PDF Report Generator

FastMCP server that turns one plain-text prompt (with embedded CSV data) into: a live Power BI dashboard, chart images, executive summary, insights, recommendations, and a professional PDF report.

1. Install dependencies

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt

2. Configure .env

AZURE_OPENAI_API_KEY=your_key_here
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT=gpt-4o-mini
AZURE_OPENAI_API_VERSION=2024-08-01-preview

POWERBI_CLIENT_ID=your_client_id_here
POWERBI_CLIENT_SECRET=your_client_secret_here
POWERBI_TENANT_ID=your_tenant_id_here
POWERBI_WORKSPACE_ID=your_workspace_id_here

LOG_LEVEL=INFO
GENERATED_DIR=generated

Getting the Power BI values

• Client ID / Client Secret / Tenant ID: from the Azure AD app registration (Azure Portal -> App registrations). The app needs the Dataset.ReadWrite.All Power BI API permission, with admin consent granted.
• Workspace ID: open your workspace at app.powerbi.com, copy the GUID from the URL: app.powerbi.com/groups/<WORKSPACE_ID>/list
• The workspace's tenant must also have "Allow service principals to use Power BI APIs" enabled (Power BI Admin Portal -> Tenant settings), and the app should be added as a member of the target workspace.

3. Test without any MCP client

python test_local.py

This prints the executive summary, insights, recommendations, chart file paths, the live Power BI dashboard URL, and the PDF path.

4. Run as an MCP server

python server.py

5. Test with MCP Inspector (requires Node.js)

npx @modelcontextprotocol/inspector python server.py

Example client prompt

Generate a professional sales report from the following data.

OrderID,Region,Sales,Profit
101,North,12000,3000
102,South,9000,1800
103,East,15000,4500
104,West,7000,1200

Generate:
Bar Chart
Pie Chart
Power BI Dashboard
Professional PDF Report

Expected output (ReportResponse)

• powerbi_dashboard_url -> live link to the dataset/report in app.powerbi.com
• powerbi_dataset_id -> the push dataset's ID
• chart_paths -> PNGs in generated/charts/ (embedded in the PDF)
• pdf_path -> PDF in generated/reports/
• executive_summary, insights, recommendations -> LLM-generated text

How the Power BI integration works

services/powerbi_service.py uses MSAL's client-credentials flow to get an app-only access token (no human login), then:

1. Checks if a push dataset with this report's name already exists in the workspace; creates one if not, with a schema matching the DataFrame columns.
2. Pushes all rows into that dataset via the REST API, in batches.
3. Returns a URL to view it at app.powerbi.com.

If Power BI publishing fails (e.g. permission not yet active), the report still completes with charts/PDF/summary - the error is logged and powerbi_dashboard_url is left as null in the response.

Extending later

• OneDrive upload: add services/onedrive_service.py using Microsoft Graph API (Files.ReadWrite permission on the same Azure AD app) to upload the finished PDF and return a shareable link.
• Database/Excel input: swap PromptService._extract_dataframe for a DB/Excel loader - the rest of the pipeline doesn't change.

Project structure

pdf-report-powerbi-mcp/
    config/settings.py         # env-driven, typed settings
    models/                    # ReportRequest, ChartSpec, ReportResponse
    services/
        prompt_service.py      # raw text -> ReportRequest (CSV parsing)
        openai_service.py      # Azure OpenAI wrapper
        summary_service.py     # data profiling + summary/insights/recs
        chart_service.py       # matplotlib chart rendering (for the PDF)
        powerbi_service.py     # real Power BI push-dataset integration
        pdf_service.py         # ReportLab PDF assembly
    tools/generate_report.py   # orchestrates the full pipeline
    utils/                     # logger, helpers
    server.py                  # FastMCP entrypoint
    test_local.py              # test without an MCP client