The Datadog MCP server provides AI assistants with full observability access to Datadog, enabling features like log search, APM trace filtering, and cross-correlation of logs, traces, and metrics.
From the registry: Full Datadog API access: monitors, logs, metrics, traces, dashboards, and observability tools
Please install the `datadog-mcp` MCP server into my current AI client (that's you).
Required prerequisites (do these first if not already done):
- **Datadog API and App keys** — Create an API key and Application key in your Datadog account with the appropriate scopes. (https://app.datadoghq.com/organization-settings/api-keys)
Canonical MCP server config (stdio transport):
- command: `npx`
- args: ["-y","datadog-mcp"]
- required environment variables:
- `DD_API_KEY`: Datadog API key (example: `<your_dd_api_key>`)
- `DD_APP_KEY`: Datadog Application key (example: `<your_dd_app_key>`)
- optional environment variables:
- `DD_SITE`: Datadog site (e.g. datadoghq.com, datadoghq.eu, us3.datadoghq.com) (example: `datadoghq.com`)
- `MCP_DEFAULT_LIMIT`: General fallback limit (example: `50`)
- `MCP_DEFAULT_LOG_LINES`: Default log lines to return (example: `200`)
- `MCP_DEFAULT_METRIC_POINTS`: Default metric timeseries data points (example: `1000`)
- `MCP_DEFAULT_TIME_RANGE`: Default time range in hours (example: `24`)
Note: Community-maintained; not officially affiliated with Datadog.
Add this MCP server to my current client's config in the correct format for you. If you need secrets or credentials I haven't provided, ASK me — do not invent values or leave raw placeholders. After adding it, tell me how to verify the server is connected.DD_API_KEYrequiredAPI key for Datadog access.DD_APP_KEYrequiredApplication key for Datadog access.Real-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.
Persistent codebase knowledge graph. Survives session restarts and context compaction.
Validate oh-my-posh configurations and segment snippets against the official schema.