How to integrate Datadog MCP with Pydantic AI

This guide walks you through connecting Datadog to Pydantic AI 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 through natural language commands. This guide will help you understand how to give your Pydantic AI 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.

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Datadog

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Datadog is a cloud monitoring and observability platform for applications and infrastructure. It helps teams detect issues and optimize performance by unifying metrics, logs, and traces.

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Introduction

This guide will help you understand how to give your Pydantic AI 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.

Also integrate Datadog with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

How to set up your Composio API key and User ID
How to create a Composio Tool Router session for Datadog
How to attach an MCP Server to a Pydantic AI agent
How to stream responses and maintain chat history
How to build a simple REPL-style chat interface to test your Datadog workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

Type Safety: Built on Pydantic for automatic data validation
MCP Support: Native support for Model Context Protocol servers
Streaming: Built-in support for streaming responses
Async First: Designed for async/await patterns

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.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

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

Step-by-step Guide

Step by step09 STEPS

Prerequisites

Before starting, make sure you have:

Python 3.9 or higher
A Composio account with an active API key
Basic familiarity with Python and async programming

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

Log in to the Composio dashboard.
Navigate to your API settings and generate a new API key.
Store this key securely as you'll need it for authentication.

Install dependencies

bash

pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

composio connects your agent to external SaaS tools like Datadog
pydantic-ai lets you create structured AI agents with tool support
python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

COMPOSIO_API_KEY authenticates your agent to Composio's API
USER_ID associates your session with your account for secure tool access
OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

What's happening:

We load environment variables and import required modules
Composio manages connections to Datadog
MCPServerStreamableHTTP connects to the Datadog MCP server endpoint
Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Datadog
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["datadog"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

What's happening:

We're creating a Tool Router session that gives your agent access to Datadog tools
The create method takes the user ID and specifies which toolkits should be available
The returned session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python

# Attach the MCP server to a Pydantic AI Agent
datadog_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[datadog_mcp],
    instructions=(
        "You are a Datadog assistant. Use Datadog tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)

What's happening:

The MCP client connects to the Datadog endpoint
The agent uses GPT-5 to interpret user commands and perform Datadog operations
The instructions field defines the agent's role and behavior

Build the chat interface

python

# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Datadog.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()

What's happening:

The agent reads input from the terminal and streams its response
Datadog API calls happen automatically under the hood
The model keeps conversation history to maintain context across turns

Run the application

python

if __name__ == "__main__":
    asyncio.run(main())

What's happening:

The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Datadog and Pydantic AI:

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Datadog
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["datadog"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    datadog_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[datadog_mcp],
        instructions=(
            "You are a Datadog assistant. Use Datadog tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Datadog.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Datadog through Composio's Tool Router. With this setup, your agent can perform real Datadog actions through natural language. You can extend this further by:

Adding other toolkits like Gmail, HubSpot, or Salesforce
Building a web-based chat interface around this agent
Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Datadog for workflow automation)

This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

TOOLS

Supported Tools

Every Datadog action and event your agent gets out of the box.

Create Dashboard

Create a dashboard in Datadog.

Create downtime

Creates a new downtime in Datadog to suppress alerts during maintenance windows or planned outages.

Create event

Creates a new event in Datadog.

Create monitor

Creates a new Datadog monitor to track metrics, logs, or other data sources with configurable alerting thresholds and notifications.

Create SLO

Create a Service Level Objective (SLO) in Datadog.

Create Synthetic API Test

Create a synthetic API test in Datadog.

Create Webhook

Create a webhook in Datadog.

Delete Dashboard

Delete a dashboard in Datadog.

Delete monitor

Deletes a Datadog monitor permanently.

Get Dashboard

Get a specific dashboard from Datadog.

Get monitor

Retrieves detailed information about a specific Datadog monitor, including its current state, configuration, and any active downtimes.

Get Service Dependencies

Get service dependency mapping from Datadog APM.

Get Synthetics Locations

Tool to retrieve all available public and private locations for Synthetic tests in Datadog.

Get host tags

Retrieves all tags associated with a specific host in Datadog.

Get usage summary

Retrieves usage summary information from Datadog including API calls, hosts, containers, and other billable usage metrics.

List All Tags

List all tags from Datadog.

List API Keys

List API keys in Datadog.

List APM Services

List APM services from Datadog.

List AWS Integration

List AWS integrations in Datadog.

List dashboards

Lists all Datadog dashboards with basic information.

List events

Lists events from Datadog within a specified time range.

List hosts

Lists all hosts in your Datadog infrastructure with detailed information including metrics, tags, and status.

List Incidents

List incidents from Datadog.

List Log Indexes

Tool to retrieve a list of all log indexes configured in Datadog, including their names and configurations.

List active metrics

Discover metric names by listing actively reporting metrics since a given timestamp.

List monitors

Get all monitor details.

List Roles

List roles from Datadog organization.

List service checks

Lists service checks from Datadog.

List SLOs

List Service Level Objectives (SLOs) from Datadog.

List Synthetics Tests

List Synthetics tests from Datadog.

List Users

List users from Datadog organization.

List Webhooks

List webhooks from Datadog.

Mute Monitor

Mute a monitor in Datadog.

Query metrics

Queries Datadog metrics and returns time series data.

Search logs

Searches Datadog logs with advanced filtering capabilities.

Search Spans Analytics

Search and analyze span data with aggregations in Datadog.

Search Traces

Search for traces in Datadog APM.

Submit metrics

Submits custom metrics to Datadog.

Unmute Monitor

Unmute a monitor in Datadog.

Update Dashboard

Update a dashboard in Datadog.

Update host tags

Updates tags for a specific host in Datadog.

Update monitor

Updates an existing Datadog monitor with new configuration, thresholds, or notification settings.

FRAMEWORKS

How to build Datadog MCP Agent with another framework

ChatGPT

Use Datadog MCP with ChatGPT

OpenAI Agents SDK

Use Datadog MCP with OpenAI Agents SDK

Claude Agent SDK

Use Datadog MCP with Claude Agent SDK

Claude Code

Use Datadog MCP with Claude Code

Claude Cowork

Use Datadog MCP with Claude Cowork

Codex

Use Datadog MCP with Codex

Cursor

Use Datadog MCP with Cursor

OpenClaw

Use Datadog MCP with OpenClaw

Hermes

Use Datadog MCP with Hermes

CLI

Use Datadog MCP with CLI

Google ADK

Use Datadog MCP with Google ADK

LangChain

Use Datadog MCP with LangChain

Vercel AI SDK

Use Datadog MCP with Vercel AI SDK

Mastra AI

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LlamaIndex

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CrewAI

Use Datadog MCP with CrewAI

OpenCode

Use Datadog MCP with OpenCode

VS Code

Use Datadog MCP with VS Code

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Ably

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FAQ

Frequently asked questions

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.

Yes, you can. Pydantic AI 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.

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.

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.

Start with Datadog.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Datadog tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Datadog MCP with Pydantic AI

Connect Datadog without auth hassles

Introduction

Also integrate Datadog with

TL;DR

What is Pydantic AI?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Create a Tool Router Session

Initialize the Pydantic AI Agent

Build the chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build Datadog MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

OpenCode

VS Code

Explore Other Toolkits

Supabase

Codeinterpreter

GitHub

Ably

Frequently asked questions

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

Can I use Tool Router MCP with Pydantic AI?+

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

How safe is my data with Composio Tool Router?+

Start with Datadog.It takes 30 seconds.

Teams ship real agents on Composio