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Datadog MCP server

Datadog is the essential monitoring and observability platform for cloud-scale applications, providing comprehensive visibility across infrastructure, applications, logs, and user experience. With this MCP server, AI agents can query metrics, search logs, manage monitors, create dashboards, and generate insights through natural language commands.

Setting up an MCP server

This article covers the standard steps for creating an MCP server in AI Gateway and connecting it to an AI client. The steps are the same for every integration — application-specific details (API credentials, OAuth endpoints, and scopes) are covered in the individual application pages.

Before you begin​

You'll need:

  • Access to AI Gateway with permission to create MCP servers
  • API credentials for the application you're connecting (see the relevant application page for what to collect)

Create an MCP server​

Find the API in the catalog​

  1. Sign in to AI Gateway and select MCP Servers from the left navigation.
  2. Select New MCP Server.
  3. Search for the application you want to connect, then select it from the catalog.

Configure the server​

  1. Enter a Name for your server — something descriptive that identifies both the application and its purpose (for example, "Zendesk Support — Prod").
  2. Enter a Description so your team knows what the server is for.
  3. Set the Timeout value. 30 seconds works for most APIs; increase to 60 seconds for APIs that return large payloads.
  4. Toggle Production mode on if this server will be used in a live workflow.
  5. Select Next.

Configure authentication​

Enter the authentication details for the application. This varies by service — see the Authentication section of the relevant application page for the specific credentials, OAuth URLs, and scopes to use.

Configure security​

  1. Set any Rate limits appropriate for your use case and the API's own limits.
  2. Enable Logging if you want AI Gateway to record requests and responses for auditing.
  3. Select Next.

Deploy​

Review the summary, then select Deploy. AI Gateway provisions the server and provides a server URL you'll use when configuring your AI client.

Connect to an AI client​

Once your server is deployed, you'll need to add it to the AI client your team uses. Select your client for setup instructions:

Tips​

  • You can create multiple MCP servers for the same application — for example, a read-only server for reporting agents and a read-write server for automation workflows.
  • If you're unsure which OAuth scopes to request, start with the minimum read-only set and add write scopes only when needed. Most application pages include scope recommendations.
  • You can edit a server's name, description, timeout, and security settings after deployment without redeploying.

Authentication​

Datadog uses API key and application key authentication. Generate both from your Datadog organization settings.

  • API Key Header: DD-API-KEY: {your-api-key}
  • Application Key Header: DD-APPLICATION-KEY: {your-application-key}
  • Where to generate: Organization Settings > API Keys and Application Keys
  • API Endpoint: Depends on your Datadog region (US1, EU1, US3, US5, AP1)

Regions and endpoints:

  • US1: https://api.datadoghq.com
  • EU1: https://api.datadoghq.eu
  • US3: https://api.us3.datadoghq.com
  • US5: https://api.us5.datadoghq.com
  • AP1: https://api.ap1.datadoghq.com

Available tools​

The Datadog MCP server exposes metrics, logs, monitors, dashboards, events, APM, and synthetic monitoring APIs.

ToolPurpose
Metrics QueryRetrieve time series data; aggregate metrics across tags; run calculations and transformations
Logs SearchSearch log events; aggregate log data; access log archives; configure pipelines
MonitorsCreate and manage monitors; configure alert conditions; set notification rules
DashboardsCreate custom dashboards; add widgets (time series, query values, heatmaps); share dashboards
EventsSubmit custom events; query event stream; correlate with metrics; tag and filter events
APM ServicesView service dependencies; track performance metrics; analyze error rates; manage SLOs
Synthetic TestsCreate API tests; run multi-step browser tests; schedule from global locations

Tips​

Use consistent tagging conventions across your infrastructure.

Tag by environment and application and leverage tag-based filtering for faster queries.

Set thresholds that reduce alert fatigue.

Use appropriate alert evaluation windows and configure escalation policies for critical services.

Use composite alerts for multi-condition scenarios.

Create focused dashboards for specific use cases rather than overly broad dashboards.

Use template variables for dynamic filtering and organize dashboards by team or service.

Use log pipelines to extract and parse important fields.

Configure log indexes to balance retention and cost.

Create saved views for common search patterns.

Set up synthetic tests from multiple geographic locations.

Track key user journeys through your application.

Use error tracking to understand failure patterns.

Configure SLO alerts for critical services.