---
title: Agentic Onboarding for Product Analytics
description: >-
  Instrument your frontend application with one prompt using LLM coding agents
  like Cursor or Claude.
breadcrumbs: Docs > Product Analytics > Agentic Onboarding for Product Analytics
---

# Agentic Onboarding for Product Analytics

{% callout %}
# Important note for users on the following Datadog sites: app.ddog-gov.com

{% alert level="danger" %}
This product is not supported for your selected [Datadog site](https://docs.datadoghq.com/getting_started/site.md). ().
{% /alert %}

{% /callout %}

{% callout %}
##### Join the Preview!

Agentic Onboarding is in Preview.
{% /callout %}

## Overview{% #overview %}

Agentic Onboarding lets LLM coding agents instrument your frontend applications for [Error Tracking](https://docs.datadoghq.com/error_tracking/frontend.md), [Real User Monitoring (RUM)](https://docs.datadoghq.com/real_user_monitoring.md), [Product Analytics](https://docs.datadoghq.com/product_analytics.md), [Infrastructure Monitoring](https://docs.datadoghq.com/containers/kubernetes.md), and [Serverless Monitoring](https://docs.datadoghq.com/serverless.md) with a single prompt.

Your coding assistant, such as [Cursor](https://cursor.com/) or [Claude Code](https://claude.ai/), detects your project's frameworks, adds configuration, and provisions required tokens and apps directly from your IDE.

## Supported frameworks{% #supported-frameworks %}

Agentic Onboarding is available for the following frameworks:

- **Error Tracking, RUM, and Product Analytics**: Android, Angular, iOS, Next.js, React, Svelte, Vanilla JS, and Vue.
- **Infrastructure Monitoring with Kubernetes**: Terraform, Ansible, Kustomize, and more.
- **Serverless Monitoring for AWS Lambda**: Terraform, AWS CDK, Serverless Framework, and more.

## Setup{% #setup %}

### Install the Datadog Onboarding MCP server{% #install-the-datadog-onboarding-mcp-server %}

To install the Datadog Onboarding Model Context Protocol (MCP) server, follow the steps for your coding assistant:

{% tab title="Claude Code" %}

{% callout %}
# Important note for users on the following Datadog sites: app.ddog-gov.com



{% alert level="danger" %}
Agentic Onboarding is not available in the selected site ().
{% /alert %}


{% /callout %}

{% callout %}
# Important note for users on the following Datadog sites: app.datadoghq.com, us3.datadoghq.com, us5.datadoghq.com, app.datadoghq.eu, ap1.datadoghq.com, ap2.datadoghq.com



1. Open an active Claude Code session with the /mcp command:

   ```
   claude mcp add --transport http datadog-onboarding- "https://mcp./api/unstable/mcp-server/mcp?toolsets=onboarding"
```

1. Select the MCP server installed in Step 1. You should see a `disconnected - Enter to login` message. Press `Enter`.

1. When you see the option to authenticate, press `Enter`. This brings you to the OAuth screen.

1. After authentication, choose Open to continue and grant access to your Datadog account.

1. Confirm that MCP tools appear under the **datadog-onboarding-** server.


{% /callout %}

{% /tab %}

{% tab title="Cursor" %}

{% callout %}
# Important note for users on the following Datadog sites: app.ddog-gov.com



{% alert level="danger" %}
Agentic Onboarding is not available in the selected site ().
{% /alert %}


{% /callout %}

{% callout %}
# Important note for users on the following Datadog sites: app.datadoghq.com, us3.datadoghq.com, us5.datadoghq.com, app.datadoghq.eu, ap1.datadoghq.com, ap2.datadoghq.com



1. Copy and paste the following deeplink into your browser:

   ```
   
```

1. In Cursor, click Install for the **datadog-onboarding-** server.

1. If the MCP server shows a Needs login or Connect link, select it and complete the OAuth flow. When prompted, choose Open to continue and grant access to your Datadog account.

1. After authentication, return to Cursor and confirm that MCP tools appear under the **datadog-onboarding-** server.


{% /callout %}

{% /tab %}

{% tab title="Datadog AI Setup CLI" %}

{% callout %}
# Important note for users on the following Datadog sites: app.ddog-gov.com



{% alert level="danger" %}
Agentic Onboarding is not available in the selected site ().
{% /alert %}


{% /callout %}

{% callout %}
# Important note for users on the following Datadog sites: app.datadoghq.com, us3.datadoghq.com, us5.datadoghq.com, app.datadoghq.eu, ap1.datadoghq.com, ap2.datadoghq.com



The Datadog AI Setup CLI configures your project without a coding assistant.

1. Run the `npx` command, replacing `<PRODUCT>` with the identifier for the product you want to set up:

| Product                   | Identifier          |
| ------------------------- | ------------------- |
| Error Tracking            | `error-tracking`    |
| Infrastructure Monitoring | `infra-monitoring`  |
| Product Analytics         | `product-analytics` |
| Real User Monitoring      | `rum`               |
| Studio                    | `studio`            |

   ```shell
   npx @datadog/ai-setup-cli --product <PRODUCT>
   ```

1. A browser window opens for authentication. Complete the OAuth flow and grant access to your Datadog account.

1. Return to your terminal. The CLI detects your project's frameworks, applies the required configuration, and provisions any necessary tokens.

After the CLI completes, skip to Deploy your app to production.


{% /callout %}

{% /tab %}

### Set up your project{% #set-up-your-project %}

Your AI coding agent can help configure Datadog for your project. When you provide a setup prompt, the agent:

- Analyzes your project and identifies the framework, language, and bundler
- Calls the MCP tool and requests permission before running
- Applies the configuration changes specified by the tool
- Provides steps to verify that your application is sending telemetry to Datadog

**Note**: Your coding agent makes changes locally but does not commit them.

To get started:

1. Choose the product you want to use and paste its setup prompt into your AI agent:

   {% tab title="Error Tracking" %}

   ```text
   Add Datadog Error Tracking to my project
```

   {% /tab %}

   {% tab title="Real User Monitoring" %}

   ```text
   Add Datadog Real User Monitoring to my project
```

   {% /tab %}

   {% tab title="Product Analytics" %}

   ```text
   Add Datadog Product Analytics to my project
```

   {% /tab %}

   {% tab title="Infrastructure Monitoring" %}

   ```text
   Add Datadog for Kubernetes to my project
```

   {% /tab %}

   {% tab title="Serverless Monitoring" %}

   ```text
   Instrument my AWS Lambda functions with Datadog
```

   {% /tab %}

1. Review and accept each action your AI agent proposes to complete the setup process.

### Deploy your app to production{% #deploy-your-app-to-production %}

Commit the changes to your repository and configure the provided environment variables in your production environment.