GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
作者提供的说明(英文原文)
Full Datadog API access: monitors, logs, metrics, traces, dashboards, and observability tools
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
DD_API_KEY, DD_APP_KEY, DD_SITE标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 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"
}
}
}
}在终端中运行这条命令
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运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.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"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"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"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
DD_SITE env这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
注册名称已通过 GitHub 账户 tantiope 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
你需要填写:DD_API_KEY, DD_APP_KEY
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.0.9)于11个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:GitHub, Desktop Commander, Cloudflare MCP
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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
只显示了开头部分;其余内容请在仓库中阅读