Dashboard examples

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Public preview
The Analytics API is in public preview: on for every account, and features may change. Monitor run metrics are in private preview: reach out to your account representative to request access. See Integration and feature lifecycles for what this means.

Overview

To use one, copy its prompt into an AI tool connected to the Monte Carlo MCP server. Your agent builds the dashboard. With the dashboard skills from the Monte Carlo agent toolkit installed (in coding agents such as Claude Code, Cursor, Codex and GitHub Copilot), the agent reads the example's page and follows its layout and queries; in Claude (desktop and web) and ChatGPT the skills are not available yet, so it works from the metrics alone.

Each page shows the prompt and what you get first; "How it's built" lists the exact queries, for agents and for anyone building by hand. Every example is laid out as Dashboard style says.

Dashboard	For	What it answers
Asset-level data quality	Data team leads, data product owners	Which tables are failing data quality checks, and on which data quality dimensions? Across the account, or for any domain, data product, asset tag or warehouse.
Validation monitor results	Data engineers, data stewards	Did my validation checks break, run by run, and against what threshold?
Alert operations	Data team leads	How many alerts did we get, and how fast did we respond and fix them?
Monitoring coverage	Data platform owners	How many of our key tables are monitored, and where are the gaps?
Table health	Data engineers on call	Which tables alerted this week, and are their alerts still open?
Credit consumption	Data platform owners, budget owners	How many credits did we use each month, and what used them?
Data product scorecard	Data product owners	Which data product needs attention: health, coverage, alerts, time to fixed and credits side by side?