> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/consume/v1/evaluate/overview.md).

# Overview

Evaluating is the second step of the consumer journey. You found a product that looks relevant; now you check whether it is actually fit for what you need. This step answers what is in the product, who owns it, how it is built, whether quality checks pass, and whether anyone is keeping it alive.

Open a product from the Hub by clicking its card or row. The product page is your evaluation surface.

![Data Product overview page with description, stats, and navigation](/files/96hsLHLPYQMOuZBDCQKP)

## Read the header first

The header gives you a fast read before you open any section: the name and type, a plain-language description, metadata chips (source, domain, documentation), the version, and the owner. Quality dimensions (**Completeness**, **Validity**, **Uniqueness**) are flagged when they need attention. Use it to decide in seconds whether the product is worth a closer look. You can **Follow** a product to track changes, copy its identifier for queries and support requests, and hover the owner to reach their contact details. If the product requires a data-sharing agreement, following it shows an **Accept & Follow** confirmation with the agreement text before access is granted; products without one continue to follow immediately.

## Where to look

Four live signals sit in the stats bar: **data quality** (rules passed out of total), **freshness** (time since last refresh), **queries** (total run against the product), and **followers**. Below that, the sections each answer a specific question:

<table><thead><tr><th width="189.23724365234375">Section</th><th>What it tells you</th></tr></thead><tbody><tr><td>Overview</td><td>A single view of how data flows from inputs through models and semantic models to metrics and connection paths. Grasp scope and shape without opening every asset.</td></tr><tr><td>What it answers</td><td>The business outcomes the product was built for, as metric cards.</td></tr><tr><td>Trust and freshness</td><td>Quality, freshness, and AI-readiness, the three signals for whether you can rely on it. See <a href="/pages/OPsa8UqaHswLqtRy56bi">Trust and freshness</a>.</td></tr><tr><td>How to activate</td><td>The supported consumption paths: MCP, BI tools, APIs, database clients.</td></tr><tr><td>How it is being used</td><td>Adoption signals: active users, query performance, top users, perspectives.</td></tr><tr><td>Contacts</td><td>Who is responsible and how to reach them.</td></tr></tbody></table>

The Overview section also has an **Is it a right fit for me?** panel with *Good for*, *Not good for*, and *Caveats*, drawn from the product's own [`usage.yaml`](https://v2.dataos.info/build/productize/init-and-configuration).

Click a card in **What it answers** to open its side drawer. If a policy masks or filters the metric's data, the drawer shows a **Protected** banner; select it to expand the humanized row filters and see exactly what's restricted for you. Trend arrows on a card reflect the actual period-over-period change and use the standard positive color for a positive trend, so read them as-is rather than a rough estimate.

## Evaluate in depth

From here, dig into whichever signal matters for your decision:

* [Explore assets](/consume/v1/evaluate/assets.md): inputs, outputs, semantic models, metrics, and how data flows between them.
* [Query in Studio](/consume/v1/evaluate/query-in-studio.md): query the governed semantic models and validate the data yourself.
* [Trust and freshness](/consume/v1/evaluate/trust-and-freshness.md): quality checks, freshness, and AI-readiness.
* [Track activity](/consume/v1/evaluate/track-activity.md): run history, version history, and recent changes.
* [Understand in natural language](/consume/v1/consume-with-ai/understand-in-natural-language.md): have an AI client summarize health, lineage, ownership, and limitations. This is evaluation's AI mode.

## You are done when

You know what the product provides, you trust the signals it shows, and you can decide whether to [activate](/consume/v1/activate/overview.md) it.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://v2.dataos.info/consume/v1/evaluate/overview.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.