Power BI Desktop MCP v2: Automate Semantic Models with AI

In this video I put my Power BI Desktop MCP server v2 to a real test: can an AI agent build a complete semantic model from scratch?

Using Claude Code with local MCP support, I let an LLM drive an 8-phase workflow:

- connect to SQL Server

- import the Contoso tables

- optimize the model

- create measures

- define UDFs

- add age buckets

- apply translations

- document everything it has done

This is an experiment, not a polished marketing demo. I show where it works well, where it fails, and how easy it is for a powerful MCP to push a model into a corrupted state if you are not careful.

What you will see:

- Overview of my MCP server for Power BI Desktop (v2)

- How I prompt the LLM to work in clearly defined phases

- Running the workflow in Claude Code planning mode with agents

- Automatic table import and star schema creation on the Contoso 10k database

- Measures, UDFs, age buckets, and a dedicated measures table

- Date table configuration, relationships, and basic model hygiene

- Spanish translations created via MCP tools

- Validating what the LLM claims it did against the actual Power BI model

- Token / context usage for a full semantic-model build

Tools used:

- Power BI Desktop with my MCP server (Model Context Protocol) v2

- SQL Server Contoso sample database (10k row version)

- Claude Code with local MCP support

- DAX and semantic modeling best-practice documents as extra context

⚠️ Important:

This MCP is powerful enough to put your model into a corrupted state if misused. Always:

- Work on a copy of your PBIX or PBIP

- Keep backups / version control

- Manually verify relationships, measures, and transformations

- Treat the LLM output as a starting point, not ground truth