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Start a free trial on cloud.anythingmcp.com, add the Datadog in one click, then point your AI client (Claude, ChatGPT, Copilot or Cursor) at the generated MCP endpoint. No Docker, no git clone, zero engineering experience required.
Summary
Query Datadog (metrics, logs, monitors, events, incidents, dashboards) from any AI agent. 8 tools, dual-key auth.
Try asking
Example prompts for Datadog
Click any prompt to copy it. Paste into Claude, ChatGPT, Cursor, Gemini, Copilot or OpenClaw to run it against this connector.
Claude is AI and can make mistakes. Please double-check responses.
💡 No install? Use cloud.anythingmcp.com directly. Sign in, click Connectors → Datadog, paste your credentials, mint an MCP API key — done. No Docker, no
git clone, no local server.
Datadog + Gemini
Query Datadog (metrics, logs, monitors, events, incidents, dashboards) from any AI agent. 8 tools, dual-key auth.
Prerequisites
See the full setup instructions baked into the connector (visible in the in-app store when you select the connector). The required environment variables for this connector are:
DATADOG_API_KEY, DATADOG_APP_KEY
Step 1 — Get credentials
This connector wraps the Datadog REST API v1/v2 (api.datadoghq.com).
Setup:
- In your Datadog org → Personal Settings → Organization Settings → API Keys → create or copy an API key. Set
DATADOG_API_KEY. - Organization Settings → Application Keys → create an application key with at least
monitors_read,logs_read_data,dashboards_read,events_readscopes. SetDATADOG_APP_KEY. - Site matters: Datadog has multiple regions: US1 (datadoghq.com — default), US3 (us3.datadoghq.com), US5 (us5.datadoghq.com), EU1 (datadoghq.eu), AP1 (ap1.datadoghq.com), US1-FED (ddog-gov.com). If your org is on a non-US1 site, change
baseUrlaccordingly. …
Step 2 — Install the adapter
git clone https://github.com/HelpCode-ai/anythingmcp.git
cd anythingmcp && docker compose up -d
Step 3 — Add the connector in Gemini
Gemini CLI reads MCP servers from ~/.gemini/settings.json (or %APPDATA%\gemini\settings.json on Windows). Add:
{
"mcpServers": {
"anythingmcp": {
"httpUrl": "https://cloud.anythingmcp.com/mcp",
"headers": { "Authorization": "Bearer YOUR_MCP_API_KEY" }
}
}
}
- Get your MCP API key from AnythingMCP → Profile → MCP API Keys → New Key.
- Save the file and restart
gemini. - Run
/mcpinside the Gemini CLI —Datadogshould be listed as available. - Vertex AI Studio: pass
https://cloud.anythingmcp.com/mcpto thetoolsarray of your request with the same Bearer header.
Available tools
| Tool | What it does |
|---|---|
datadog_query_metrics | Run a metrics query over a time window |
datadog_search_logs | Search logs using the Logs Search syntax |
datadog_list_monitors | List monitors |
datadog_get_monitor | Get a single monitor by ID with full definition (query, thresholds, notifications, state) |
datadog_list_dashboards | List all dashboards in the org |
datadog_get_dashboard | Fetch a dashboard's full layout: widgets, queries, template variables |
datadog_list_events | List events in a time range |
datadog_post_event | Post an event to the Datadog event stream (useful for marking deploys, releases, manual incidents) |
FAQ
Does Gemini 1.5 Pro or 2.x support MCP? Yes — Gemini CLI ≥ 0.4 and Vertex AI tools API both accept MCP httpUrl connectors with Bearer headers.
Next steps
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