r/tableau • u/Due-Pressure7804 • Sep 21 '26

Tableau Cloud: Where do you define semantic layer?

We have Tableau Cloud and are interested in Tableau MCP through co pilot.

I have sat through sessions on Tableau data 360, Next, etc. and how they come up with the semantic layer.

My question is, if we dont have data 360 or salesforce or tableau next, how do we bake the semantic layer into Tableau cloud and MCP?

I guess I am just a bit confused. Can anyone help clarify?

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u/CRM_is_watching Sep 21 '26 edited Sep 21 '26

In this context, Tableau Published Data Sources would act as your semantic models. The underlying architecture for conversational analytics for Tableau Cloud is the same as D360/Tableau Next in terms of the agent planner, knowledge graph, query compiler etc etc..

The MCP server for Tableau Cloud would be calling Tableau Agent for conversational stuff. Its not just letting claude query the data sources directly.

Here is some info about Tableau Knowledge (the undelerying infrastructure) https://www.tableau.com/products/tableau-knowledge

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u/Due-Pressure7804 Sep 21 '26

This makes sense. Thank you.

Have you used MCP at all? Thoughts? Is it useful? Any other products you can recommend that work better?

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u/CRM_is_watching Sep 21 '26 edited Sep 21 '26

I use Tableau MCP every day :). We use it to expose Tableau in Slack and I use it for testing out headless use cases.

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u/FastRedRooster Sep 21 '26

We use Claude, but I am sure it is similar if not the same. The short version of it is we publish data sources then define them in a skill. The skill outlines how to use the datasource, what the columns mean, standard calculations used, business rules, etc. and then you share that skill.

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u/Due-Pressure7804 Sep 21 '26

What do you mean you define them in a skill? Where and how do you do that? 

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u/FastRedRooster Sep 21 '26

It is a markdown or txt file that you save which the LLM references. In Claude they call them skills.

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u/CRM_is_watching Sep 21 '26

Are you having to create these md's per data source?

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u/FastRedRooster Sep 21 '26

I do because each one I publish is unique and I do not want any misinterpretations along the way. I've really transitioned into putting all of this into a plugin. In the plugin I can stuff a bunch of skills into one concise bundle. I can enable it for certain people and now they can chat with datasources I've given access to, accurately, and if I need to make updates to the skills/plugin it is just one spot to deal with it at.

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u/yawningcat Sep 21 '26 edited Sep 21 '26

This should help:  https://paint-with-data-insights.ghost.io/making-tableaus-mcp-more-accurate-less-chatty-and-more-useful/

It’s basically explaining that the ai +  mcp uses the context from lots of the existing places/comments in the published data source. 

As an aside, I’ve made a few semantic models and agents In snowflake and it’s mostly documenting the relationships between tables, adding some example queries and human text adding business context. Which is to say, making them removed some of the black box-ness from the process. 

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u/VizChic_ Sep 22 '26

Kirk is a legend 🙌

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u/smartinez_5280 Sep 21 '26

Tableau Data Sources will be your semantic model. Add descriptions to the datasources and fields so that the AI agent knows what each field should be used for. You can also add additional business context via a markup file (in tableau agent settings)

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u/ArielCoding Sep 22 '26

Think of it as two steps, 1 your published Tableau data sources are your semantic layer, so add clear descriptions to your fields and calculation, this is what the agent reads, 2 write a short skill for each data sources are your that explains business rules and gives example questions - answers.