Proxuma Power BI Skills

AI prompt files and MCP server for generating reports and dashboards from your Power BI data. Works with Claude Code, GitHub Copilot, Cursor, and any MCP-compatible AI tool.

You ask a business question. The AI queries your data model, anonymizes it, and generates a complete HTML report or dashboard builder with real numbers. Your data never reaches the AI in readable form.

What's included

Component	Description
MCP Server	Python server connecting AI tools to Power BI and Fabric APIs
Report Prompt	Generates standalone HTML reports with KPIs, tables, analysis, and findings
QBR Prompt	Generates Quarterly Business Review reports
Project Report Prompt	Generates project status reports
Data Anonymization	Two-pass anonymization: deterministic aliases + NLP safety net
Setup Wizard	Auto-discovers workspaces, datasets, and sensitive columns

Quick start

git clone https://github.com/Proxuma/powerbi-claude-skills.git
cd powerbi-claude-skills
pip install -r requirements.txt
python -m server.wizard

The wizard walks you through Microsoft sign-in, picks your workspace and dataset, detects sensitive columns, and writes the config. No GUIDs to hunt for.

Then add the MCP server to your AI tool:

Claude Code:

claude mcp add powerbi -- python -m server.server

VS Code (GitHub Copilot / Cursor): Add to .vscode/mcp.json:

{
  "servers": {
    "powerbi": {
      "command": "python",
      "args": ["-m", "server.server"],
      "cwd": "/path/to/powerbi-claude-skills"
    }
  }
}

Claude Desktop: Add to claude_desktop_config.json:

{
  "mcpServers": {
    "powerbi": {
      "command": "python",
      "args": ["-m", "server.server"],
      "cwd": "/path/to/powerbi-claude-skills"
    }
  }
}

Prompts

Import these as slash commands or paste them as system prompts.

File	Use
prompts/powerbireport.md	#powerbireport what is my monthly revenue trend?
prompts/powerbireportQBR.md	#powerbireportQBR Q1 2026
prompts/projectreport.md	#projectreport Project Alpha
prompts/powerbi.md	General Power BI data questions

Data anonymization

All data is automatically anonymized before it reaches the AI. The AI only sees aliases like Client_A, Resource_1, Contact_3.

How it works

1. On first query, the server loads unique values from your configured sensitive columns via DAX
2. Every response passes through two layers:
   • Pass 1 — Deterministic lookup: known entities get consistent aliases (fast, auditable)
   • Pass 2 — Presidio NLP: catches unexpected PII in free-text fields (optional safety net)
3. After report generation, restore real names locally

Restoring real names

Option A — Drag and drop: Open the generated report in a browser. Drag ~/.powerbi-mcp/sessions/latest/mapping.json onto the restore button at the top of the page.

Option B — CLI:

python -m server report.html -o report-real.html

Configuration

The wizard (option 4) auto-detects sensitive columns. Or edit ~/.powerbi-mcp/config.json manually:

{
  "anonymization": {
    "enabled": true,
    "sensitive_columns": {
      "client": ["'Company'[CompanyName]"],
      "resource": ["'Resource'[FullName]"],
      "contact": ["'Contact'[ContactName]"]
    },
    "presidio_enabled": true
  }
}

Audit trail

Every session stores its mapping at ~/.powerbi-mcp/sessions/<id>/mapping.json. This file never leaves your machine. Use it to verify what was anonymized and provide compliance documentation.

MCP tools

Once the server is running, your AI assistant has access to:

Tool	Description
list_workspaces	List all Power BI workspaces
list_datasets	List datasets in a workspace
execute_dax	Run a DAX query and get anonymized results
search_schema	Search for measures, columns, or tables
list_measures	List all measure names
list_fabric_items	List items in a Fabric workspace
get_schema	Full schema (caution: can be >10MB)
anonymization_status	Show anonymization state and entity counts

Requirements

• Python 3.10+
• Power BI Pro or Premium Per User license (for API access)
• An MCP-compatible AI tool (Claude Code, GitHub Copilot, Cursor, Claude Desktop)

No Azure app registration needed. The server uses the same public client flow as Power BI Desktop.

Authentication

1. The wizard opens a browser for Microsoft sign-in
2. You sign in with your Power BI account
3. Tokens are cached locally in ~/.powerbi-mcp/
4. Subsequent runs refresh automatically — no re-login needed

Tokens are stored only on your machine. The MCP server never sends credentials to any third party.

Project structure

powerbi-claude-skills/
├── server/
│   ├── server.py              # MCP server
│   ├── auth.py                # Azure AD authentication
│   ├── wizard.py              # Setup wizard
│   ├── entity_registry.py     # Deterministic entity anonymization
│   ├── anonymizer.py          # Two-pass anonymizer (registry + Presidio)
│   ├── mapping.py             # Session mapping persistence
│   ├── deanonymizer.py        # Restore real names (XSS-safe)
│   ├── __main__.py            # CLI deanonymize entry point
│   └── config.example.json    # Example configuration
├── prompts/
│   ├── powerbireport.md       # Report generator
│   ├── powerbireportQBR.md    # QBR report generator
│   ├── projectreport.md       # Project report generator
│   └── powerbi.md             # General Power BI queries
├── templates/
│   └── report-shell.html      # Report HTML template (with restore UI)
├── tests/                     # Test suite
├── requirements.txt
├── LICENSE
└── README.md

Running tests

pip install pytest
python -m pytest tests/ -v

Compatibility

AI Tool	Status
Claude Code (CLI)	Supported
GitHub Copilot (VS Code, Agent mode)	Supported
Claude Desktop	Supported
Cursor	Supported
ChatGPT (via MCP plugin)	Experimental

License

MIT