Power BI + AI via MCP:
what actually works

You've probably tried Copilot in Power BI. Most people were underwhelmed. It's a chat window on top of a report - it can answer questions about your data, but it can't touch your model. It doesn't know your measures. It can't fix anything.

MCP is completely different. MCP (Model Context Protocol) gives an external AI - Claude, GitHub Copilot, Gemini - a direct connection inside your Power BI semantic model. Not reading a screenshot. Not guessing from a description. Actually seeing every measure, every table, every relationship, every line of Power Query. And making changes to it.

How it works in three steps

Two versions: local vs remote

What you need

Path A: VS Code + GitHub Copilot recommended - ~5 minutes

Easiest setup. One extension install, works immediately. Requires a GitHub Copilot subscription (individual plan from $10/month).

Path B: Claude Desktop ~15 minutes, manual config

{
  "mcpServers": {
    "powerbi-modeling-mcp": {
      "type": "stdio",
      "command": "C:\\MCPServers\\PowerBIModelingMCP\\extension\\server\\powerbi-modeling-mcp.exe",
      "args": ["--start"],
      "env": {}
    }
  }
}

What doesn't work

Once connected, open Claude (or Copilot), make sure Power BI Desktop is running with your model open, and try these in order. Copy and paste them as-is.

Watch it read your entire model structure back to you. If this works - you're connected and ready.

For a model with 80 measures, this takes about 2 minutes. Manually it's a full day.

The "list first, then confirm" part is important. Always ask the agent to show you what it plans to do before applying changes to a model you care about.

Each prompt is designed to get a specific, reliable result - with context that prevents the most common AI mistakes.

The third step forces the AI to generate a verification query alongside the code. This surfaces logical errors before you paste anything into your model.

Giving the AI context about your data structure (the Deleted flag) before asking it to document prevents it from writing descriptions that ignore your business rules.

Every time Claude generates or modifies a measure, verify it actually calculates correctly - not just runs without errors. AI-generated DAX can look right, pass syntax checks, and still return wrong numbers. Run these in DAX Studio or SSMS before merging any AI-assisted change into production.

EVALUATE ROW(
  "Test",     "TotalSales 2023",
  "Expected", 12345,
  "Actual",   CALCULATE([TotalSales], 'Date'[Year] = 2023)
)

If Actual does not equal Expected - the test fails. Replace the measure name, year, and expected value with your own.

EVALUATE UNION(
  ROW("Test", "Sales 2023", "Result",
    IF(CALCULATE([TotalSales], 'Date'[Year] = 2023) = 12345,
      "PASS", "FAIL")),
  ROW("Test", "Sales Q1", "Result",
    IF(CALCULATE([TotalSales], 'Date'[Quarter] = "Q1") = 3100,
      "PASS", "FAIL"))
)

Returns PASS or FAIL for each scenario. Add as many ROW() blocks as you need. One failed row = something is wrong with that measure.