How to integrate Datadog MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Datadog to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Datadog agent that can create downtime for nightly maintenance window, list all monitors tracking cpu usage, create synthetic api test for login endpoint, get details of production dashboard through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Datadog account through Composio's Datadog MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Datadog
  • Configure an AI agent that can use Datadog as a tool
  • Run a live chat session where you can ask the agent to perform Datadog operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

What is the Datadog MCP server, and what's possible with it?

The Datadog MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Datadog account. It provides structured and secure access to your monitoring and observability platform, so your agent can perform actions like creating dashboards, managing monitors, scheduling downtimes, and tracking key events on your behalf.

  • Custom dashboard creation and management: Direct your agent to build new dashboards or retrieve detailed information about existing dashboards for unified infrastructure and application monitoring.
  • Monitor setup and deletion: Easily have your agent create new monitors to track critical metrics or remove outdated ones to keep your alerting system relevant.
  • Automated downtime scheduling: Let your agent schedule maintenance windows by creating downtimes that suppress alerts during planned outages or deployments.
  • Event tracking and logging: Ask your agent to create and log significant events—like deployments or configuration changes—so your team always stays informed.
  • Service level objectives and synthetic testing: Instruct your agent to define SLOs or set up synthetic API tests for continuous reliability and performance tracking.

Supported Tools & Triggers

Tools
Create DashboardCreate a dashboard in datadog.
Create downtimeCreates a new downtime in datadog to suppress alerts during maintenance windows or planned outages.
Create eventCreates a new event in datadog.
Create monitorCreates a new datadog monitor to track metrics, logs, or other data sources with configurable alerting thresholds and notifications.
Create SLOCreate a service level objective (slo) in datadog.
Create Synthetic API TestCreate a synthetic api test in datadog.
Create WebhookCreate a webhook in datadog.
Delete DashboardDelete a dashboard in datadog.
Delete monitorDeletes a datadog monitor permanently.
Get DashboardGet a specific dashboard from datadog.
Get monitorRetrieves detailed information about a specific datadog monitor, including its current state, configuration, and any active downtimes.
Get Service DependenciesGet service dependency mapping from datadog apm.
Get Synthetics LocationsTool to retrieve all available public and private locations for synthetic tests in datadog.
Get host tagsRetrieves all tags associated with a specific host in datadog.
Get Trace by IDGet detailed information about a specific trace by its id.
Get usage summaryRetrieves usage summary information from datadog including api calls, hosts, containers, and other billable usage metrics.
List All TagsList all tags from datadog.
List API KeysList api keys in datadog.
List APM ServicesList apm services from datadog.
List AWS IntegrationList aws integrations in datadog.
List dashboardsLists all datadog dashboards with basic information.
List eventsLists events from datadog within a specified time range.
List hostsLists all hosts in your datadog infrastructure with detailed information including metrics, tags, and status.
List IncidentsList incidents from datadog.
List Log IndexesTool to retrieve a list of all log indexes configured in datadog.
List monitorsGet all monitor details.
List RolesList roles from datadog organization.
List service checksLists service checks from datadog.
List SLOsList service level objectives (slos) from datadog.
List Synthetics TestsList synthetics tests from datadog.
List UsersList users from datadog organization.
List WebhooksList webhooks from datadog.
Mute MonitorMute a monitor in datadog.
Query metricsQueries datadog metrics and returns time series data.
Search logsSearches datadog logs with advanced filtering capabilities.
Search Spans AnalyticsSearch and analyze span data with aggregations in datadog.
Search TracesSearch for traces in datadog apm.
Submit metricsSubmits custom metrics to datadog.
Unmute MonitorUnmute a monitor in datadog.
Update DashboardUpdate a dashboard in datadog.
Update host tagsUpdates tags for a specific host in datadog.
Update monitorUpdates an existing datadog monitor with new configuration, thresholds, or notification settings.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Datadog project
  • Some knowledge of Python or Typescript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Datadog.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Datadog Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["datadog"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only datadog.
  • The router checks the user's Datadog connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Datadog.
  • This approach keeps things lightweight and lets the agent request Datadog tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Datadog. "
        "Help users perform Datadog operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Datadog and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Datadog operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Datadog.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Datadog and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["datadog"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Datadog. "
        "Help users perform Datadog operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Datadog MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Datadog.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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FAQ

What are the differences in Tool Router MCP and Datadog MCP?

With a standalone Datadog MCP server, the agents and LLMs can only access a fixed set of Datadog tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Datadog and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Datadog tools.

Can I manage the permissions and scopes for Datadog while using Tool Router?

Yes, absolutely. You can configure which Datadog scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Datadog data and credentials are handled as safely as possible.

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