GitHub
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.
Description from the maker (original English)
Full Datadog API access: monitors, logs, metrics, traces, dashboards, and observability tools
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
DD_API_KEY, DD_APP_KEY, DD_SITEValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
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-mcpFill in the empty values before running it.
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.
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.
DD_SITE envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
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
The registry name was verified with the GitHub account tantiope, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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.
You need to fill in: DD_API_KEY, DD_APP_KEY
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).
The GitHub repo last changed –, and the latest version (1.0.9) reached the registry 11 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: GitHub, Desktop Commander, Cloudflare MCP
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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.
MCP server for terminal commands, file operations, and process management
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.
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.
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" }
DD_API_KEY=your-api-key
DD_APP_KEY=your-app-key
DD_SITE=datadoghq.com # Default. Use datadoghq.eu for EU, etc.
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
MCP_TRANSPORT=stdio # stdio | http MCP_PORT=3000 # HTTP port MCP_HOST=0.0.0.0 # HTTP host
--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
| Transport | When to use | Endpoints |
|---|---|---|
stdio (default) | Local MCP clients — Claude Desktop, Cursor, VS Code | n/a (process stdin/stdout) |
http | Remote / container / Kubernetes | POST /mcp · GET /mcp (SSE) · DELETE /mcp · GET /health |
Select with --transport=http or MCP_TRANSPORT=http.
{
"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" } } } }
Use environment variables — not container args — for transport configuration:
env:
Note: Kubernetes
args:replaces the entire Dockerfile CMD, causing Node.js to receive the flags instead of your application. Environment variables avoid this issue.
| Tool | Action | Category | Description | Required Scopes | ||
|---|---|---|---|---|---|---|
monitors | list | Alerting | List monitors with optional filters | monitors_read | ||
monitors | get | Alerting | Get monitor by ID | monitors_read | ||
monitors | search | Alerting | Search monitors by query | monitors_read | ||
monitors | create | Alerting | Create 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 | ||
monitors | update | Alerting | Update 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 | ||
monitors | preview | Alerting | Render 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 | ||
monitors | test_notification | Alerting | Known limitation: returns ENOT_SUPPORTED — Datadog has no public REST endpoint for triggering a test notification. Documentation pointer in response. | n/a | ||
monitors | delete | Alerting | Delete a monitor | monitors_write | ||
monitors | mute | Alerting | Mute a monitor | monitors_write | ||
monitors | unmute | Alerting | Unmute a monitor | monitors_write | ||
monitors | top | Alerting | Top 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 | ||
monitors | history | Alerting | Count 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 | ||
dashboards | list | Visualization | List all dashboards | dashboards_read | ||
dashboards | get | Visualization | Get dashboard by ID | dashboards_read | ||
dashboards | create | Visualization | Create a new dashboard | dashboards_write | ||
dashboards | update | Visualization | Update a dashboard | dashboards_write | ||
dashboards | delete | Visualization | Delete a dashboard | dashboards_write | ||
logs | search | Logs | Search logs with query syntax and filters | logs_read_data, logs_read_index_data | ||
logs | aggregate | Logs | Aggregate log data with groupBy | logs_read_data | ||
logs_pipelines | list, get | Logs Config | Inspect log processing pipelines and their processors | logs_read_config | ||
logs_pipelines | create, update, delete, reorder | Logs Config | Author pipelines and processor chains | logs_write_config | ||
logs_pipelines | get_order | Logs Config | Read pipeline evaluation order | logs_read_config | ||
logs_indexes | list, get | Logs Config | Inspect indexes (filter, retention, Flex tier, exclusion filters); create/delete are UI-only per Datadog and not exposed | logs_read_config | ||
logs_indexes | update, reorder | Logs Config | Update index filter/retention/quota and reorder evaluation | logs_write_config | ||
logs_indexes | get_order | Logs Config | Read index evaluation order | logs_read_config | ||
logs_archives | list, get | Logs Config | Inspect log archives (S3 / GCS / Azure destinations); per-provider credential fields are forwarded unchanged | logs_read_archives | ||
logs_archives | create, update, delete, reorder | Logs Config | Manage archive destinations; destination.type validated against `s3 | gcs | azure_storage` before SDK call | logs_write_archives |
logs_archives | get_order | Logs Config | Read archive evaluation order | logs_read_archives | ||
metrics | query | Metrics | Query 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 | ||
metrics | search | Metrics | Search for metrics by name | metrics_read | ||
metrics | list | Metrics | List active metrics | metrics_read | ||
metrics | metadata | Metrics | Get metric metadata | metrics_read | ||
traces | search | APM | Search spans with filters | apm_read | ||
traces | aggregate | APM | Aggregate trace data | apm_read | ||
traces | services | APM | List APM services | apm_service_catalog_read | ||
events | list | Events | List events | events_read | ||
events | get | Events | Get event by ID | events_read | ||
events | create | Events | Create an event | events_read | ||
events | search | Events | Search 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 | ||
events | histogram | Events | Server-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 | ||
events | aggregate | Events | Client-side aggregation by monitor_name, source, etc. | events_read | ||
events | top | Events | Top 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
Only the beginning is shown; read the rest in the repo