How to Install Datadog MCP in OpenAI Agents SDK

Official Datadog integration — query metrics, logs, traces, and incidents for full-stack observability

What Datadog MCP does

The official Datadog MCP server gives AI agents access to your full observability stack — metrics, logs, distributed traces, and incidents — enabling autonomous debugging and root cause analysis. Features: - Query metrics with full PromQL/DQL expression support - Search and tail logs in real time - Inspect distributed traces and service maps - View and manage monitors and alert policies - Access incident timelines and postmortems - Query APM service performance data - Inspect infrastructure maps and host health - Run synthetic test results analysis - Create dashboards and widgets programmatically Real-world workflow: 1. Deployment triggers an alert 2. Claude queries Datadog for the specific metric spike 3. Pulls correlated logs from the same time window 4. Identifies the root cause from trace data 5. Proposes a fix — all within the same conversation The fastest path from "something is wrong" to "here's why and how to fix it."

datadog

monitoring

observability

metrics

logs

Installation steps

1Install: pip install openai-agents
2Copy the snippet below into your agent code
3Replace placeholder env values with your real keys
4Pass mcp_servers=[mcp_server] to your Agent()

Python snippet

Python snippetpython

from agents import Agent
from agents.mcp import MCPServerStdio

mcp_server = MCPServerStdio(
    command="npx",
    args=["-y", "@datadog/mcp-server"],
    env={
        "DATADOG_API_KEY": "your_api_key",
        "DATADOG_APP_KEY": "your_app_key",
        "DATADOG_SITE": "datadoghq.com",
    },
)

agent = Agent(
    name="My Agent",
    model="gpt-4o",
    mcp_servers=[mcp_server],
)

Full server details Open Config Editor

Install in other clients

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For maintainers

If you maintain Datadog MCP, add this badge to your README to show it's verified on CuratedMCP:

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