> 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/activate/database-clients.md).
# Database clients
Data Product semantic models are accessible from any DB client that supports the MySQL wire protocol. Use this option when a consumer prefers working in a local SQL tool rather than the browser.
For example, in the `Orders Analytics Platform`, DB clients can query semantic models such as `customer_profile`, `daily_sales`, and `product_profile`.
Before connecting a client, make sure the [prerequisites](/consume/v1/get-started/before-you-begin.md) are in place: tenant access, a deployed Data Product, and a DataOS API token.
## Open the DB Client page
The **Query in your DB Client** page routes you to the right setup steps for your client so you don't piece together connection details yourself. From the Data Product page, select **Activate → Database Client** to open it; each supported client has a **Get started** link to its setup and usage guide.
## DB Client options
You query semantic model names as tables and their dimensions and measures as columns; the semantic layer handles joins, aggregations, and policies. See [Query](/consume/v1/activate/database-clients/mysql/query.md) for the SQL form.
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# Agent Instructions
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## 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/activate/database-clients.md?ask=&goal=
```
`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.
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