pbi-cli-tool 0.1.0

CLI for Power BI semantic models - wraps the Power BI MCP server for token-efficient AI agent usage

pbi-cli

Token-efficient CLI for Power BI semantic models.

pbi-cli wraps Microsoft's Power BI MCP server so you can manage semantic models from the terminal. MCP tool schemas consume ~4,000+ tokens in an AI agent's context window; a pbi command uses ~30. One install, no separate MCP server configuration required.

pip install pbi-cli-tool
pbi setup
pbi connect --data-source localhost:54321
pbi measure list

Why pbi-cli?

Approach	Context cost	Setup
Raw MCP server	~4,000 tokens per tool schema	Manual config per project
pbi-cli	~30 tokens per command	pip install pbi-cli-tool

Designed for Claude Code and other AI agents, but works great for humans too. Use --json for machine-readable output or enjoy Rich-formatted tables by default.

Installation

pip install pbi-cli-tool

Prerequisites

• Python 3.10+
• Power BI Desktop (for local development) or a Fabric workspace

First-time setup

Download the Power BI MCP binary:

pbi setup

This downloads the official Microsoft binary from the VS Code Marketplace to ~/.pbi-cli/bin/. You can also point to an existing binary:

export PBI_MCP_BINARY=/path/to/powerbi-modeling-mcp

Quick Start

Connect to Power BI Desktop

# Connect to a local Power BI Desktop instance
pbi connect --data-source localhost:54321

# Connect to a Fabric workspace model
pbi connect-fabric --workspace "My Workspace" --model "Sales Model"

Run DAX queries

pbi dax execute "EVALUATE TOPN(10, Sales)"
pbi dax execute --file query.dax
cat query.dax | pbi dax execute -

Manage measures

pbi measure list
pbi measure create "Total Revenue" --expression "SUM(Sales[Revenue])" --table Sales
pbi measure get "Total Revenue" --table Sales

Export and import models

pbi database export-tmdl ./my-model/
pbi database import-tmdl ./my-model/

Command Reference

Group	Description	Examples
setup	Download and manage the MCP binary	pbi setup, pbi setup --check
connect	Connect to Power BI via data source	pbi connect -d localhost:54321
connect-fabric	Connect to Fabric workspace	pbi connect-fabric -w "WS" -m "Model"
disconnect	Disconnect from active connection	pbi disconnect
connections	Manage saved connections	pbi connections list
dax	Execute and validate DAX queries	pbi dax execute "EVALUATE Sales"
measure	CRUD for measures	pbi measure list, pbi measure create
table	CRUD for tables	pbi table list, pbi table get Sales
column	CRUD for columns	pbi column list --table Sales
relationship	Manage relationships	pbi relationship list
model	Model metadata and refresh	pbi model get, pbi model refresh
database	Import/export TMDL and TMSL	pbi database export-tmdl ./out/
security-role	Row-level security roles	pbi security-role list
calc-group	Calculation groups and items	pbi calc-group list
partition	Table partitions	pbi partition list --table Sales
perspective	Model perspectives	pbi perspective list
hierarchy	User hierarchies	pbi hierarchy list --table Date
expression	Named expressions	pbi expression list
calendar	Calendar table management	pbi calendar list
trace	Diagnostic traces	pbi trace start
transaction	Explicit transactions	pbi transaction begin
advanced	Cultures, translations, functions	pbi advanced culture list
repl	Interactive REPL session	pbi repl

Run pbi <command> --help for full option details.

REPL Mode

The interactive REPL keeps the MCP server process alive across commands, avoiding the 2-3 second startup cost on each invocation:

$ pbi repl
pbi-cli interactive mode. Type 'exit' or Ctrl+D to quit.
pbi> connect --data-source localhost:54321
Connected: localhost-54321 (localhost:54321)
pbi(localhost-54321)> measure list
...
pbi(localhost-54321)> dax execute "EVALUATE Sales"
...
pbi(localhost-54321)> exit
Goodbye.

Features:

• Persistent MCP server connection (no restart between commands)
• Command history (stored at ~/.pbi-cli/repl_history)
• Tab completion for commands and subcommands
• Dynamic prompt showing active connection name

For AI Agents

Use --json before the subcommand for machine-readable JSON output:

pbi --json measure list
pbi --json dax execute "EVALUATE Sales"
pbi --json model get

JSON output goes to stdout. Status messages go to stderr. This makes piping and parsing straightforward.

Named connections

Use -c to target a specific named connection:

pbi -c my-conn measure list
pbi -c prod-model dax execute "EVALUATE Sales"

Configuration

pbi-cli stores its configuration in ~/.pbi-cli/:

~/.pbi-cli/
  config.json          # Binary version, path, args
  connections.json     # Named connections
  repl_history         # REPL command history
  bin/
    {version}/
      powerbi-modeling-mcp[.exe]

Binary resolution order

1. PBI_MCP_BINARY environment variable (explicit override)
2. ~/.pbi-cli/bin/{version}/ (managed by pbi setup)
3. VS Code extension fallback (~/.vscode/extensions/analysis-services.powerbi-modeling-mcp-*/server/)

Development

git clone https://github.com/pbi-cli/pbi-cli.git
cd pbi-cli
pip install -e ".[dev]"

# Lint
ruff check src/ tests/

# Type check
mypy src/

# Test
pytest -m "not e2e"

Contributing

Contributions are welcome! Please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-change)
3. Make your changes with tests
4. Run ruff check and mypy before submitting
5. Open a pull request

License

MIT