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Datadog MCP Tools for AI Agents

Let an agent search logs and APM spans, inspect a trace, discover usable facets, and aggregate events over a time window.

Available Tools

6 tools available in this integration

Datadog.AggregateEvents

Aggregate log events or spans over a window, optionally grouped and bucketed by time.

Datadog.DiscoverFacets

Probe which standard facets are groupable and filterable for a dataset in a window.

Datadog.GetTrace

Retrieve a single trace assembled with a summary, bounding the span list.

Datadog.ListFacets

Probe which standard facets are groupable and filterable for a dataset in a window.

Datadog.SearchLogs

Search Datadog log events matching a query over a time window.

Datadog.SearchSpans

Search Datadog APM spans matching a query over a time window.

Working directly against the raw API? 588 more tools in the Datadog API server →

Why agent-optimized tools matter

Built for intent, not API schemas

Ask the agent to "find the checkout errors from the last hour and show the trace behind the spike." Arcade picks the right Datadog tools without guessing query syntax or trace IDs.

Up to 100x fewer tokens

Datadog searches target a time window and trace reads bound the span list, so an investigation can focus on matching records and one relevant trace without loading an entire service history into the model.

Complete the workflow end to end

Search logs, search spans, inspect facets, aggregate events, and retrieve a trace through one toolkit designed around the complete observability task.

Make actions predictable and controlled

Arcade tools return structured results and useful errors across users and runs. Teams can apply scope restrictions, redaction, and approval policies so agents follow the same controls every time.

Securing Datadog

The more capabilities agents get, the more useful they are. However, more capabilities bring more risks of undesired effects such as data loss and exfiltration.

This is why Arcade provides you with contextual rules that dictate how and when a tool is allowed to run. Arcade’s contextual access lets you govern tool visibility and behavior at every stage of execution and control who can see a tool, what inputs are allowed, and what comes back.

Learn about Contextual Access Policies

For Datadog, you can implement some of these policies:

Access when tools are listed for a user
  • Decide which tools an agent is allowed to see at all. A tool that is never listed cannot be called, whoever is asking.
Pre-execution before each tool call
  • Inspect the inputs a call is about to run with, and block it or send it for human approval when they fall outside what the agent should do unsupervised.
Post-execution after the tool returns
  • Redact log message bodies, email addresses, access tokens, and authorization headers from Datadog.SearchLogs results before they reach the model.
  • Deny Datadog.GetTrace results when spans contain customer identifiers, session tokens, or request and response bodies covered by your data-handling policy.
  • Strip database statements, HTTP headers, and user identifiers from Datadog.SearchSpans results so private request data does not reach the model.

How to use Datadog with your agent

Pick your MCP client below and follow the setup to connect Datadog to your agent.

Your questions, answered

A Datadog MCP server exposes log and APM capabilities as structured tools an AI agent can call through the Model Context Protocol. Through Arcade, an agent can search logs and spans, inspect facets, aggregate events, and retrieve individual traces.

This integration uses Arcade's MCP endpoint rather than a Datadog-hosted MCP server. Arcade adds credential brokering, approval and redaction policies, scope controls, and one connection path for supported agent frameworks and clients.

Arcade agents can search log events and APM spans within a time window, discover and list groupable or filterable facets, aggregate events or spans by dimensions and time buckets, and retrieve a bounded trace with its summary and spans. These actions are read-only.

Configure one Datadog API key or secret with permission to read the logs and APM data used by your workflows, including searches, aggregations, facets, and traces. The exact access granted depends on the key or secret you configure.

No. This integration uses a shared API key or secret configured once rather than asking each user to connect a personal Datadog account. Arcade brokers the credential, and the model never receives it.

Yes. Contextual Access policies can require human approval before a Datadog call proceeds. The shipped Datadog actions are read-only, so there is no write or destructive action in this integration that needs approval by default.

Arcade proxies Datadog API calls and does not persist your Datadog content as part of normal tool execution. The configured credential is brokered and stored encrypted at rest, and the model never receives it.

Any framework or client that can connect to Arcade's MCP gateway or tool APIs works, including LangChain, OpenAI Agents, CrewAI, Claude Desktop, Claude Code, Cursor, Visual Studio Code, Microsoft Copilot Studio, and GitHub Copilot.

When the configured Datadog key or secret is revoked, rotated, expires, or can no longer be used, calls stop until you reconnect or replace the credential. Arcade cannot refresh a credential that Datadog no longer accepts.

Datadog's API rate limits and quotas apply to searches, aggregations, facet discovery, and trace retrieval made through Arcade. Design repeated investigations around bounded time windows and handle Datadog throttling in the agent.

Ready to actually ship your agent?

Connect your agent to Datadog with a shared API key, agent-optimized tools and approval controls for consequential actions.

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