> 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/references/interfaces/ai-activation/request-flow.md).

# Request flow

AI activation starts in the host. The user asks a question, the model decides whether it needs external context, and the MCP client calls Data Product MCP when a Data Product tool is needed.

## Request path

1. The user asks a question in an AI host.
2. The host sends the prompt to the model with the available MCP tool descriptions.
3. The model decides whether a Data Product MCP tool is needed.
4. The host asks the MCP client to call the selected tool with structured arguments.
5. The MCP client sends the request to the Data Product MCP server.
6. Data Product MCP validates the request and calls the appropriate DataOS or Data Product APIs.
7. DataOS applies identity, tenant, policy, masking, semantic, quality, lineage, and contract checks.
8. Data Product MCP returns a structured response with data, metadata, warnings, citations, or errors.
9. The host presents the model's explanation to the user, grounded in the tool response.

## Example

A user may ask `Is orders360 fresh enough to use for today's revenue review?`

The model can call:

* `vulcan_runs` to inspect recent runs.
* `vulcan_quality` to inspect quality status.
* `vulcan_about` to identify the owner if follow-up is needed.

The final answer is grounded in those responses. It is not a freeform guess.


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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/references/interfaces/ai-activation/request-flow.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.

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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.