorssearchAlertingSearch monitors by querymonitors_read
monitorscreateAlertingCreate a new monitor; config is validated against a typed schema covering documented options (notifyNoData, renotifyInterval, thresholds, …) — unknown keys surface in warnings. Pass dry_run: true to validate without creating (uses /api/v1/monitor/validate, allowed in read-only mode).monitors_write
monitorsupdateAlertingUpdate an existing monitor; same validated schema as create; partial configs accepted; validation errors short-circuit before any HTTP call as EINVALID_MONITOR_CONFIG:monitors_write
monitorspreviewAlertingRender a monitor template (inline message or by monitor_id/id) with optional context of variables and conditionals. Returns {rendered, variablesUsed, variablesMissing, conditionalsResolved, tagConditionalsResolved}. Supports Datadog Mustache subset: variable substitution + six documented conditionals (is_alert, is_warning, is_no_data, is_recovery, is_alert_to_warning, is_warning_to_alert) + tag conditionals {{#is_match "tag" "val"}}/{{#is_exact_match "tag" "val"}} (and ^ negations); {{#each}}/partials throw EUNSUPPORTED_TEMPLATE_SYNTAX. Read-only.monitors_read
monitorstest_notificationAlertingKnown limitation: returns ENOT_SUPPORTED — Datadog has no public REST endpoint for triggering a test notification. Documentation pointer in response.n/a
monitorsdeleteAlertingDelete a monitormonitors_write
monitorsmuteAlertingMute a monitormonitors_write
monitorsunmuteAlertingUnmute a monitormonitors_write
monitorstopAlertingTop N monitors by alert frequency with real monitor names and context breakdown. WARNING: total_count includes renotifies/re-evaluations (Datadog emits a renotify event every renotify_interval minutes while Alert). For real fires use action=history.monitors_read
monitorshistoryAlertingCount and list real state transitions for one monitor over a time window. Filters by transitionType (default ["alert","alert recovery"] — fires+recoveries, excludes renotifies) and optional group. Returns {transitions: [...], count, meta} where count is the number of real transitions (e.g. for one always-Alert burn-rate monitor over 7d: 98 raw events vs 38 real transitions).monitors_read, events_read
dashboardslistVisualizationList all dashboardsdashboards_read
dashboardsgetVisualizationGet dashboard by IDdashboards_read
dashboardscreateVisualizationCreate a new dashboarddashboards_write
dashboardsupdateVisualizationUpdate a dashboarddashboards_write
dashboardsdeleteVisualizationDelete a dashboarddashboards_write
logssearchLogsSearch logs with query syntax and filterslogs_read_data, logs_read_index_data
logsaggregateLogsAggregate log data with groupBylogs_read_data
logs_pipelineslist, getLogs ConfigInspect log processing pipelines and their processorslogs_read_config
logs_pipelinescreate, update, delete, reorderLogs ConfigAuthor pipelines and processor chainslogs_write_config
logs_pipelinesget_orderLogs ConfigRead pipeline evaluation orderlogs_read_config
logs_indexeslist, getLogs ConfigInspect indexes (filter, retention, Flex tier, exclusion filters); create/delete are UI-only per Datadog and not exposedlogs_read_config
logs_indexesupdate, reorderLogs ConfigUpdate index filter/retention/quota and reorder evaluationlogs_write_config
logs_indexesget_orderLogs ConfigRead index evaluation orderlogs_read_config
logs_archiveslist, getLogs ConfigInspect log archives (S3 / GCS / Azure destinations); per-provider credential fields are forwarded unchangedlogs_read_archives
logs_archivescreate, update, delete, reorderLogs ConfigManage archive destinations; destination.type validated against `s3gcsazure_storage` before SDK calllogs_write_archives
logs_archivesget_orderLogs ConfigRead archive evaluation orderlogs_read_archives
metricsqueryMetricsQuery timeseries data. Response meta includes rollupRequested (parsed from rollup(method, seconds), with methodInferred flag), rollupEffective (interval derived from returned pointlist intervals + deduped intervalsObserved for multi-series), and rollupOverridden: boolean so callers can detect when Datadog silently downsampled.metrics_read, timeseries_query
metricssearchMetricsSearch for metrics by namemetrics_read
metricslistMetricsList active metricsmetrics_read
metricsmetadataMetricsGet metric metadatametrics_read
tracessearchAPMSearch spans with filtersapm_read
tracesaggregateAPMAggregate trace dataapm_read
tracesservicesAPMList APM servicesapm_service_catalog_read
eventslistEventsList eventsevents_read
eventsgetEventsGet event by IDevents_read
eventscreateEventsCreate an eventevents_read
eventssearchEventsSearch events with v2 API and cursor pagination. Optional transitionType filter (e.g. ["alert","alert recovery"]) restricts to monitor state-transition events — without it, source:alert includes renotifies. For monitor-specific fires use monitors action=history. Optional timezone adds *Local ISO 8601 siblings to every timestamp. Zero-result responses include a diagnostics array hinting at the cause (UNINDEXED_TAG_PREFIX, NARROW_TIME_RANGE, RESTRICTIVE_SOURCE_FILTER).events_read
eventshistogramEventsServer-side bucketing of events by hour_of_day, day_of_week, or day_of_month in an IANA timezone (DST-safe via Intl.DateTimeFormat). Accepts the same transitionType filter as search so monitor histograms can exclude renotifies. Cursor-paginates the underlying search; cap at limits.maxEventsForHistogram (default 5000, MCP_MAX_EVENTS_HISTOGRAM env var). When the cap is hit, returns bucketCountIncomplete: true and nextCursor for continuation.events_read
eventsaggregateEventsClient-side aggregation by monitor_name, source, etc.events_read
eventstopEventsTop N event groups by count with generic groupBy support (deployments, configs, alerts, etc.). Groups without context tags are included as "no_context"events_read

| events | timeseries | Events | Time-bucketed alert

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