Datadog MCP server - 165 tools for metrics, monitors, logs, APM, RUM, incidents, CI/CD, status pages, fleet automation, and more
Connect Datadog to Claude, Cursor or any other MCP client and it stops being a tab you switch to. Datadog MCP server - 165 tools for metrics, monitors, logs, APM, RUM, incidents, CI/CD, status pages, fleet automation, and more. The datadog mcp server is what makes that connection.
Connect the server to Claude Desktop or Claude Code, then paste any of these:
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
Token — efficient by design** — extractFields projection, DD_TOOLS/DD_DISABLE 16-category toggles, and a search-tools meta-tool keep LLM context low acrosstriage-incident — given an incident ID, walks linked monitors, recent error spikes, and recent deploysaudit-monitor-noise — flag flapping monitors via alert frequency × MTTRanalyze-rum-error-spike — diff RUM error rates across two windows, attribute to top error groupsinvestigate-slow-trace — given a slow trace ID, traverse the span tree and surface bottleneck spansextractFields — token-efficient response projectionscreateWrapToolHandler — domain-specific redaction (DD_API_KEY/DD_APP_KEY) + Datadog ApiException error extractionsearch — tools meta-toolDeployment — Remote (managed by Datadog)Auth — Datadog SSOSites — Public Datadog sitesVariable — Required@us-all/datadog-mcp on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Configuration is passed through the environment: DD_API_KEY, DD_APP_KEY, DD_SITE, MCP_HTTP_TOKEN, MCP_HTTP_HOST. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Among the monitoring and observability options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Datadog's toolset — Token, triage-incident, audit-monitor-noise and 11 more — is a fair guide to whether it matches your workflow. It is maintained by GitHub Actions; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Token | efficient by design** — extractFields projection, DD_TOOLS/DD_DISABLE 16-category toggles, and a search-tools meta-tool keep LLM context low across 163 tools. |
| triage-incident | given an incident ID, walks linked monitors, recent error spikes, and recent deploys. |
| audit-monitor-noise | flag flapping monitors via alert frequency × MTTR. |
| analyze-rum-error-spike | diff RUM error rates across two windows, attribute to top error groups. |
| investigate-slow-trace | given a slow trace ID, traverse the span tree and surface bottleneck spans. |
| extractFields | token-efficient response projections |
| createWrapToolHandler | domain-specific redaction (DD_API_KEY/DD_APP_KEY) + Datadog ApiException error extraction |
| search | tools meta-tool |
| Deployment | Remote (managed by Datadog) |
| Auth | Datadog SSO |
| Sites | Public Datadog sites |
| Variable | Required |
| DD_API_KEY | ✅ |
| DD_APP_KEY | ✅ |
{
"mcpServers": {
"datadog": {
"command": "npx",
"args": ["-y", "@us-all/datadog-mcp"],
"env": {
"DD_API_KEY": "<your-api-key>",
"DD_APP_KEY": "<your-app-key>",
"DD_SITE": "datadoghq.com"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| DD_API_KEY | Credential the server authenticates with. | Yes |
| DD_APP_KEY | Credential the server authenticates with. | Yes |
| DD_SITE | Configuration value read at startup. | Optional |
| MCP_HTTP_TOKEN | Credential the server authenticates with. | Yes |
| MCP_HTTP_HOST | Endpoint or connection string the server talks to. | Optional |
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.