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Datadog MCP Server

Name: Datadog
Availability: InStock
Author: Vinkius

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Monitor applications via Datadog — query performance metrics, search logs, and list active monitors directly from any AI agent.

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What is the Datadog MCP Server?

The Datadog MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Datadog via 11 tools. Monitor applications via Datadog — query performance metrics, search logs, and list active monitors directly from any AI agent. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.

Built-in capabilities (11)

get_dashboardget_monitorlist_dashboardslist_downtimeslist_eventslist_hostslist_monitorslist_slosmute_monitorquery_metricssearch_logs

Tools for your AI Agents to operate Datadog

Ask your AI agent "Show me the CPU usage for 'web-server' over the last 30 minutes" and get the answer without opening a single dashboard. With 11 tools connected to real Datadog data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.

Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.

Why teams choose Vinkius

One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.

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Datadog MCP Server capabilities

11 tools

get_dashboard

Resolves all widget configurations, template variables, and layout structures for visualization rendering. Get dashboard details

get_monitor

Resolves notification settings, threshold values, and historical status changes for the given monitor ID. Get monitor details

list_dashboards

Returns a list of dashboard identifiers, titles, layout types (timeboard/screenboard), and direct access URLs. List all dashboards

list_downtimes

Returns scope tags, recurring schedules, and current status to identify planned maintenance periods. List scheduled downtimes

list_events

Returns a collection of events including titles, priority levels, and source identifiers (e.g., monitor alerts, deployment events). List events

list_hosts

Returns host metadata including agent version, active tags, and associated cloud provider attributes. List infrastructure hosts

list_monitors

Filters results by operational state (alert, warn, no data, ok) and returns monitor metadata including type, query, and current status. List monitors by state

list_slos

Returns SLO definitions including target percentages, time windows, and current compliance status for monitor-based or metric-based objectives. List Service Level Objectives

mute_monitor

Interacts with the alerting boundary to set temporary silence periods, optionally with an automatic expiration timestamp. Mute a monitor

query_metrics

Resolves time-series data within the specified UNIX timestamp range. Returns metric points, scope tags, and unit metadata for infrastructure and application monitoring. Query time-series metrics

search_logs

Interacts with the log storage boundary to retrieve entries matching the query syntax, including timestamps, status levels, and structured attributes. Search application logs

What the Datadog MCP Server unlocks

Connect your Datadog account to any AI agent and take full control of your infrastructure monitoring and log management through natural conversation.

What you can do

Metric Auditing — Execute static queries targeting numeric telemetry datastores to resolve specific DDQL metrics objects generated dynamically
Log Investigation — Perform structural extraction matching target string traces inside Datadog logs to evaluate status boundaries across your apps
Monitor Management — Discover explicit system rule endpoints bounding configured triggers against alert metrics to verify health states
Telemetry Extraction — Fetch timestamp arrays natively from numeric logged endpoints to analyze performance trends over specific time intervals
Log Filtering — Apply ISO boundary mappings to compare logging payloads and identify exactly when errors or bottlenecks occurred

How it works

1. Connect the Datadog integration to your AI assistant.
2. Authorize using your Datadog API Key, APP Key, and Site.
3. Monitor your entire cloud infrastructure using natural language.

Who is this for?

DevOps Engineers — monitor system health and audit alerts without switching to the Datadog dashboard
Software Developers — search through application logs and verify metric telemetry directly from the IDE or chat
SREs — monitor active alerts and analyze performance trends during incident response
System Admins — audit monitor configurations and verify system boundaries through natural language

Frequently asked questions about the Datadog MCP Server

Can my agent query specific Datadog metrics using DDQL?

Yes. Use the 'query_metrics' tool. Provide your DDQL query string and the target time range. The agent will fetch the numeric timeseries data directly from Datadog's telemetry datastores.

How do I search for a specific error message across my application logs?

Use the 'search_logs' tool. Provide a query matching your error string and an ISO time boundary. The agent will retrieve the structural extraction of logs matching those parameters to help you identify failures.

Can I see which monitors are currently in an alert state?

Absolutely. The 'list_monitors' tool allows you to filter by group state (e.g., 'alert,warn'). The agent pulls the explicitly configured system triggers to show you which services are currently unhealthy.