Datadog MCP Server

The Datadog MCP Server is Datadog's official Model Context Protocol server for AI-powered observability workflows. It connects MCP-capable AI clients such as Claude Code, Cursor, OpenAI Codex, or Gemini CLI to the Datadog observability platform. The direct product link points to the official Datadog documentation; the open-source repository datadog-labs/mcp-server contains source code, examples, and the license. According to the provider, the server acts as a bridge between observability data in Datadog and AI agents that need structured access to logs, metrics, traces, monitors, dashboards, and incidents.

What is the Datadog MCP Server

The Datadog MCP Server allows AI agents to access observability data directly, without switching between an IDE and the Datadog UI. According to the provider, it is HIPAA-eligible and supports a wide range of MCP clients. It runs exclusively as a hosted remote server and is accessible at mcp.datadoghq.com. EU customers use mcp.datadoghq.eu instead. According to Datadog, the server does not send data to third-party AI providers; the connected AI client determines which Datadog data is forwarded to its model provider. This distinction matters in data-sensitive environments: the MCP Server itself only processes tool calls and returns results, not prompts or model reasoning.

Prerequisites

Using the Datadog MCP Server requires an active Datadog account with appropriate permissions and an MCP-capable AI client. According to the provider, the server applies the authenticated user's existing access controls: role-based access control (RBAC), data access control, and log restriction queries apply exactly as they do for direct API or UI access. The server cannot grant a user access beyond their existing permission profile. Write operations require the corresponding permission, such as monitors_write, and are checked on each tool call.

Features: Logs, Metrics, Monitors, Dashboards, and Incidents

The core toolset includes, according to the provider, tools for logs, metrics, traces, dashboards, monitors, incidents, hosts, services, events, and notebooks. Additional toolsets can be enabled for specific areas: alerting for monitor validation and SLO search, dashboards for creating, updating, and deleting dashboards, DDSQL for structured queries over infrastructure resources and log data, error tracking, feature flags, Kubernetes resources, network monitoring, real user monitoring, synthetics, software delivery metrics, and workflow automation. The toolset system allows loading only the tools actually needed, preserving the AI client's context window. Using toolsets=all enables all generally available toolsets at once.

Setup

For Claude Code on the US1 site, a single command suffices: claude mcp add --transport http datadog https://mcp.datadoghq.com/v1/mcp. Many other clients can be configured through a .mcp.json file. The server uses Streamable HTTP as its primary transport and OAuth for authentication. Specific toolsets can be restricted using query parameters, for example ?toolsets=core,dashboards. Individual tools can be excluded with omit_tools without disabling the entire toolset. The Codex CLI uses different configuration parameters via HTTP headers.

Usage limits per the provider

According to the provider, the following fair-use limits apply: a burst limit of 50 requests per 10 seconds and 100,000 monthly tool calls. These limits can be adjusted on request. GovCloud is currently not compatible. Datadog states in its privacy notice that it collects certain usage data from the remote server, including interaction patterns, errors, and user identifiers, in accordance with the Datadog Privacy Policy. This data is retained for 120 days.

Security and data access

According to the provider, the server forwards the authenticated user's own credentials to Datadog APIs. Existing access controls apply automatically. For sensitive data categories such as logs or APM spans, Datadog recommends using data access control and log restriction queries to limit what the MCP Server returns. Because the AI client controls what is sent to its model provider, reducing data access also reduces potential exposure. Write tools require explicit permissions and are checked per call. A read-only user cannot execute write-enabled tools.

FAQ

Is the Datadog MCP Server free? The server itself is open source (MIT license) and access is available with an existing Datadog account. Current plan and usage terms are published at datadoghq.com/pricing. Costs arise from Datadog products, resource usage, the selected plan, and the chosen AI client — not from the MCP Server itself.

Can I run the server locally? The official deployment is exclusively as a hosted remote server operated by Datadog. The repository contains agent integration examples but does not provide a standalone local server mode as an official product variant.

How many tools are available? The number depends on the enabled toolsets. The core toolset covers the most common use cases; toolsets=all loads all generally available toolsets. The complete tool reference is available in the Datadog documentation.

Active Datadog account, appropriate permissions (RBAC), and an MCP-capable AI client such as Claude Code, Cursor, or Codex.

For Claude Code on US1: claude mcp add --transport http datadog https://mcp.datadoghq.com/v1/mcp. EU customers use mcp.datadoghq.eu. Configure other clients via .mcp.json or client-specific instructions.

claude mcp add --transport http datadog https://mcp.datadoghq.com/v1/mcp

OAuth flow through Datadog login; alternatively API key depending on the client. Existing RBAC permissions are applied directly.

Permissions follow the authenticated Datadog user; no access beyond existing permissions is possible. Write tools are checked on each call.

The server receives tool calls and returns results. Datadog data is not sent directly to AI providers; the client controls that.

Broad permissions, enabled write tools, or missing data access control can expose sensitive observability data or trigger changes to monitors and dashboards.