powerbi-mcp

A small Model Context Protocol (MCP) server for Microsoft Power BI. It lets an MCP-capable client (Claude, VS Code, etc.) discover your workspaces and datasets, read a semantic model's structure, and run DAX queries — all through the public Power BI and Microsoft Fabric REST APIs.

Authentication uses MSAL with the Windows broker (WAM): you sign in once, the token is cached locally, and the server refreshes it silently afterwards. No tokens are ever pasted by hand or stored in config.

Tools

Related MCP server: Microsoft Fabric MCP Server

Prerequisites

• Python 3.10+

• A Power BI account with access to at least one workspace

• Windows recommended — the interactive sign-in uses the OS broker (WAM). On macOS/Linux it falls back to the system browser.

Setup (step by step)

1. Install

git clone https://github.com/rajivdatta/powerbi-mcp.git
cd powerbi-mcp
python -m venv .venv
.venv\Scripts\activate          # Windows  (source .venv/bin/activate on macOS/Linux)
pip install -r requirements.txt

2. (Optional) Configure your tenant

All configuration is optional and via environment variables — copy .env.example to .env if you want to set any:

Finding your tenant ID: Azure Portal → Microsoft Entra ID → Overview → Tenant ID, or just use your email domain. Leaving it unset (organizations) lets any work/school account sign in.

.env is git-ignored.

3. Sign in once

.venv\Scripts\activate
python server.py --login

This opens an interactive sign-in (Windows broker popup or browser) and caches a refresh token at %LOCALAPPDATA%\powerbi-mcp\token_cache.bin. After this, the server refreshes access tokens silently on every call — no re-login needed.

To switch accounts later, just run --login again and pick the other account.

4. Test it standalone

python -c "import server; print(server.list_workspaces())"

You should get a JSON list of your workspaces (each with an id and name).

5. Register with your MCP host

Point your client at server.py (see examples/mcp.json). Use the venv's Python so dependencies resolve:

{
  "mcpServers": {
    "powerbi": {
      "command": "C:\\path\\to\\powerbi-mcp\\.venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\powerbi-mcp\\server.py"],
      "env": { "POWERBI_TENANT_ID": "your-tenant-id-or-domain" }
    }
  }
}

Restart your MCP client, then try: "using powerbi, list my workspaces".

Use with Claude Desktop

Claude Desktop reads its MCP servers from claude_desktop_config.json. Open it from Settings → Developer → Edit Config (this creates the file if it doesn't exist), or edit it directly:

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Add this server under mcpServers, using absolute paths to the venv's Python and server.py:

{
  "mcpServers": {
    "powerbi": {
      "command": "C:\\path\\to\\powerbi-mcp\\.venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\powerbi-mcp\\server.py"],
      "env": { "POWERBI_TENANT_ID": "your-tenant-id-or-domain" }
    }
  }
}

On macOS the paths are POSIX, e.g. "command": "/Users/you/powerbi-mcp/.venv/bin/python". Save the file and fully quit and reopen Claude Desktop (use Quit from the tray/menu-bar icon — closing the window isn't enough). The server's tools then appear in the tools (🔌) menu of a new chat. Sign in first with python server.py --login so the token cache exists before Desktop launches it.

Usage flow

A typical sequence:

1. list_workspaces → pick a workspace id

2. list_datasets(workspace_id) → pick a dataset id

3. get_model_definition(workspace_id, dataset_id) → learn table & measure names

4. run_dax(workspace_id, dataset_id, "EVALUATE ...") → get results

Security

The MSAL token cache (%LOCALAPPDATA%\powerbi-mcp\token_cache.bin) contains a refresh token. Treat it like a password — it lives outside the repo and is never committed.

License

MIT (c) 2026 Rajiv Datta