The Power BI Modeling MCP Server — Hands-On

How do you let an AI agent actually edit your Power BI semantic model — not just hand you DAX to paste? You connect a Copilot- or Claude-style agent to your live model through Microsoft's official Power BI Modeling MCP server and the XMLA endpoint, then drive bulk model work (descriptions, hides, sort-bys, mark-as-date, new measures, even SVG-via-DAX visuals) in a fraction of the time — with the changes landing as a reviewable git diff when the model is saved as a Power BI project.

What you'll learn

  • Connect an AI agent to your live Power BI semantic model via the Modeling MCP server and the XMLA endpoint

  • Add descriptions to every table, column, and measure in your model in one prompt

  • Run an optimization pass where the agent proposes a plan first, you approve it, and then it executes

  • Verify what the agent actually changed by reading the TMDL git diff in a Power BI project

  • Roll back any change you do not like, file by file, using source control as your undo button

  • Generate an SVG sparkline visual by asking the agent to write the DAX measure for it

Resources

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Get Your Power BI Reports AI-Ready with GitHub

A beginner-friendly setup walkthrough for getting a Power BI project into a GitHub repository so you can safely bring AI into your reporting workflow, with rollback and visible diffs as the safety net.

What you'll learn

  • Install Git and Visual Studio Code and configure Git with your name and email

  • Create a GitHub repository and clone it locally with VS Code

  • Save an existing Power BI report as a Power BI project (PBIP) inside the repo

  • Use a .gitignore to keep the local data cache out of the repository

  • Run a full lifecycle pass: branch, add a DAX measure, view the diff, commit, sync, and merge a pull request

Resources

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Power BI as Code: The Setup AI Needs for Your Reports

Save your Power BI report as a PBIP project and it lands on disk as a folder of text — JSON for the report layer, TMDL for the model — which is the format AI tools like Claude and Copilot can actually read, reason about, and edit. This video walks through the three preview features that make that possible, then tours what each piece of the project looks like on disk.

What you'll learn

  • Enable the three Power BI Desktop preview features that make PBIP usable for AI workflows (PBIP save option, TMDL semantic model, enhanced report metadata)

  • Save an existing .pbix as a PBIP project and see what changed on disk

  • Read the .Report folder — how PBIR represents your report as files and folders of JSON

  • Read the .SemanticModel folder — how TMDL captures your data model as plain text

  • Use TMDL triple-slash (///) comments as model descriptions that round-trip from AI edits back into Desktop

Resources

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