Connect to a Power BI target and initialize an orchestrator session.

Use this tool when the user asks to:

• Connect to a Power BI Desktop session, Fabric workspace, PBIP folder, or PBIX file.

• Start an interactive session to inspect or modify Power BI assets.

Args: target_type: One of "pbi_desktop", "fabric_workspace", "pbip_folder", "pbix_file". target_ref: Reference path (for PBIP/PBIX) or workspace UUID (for Fabric). auth_mode: "interactive" (default, browser login) or "service_principal". tenant_id: Azure AD tenant ID (required when auth_mode is service_principal).

Returns: ConnectResult with session_id, engines_available status, and any diagnostic warnings.

Create a versionable, multi-step execution plan from an intent template.

Use this tool when the user asks to:

• Plan a complex or risky operation requiring atomic execution or rollback capability.

• Safely rename a table or column across both model and visual bindings (intent="safe_rename").

• Plan an audit, deployment, or DAX regression run.

Args: intent: One of the supported templates ("safe_rename", "audit", "deploy", "dax_regression"). options: Template-specific arguments (e.g. for safe_rename: old_path, new_path, scope). May also contain PlanOptions fields (auto_rollback, max_impact_threshold, dry_run_first).

Returns: PlanResult with plan_id, plan_yaml, steps, risk_score, estimated_changes.

Execute an approved plan with automatic rollback on step failure.

Use this tool when the user asks to:

• Execute or apply an approved plan created by plan_change.

• Run plan steps in dry-run mode before modifying actual files.

Args: plan_id: ID of the plan previously created by plan_change. dry_run: If True, simulate execution without modifying files or cloud resources. confirm_each_step: Reserved hook for interactive step confirmations.

Returns: ApplyResult with execution status, executed steps, and rollback details if needed.

Composite quality and compliance audit on a Power BI project (PBIP).

Use this tool when the user asks to:

• Audit, inspect, or validate a Power BI project (.pbip) or semantic model.

• Check Best Practice Analyzer (BPA) rules, DAX code quality, or naming conventions.

• Validate WCAG 2.1 accessibility (contrast, missing alt text, chart readability).

Args: pbip_path: Path to the root .pbip directory or folder. bpa_ruleset: Best practice ruleset to run ("default", "strict", "lenient"). dax_measures_json: Optional JSON string or dictionary mapping measure names to DAX expressions. bpa: Whether to execute Tabular BPA checks. dax_lint: Whether to execute static DAX linting checks. accessibility: Whether to audit WCAG 2.1 accessibility on report pages. naming: Whether to validate column, measure, and table naming conventions.

Returns: Dict with overall score (0-100), pass/fail status, and categorized findings.

Deploy a PBIP project to a Fabric/Power BI workspace with pre-deploy gates.

Use this tool when the user asks to:

• Deploy, publish, or release a Power BI project (.pbip) to Microsoft Fabric or Power BI Service.

• Run pre-deployment quality gates before publishing.

• Schedule automatic daily dataset refreshes upon publication.

Args: pbip_path: Local filesystem path to the root .pbip directory. workspace_id: Target Fabric / Power BI workspace ID (UUID). refresh_daily_hour: Daily UTC hour (0-23) for scheduled refresh (default: 6 AM UTC). findings_json: Optional list or JSON string of pre-existing audit findings to evaluate. gate_profile: Quality gate profile ("strict", "standard", "lenient"). auth_mode: "interactive" (default, browser login) or "service_principal". tenant_id: Azure AD tenant ID. client_id: Azure AD client ID. client_secret: Azure AD client secret. mock: If True, simulate deployment without calling external APIs.

Returns: Dict with deployment status, gate evaluation results, published item IDs, and refresh configuration.

Trigger, monitor, and optionally wait for a Power BI dataset refresh.

Use this tool when the user asks to:

• Refresh data in a published Power BI semantic model.

• Check the completion status of a refresh operation.

• Perform full, automatic, or data-only refreshes.

Args: workspace_id: Fabric / Power BI workspace ID (UUID). dataset_id: Dataset / semantic model ID (UUID). refresh_type: "full", "automatic", "data_only", "calculate", or "clearValues". wait: Whether to poll and wait for the refresh to complete before returning. timeout_s: Maximum wait time in seconds (default: 1800). auth_mode: "interactive" or "service_principal". tenant_id: Azure AD tenant ID. client_id: Azure AD client ID. client_secret: Azure AD client secret.

Returns: Dict with refresh status, duration, error details, and rollback status if applicable.

Run DAX queries against a baseline and assert regression tolerance.

Use this tool when the user asks to:

• Verify that measures or models return consistent results across changes.

• Compare live DAX calculation outputs against a golden baseline file.

• Check numerical tolerances on calculation outputs during CI/CD.

Args: baseline_path: Path to the JSON baseline file containing expected results. queries_json: Optional list or JSON string of DAX queries to execute. tolerance_pct: Maximum allowed percentage difference between actual and expected numeric values (default: 0.1%). query_executor: Optional custom query execution callable.

Returns: Dict containing diff summary, passed/failed queries, and variance details.

Execute a DAX query against a published Power BI semantic model / Fabric dataset.

Use this tool when the user asks to:

• Run, evaluate, or test a DAX query against a live dataset in Power BI or Fabric.

• Inspect actual business data, measure outputs, KPI calculations, or table rows.

• Verify Row-Level Security (RLS) filters by simulating a specific user principal name.

Args: workspace_id: Fabric / Power BI workspace ID (UUID). dataset_id: Published semantic model / dataset ID (UUID). dax_query: The DAX query expression (e.g. "EVALUATE TOPN(10, 'Sales')" or "EVALUATE ROW("Total", [Total Sales])"). impersonated_user_name: Optional User Principal Name (UPN) to test RLS rules as that user. auth_mode: "interactive" (default, browser login) or "service_principal". tenant_id: Azure AD tenant ID (required for service_principal). client_id: Azure AD client ID (for service_principal). client_secret: Azure AD client secret (for service_principal). fabric_client: Optional injected FabricClient instance (for testing).

Returns: Dict containing query execution results with tabular rows, columns, and execution metadata.

Compare two semantic models and report structural differences.

Use this tool when the user asks to:

• Compare two versions of a Power BI model or PBIP directory.

• See what tables, columns, measures, or relationships changed between branches or releases.

Args: before: Path to the baseline PBIP directory or snapshot. after: Path to the target PBIP directory or snapshot. inspector: Optional model inspector callable.

Returns: Dict detailing added, removed, and modified tables, columns, measures, and relationships.

Evaluate audit findings against a pre-deployment quality gate profile.

Use this tool when the user asks to:

• Check whether audit findings block deployment or meet quality criteria.

• Validate findings against 'strict', 'standard', or 'lenient' governance gates.

Args: findings_json: List or JSON string of audit findings to evaluate. profile: Gate profile to evaluate against ("strict", "standard", "lenient").

Returns: Dict with gate decision (passed=True/False), blocking findings, and warnings.

Generate Markdown documentation and Mermaid ER diagram for a semantic model.

Use this tool when the user asks to:

• Document a Power BI dataset or semantic model.

• Generate a data dictionary listing all tables, columns, types, and descriptions.

• Create a Mermaid entity-relationship (ER) diagram of the model.

Args: pbip_path: Path to the .pbip directory. output_path: Optional file path to save the generated Markdown. inspector: Optional model inspector.

Returns: Dict with data dictionary markdown content, table count, measure count, and output path.

Apply a colorblind-safe theme, backfill visual alt text, and re-audit WCAG.

Use this tool when the user asks to:

• Make a report accessible and WCAG 2.1 compliant.

• Apply a colorblind-safe palette (e.g. Okabe-Ito, ColorBrewer).

• Automatically generate informative alt text for visuals lacking descriptions.

Args: pbip_path: Path to the .pbip directory. palette: Colorblind-safe palette name ("okabe_ito", "colorbrewer"). auto_backfill_alt_text: Whether to generate missing alt text on visuals. alt_text_template: Format template for generated alt text.

Returns: Dict with updated WCAG score, modified visual count, and theme update details.

Add a DAX measure to a semantic model with automated linting and validation.

Use this tool when the user asks to:

• Create or add a new DAX measure to a Power BI model.

• Validate DAX syntax and best practices (preventing division by zero, unformatted measures, etc.).

• Dry-run a measure to check for lint issues before committing to TMDL.

Args: target: Target PBIP directory or TMDL path. measure_name: Name of the measure to create. table: Target table where the measure will reside. expression: DAX formula for the measure (e.g. "DIVIDE([Total Sales], [Units], 0)"). format_string: Format string (e.g. "$#,##0.00", "0.0%"). description: Measure documentation or business description. is_hidden: Whether the measure should be hidden in report view. fail_on_severity: Minimum lint severity that blocks creation ("error", "warning", "info"). dry_run: If True, validate lint rules without writing to disk. runtime_check: Whether to execute the measure against an active engine if connected. measure_writer: Optional custom measure writer callable.

Returns: Dict with success status, lint findings, and modified file paths.

Scaffold a PBIR report folder and layout from an existing semantic model.

Use this tool when the user asks to:

• Create a new report (.Report folder) for an existing dataset.

• Generate starter report pages with cards, charts, and an accessible theme.

Args: pbip_path: Path to the .pbip directory containing the dataset. page_name: Name of the initial report page (default: "Overview"). visual_count: Number of starter visuals to generate. theme: Theme name to apply (default: "okabe_ito"). include_card: Whether to generate a top-line KPI card visual. inspector: Optional model inspector.

Returns: Dict with created page path, visual IDs, and scaffolded report files.

Modify a visualContainer in a PBIR report page (type, fields, formatting, layout).

Use this tool when the user asks to:

• Edit, reformat, or resize a specific chart/visual on a report page.

• Change visual fields, bindings, alt text, or visibility.

Args: pbip_path: Path to the root .pbip directory. page_name: Name of the page containing the visual. visual_id: Unique ID of the visualContainer to edit. type: New visual type if changing (e.g. "barChart", "lineChart", "card"). fields_json: Optional dict or JSON string specifying field bindings to update. format_json: Optional dict or JSON string specifying formatting options. position_json: Optional dict or JSON string with x, y, width, height layout. alt_text: New alt text for accessibility. is_hidden: Whether to hide the visualContainer.

Returns: Dict with success status, changes applied, and page path.

Consolidate repetitive measures (e.g. YTD, QTD, PY) into calculation groups.

Use this tool when the user asks to:

• Refactor or clean up redundant DAX measures using calculation groups.

• Reduce model complexity and standardize time intelligence calculations.

Args: target: Target PBIP directory or TMDL path. min_candidates: Minimum measure patterns needed to trigger consolidation. reconcile_strategy: "strict" or "lenient". preserve_originals: Whether to keep original measures alongside the calculation group. auto_apply: If True, write calculation items immediately; if False, return proposed refactoring plan. inspector: Optional model inspector. measure_writer: Optional measure writer callable.

Returns: Dict with proposed or applied calculation items, candidate measures, and impact assessment.

Recommend optimal visual types and chart configurations for given KPIs.

Use this tool when the user asks to:

• Choose the best charts or visual types for a specific set of KPIs or metrics.

• Tailor visual recommendations to an audience ('executive', 'analytical', 'operational').

• Get primary and alternative chart suggestions with rationale based on data types.

Args: kpis_json: List of KPIs or JSON string (each with name, semantic_type, fields, etc.). audience: Target persona ("executive", "analytical", "operational"). max_results: Maximum number of alternative visual recommendations per KPI. inspector: Optional model inspector providing column cardinality and schema info.

Returns: Dict with recommended primary visual, alternatives, and rationale for each KPI.

Synthesize a complete PBIR report page layout from a natural language brief.

Use this tool when the user asks to:

• Design or generate a new Power BI report page based on business requirements.

• Automatically select, size, position, and format visuals matching an analytical goal.

• Apply professional color schemes and visual hierarchy to a page.

Args: pbip_path: Path to the target .pbip directory. brief: Natural language description of what the report page should convey. page_name: Display name for the newly created report page (default: "Overview"). audience: Target audience ("executive", "analytical", "operational"). palette: Color palette name (default: "okabe_ito"). inspector: Optional model inspector for schema context.

Returns: Dict containing synthesized visual containers, positions, and page metadata.

Analyze PBIR report pages for visual performance bottlenecks and anti-patterns.

Use this tool when the user asks to:

• Diagnose slow-loading report pages or improve visual performance.

• Identify excessive visual density, expensive custom visuals, or unoptimized filters.

Args: pbip_path: Path to the .pbip directory. target_load_ms: Desired maximum page load latency in milliseconds (default: 5000 ms).

Returns: Dict with performance score (0-100), estimated load time, and actionable recommendations.

Evaluate report storytelling, visual hierarchy, cognitive load, and UX design.

Use this tool when the user asks to:

• Audit report design quality, narrative flow, or visual hierarchy.

• Check if a report follows dashboard best practices for a specific audience.

Args: pbip_path: Path to the .pbip directory. page_name: Optional specific page name to audit; audits all pages if omitted. audience_assumed: Target audience ("executive", "analytical", "operational"). strictness: Scoring strictness ("lenient", "standard", "strict").

Returns: Dict with UX score (0-100), category breakdowns (hierarchy, density, narrative), and suggestions.

Capture screenshots or structural wireframes of Power BI report pages.

Use this tool when the user asks to:

• Visually inspect or capture report pages for reviews or regression diffs.

• Generate SVG wireframes or image snapshots of PBIR layouts.

Args: pbip_path: Path to the .pbip directory. pages: Optional list of specific page names to capture. format: Output format ("png", "svg", "pdf"). resolution: Target resolution ("desktop", "mobile", "tablet"). output_dir: Directory where captured images are written. wait_ms: Time in ms to wait for visual rendering.

Returns: Dict with output image paths, warnings, and rendering metadata.

Generate a TMDL semantic model and PBIP project from a declarative schema spec.

Use this tool when the user asks to:

• Create, scaffold, or generate a new Power BI semantic model from scratch.

• Define tables, columns, data types, relationships, and hierarchies declaratively.

Args: spec_yaml: YAML specification of tables, columns, types, and relationships. spec_json: JSON specification (either as a string or a structured object/dict). output_pbip_path: Target directory to write the generated .pbip project. dry_run: If True, validate specification without writing files to disk.

Returns: Dict with tables created, relationships created, hierarchies created, and validation status.

Configure Row-Level Security (RLS) roles and validation rules in a TMDL model.

Use this tool when the user asks to:

• Set up, add, or configure RLS roles and DAX table filter expressions.

• Test and validate security rules against sample queries.

Args: target: Target PBIP directory or TMDL path. spec_yaml: YAML specification of security roles, members, and DAX filters. spec_json: JSON specification (either as a string or a structured object/dict). dry_run: If True, validate role specification without writing to disk. rollback_on_test_failure: Whether to revert modifications if test queries fail.

Returns: Dict with roles created, test query outcomes, and rollback status if applicable.

Promote artifacts across Microsoft Fabric Deployment Pipeline stages.

Use this tool when the user asks to:

• Promote or move items between Fabric deployment stages (e.g. dev to test, test to prod).

• Run pre-promotion quality gates before moving items.

Args: pipeline_id: Fabric deployment pipeline ID (UUID). source_stage: Source stage ("dev", "test", "prod"). target_stage: Target stage ("test", "prod"). items: Optional list of specific item IDs to promote. Promotes all if omitted. dry_run: If True, validate stages and gate checks without triggering actual promotion.

Returns: Dict with promotion status, gate outcomes, and affected items.

Export and commit a Fabric workspace into a local Git repository.

Use this tool when the user asks to:

• Back up or version-control a Fabric workspace into Git.

• Snapshot reports and semantic models into local PBIP files with Git commits.

Args: workspace_id: Source Fabric workspace ID (UUID). output_repo_path: Local path to destination Git repository. branch: Git branch to commit into. commit_message: Commit message describing the snapshot. exclude_items: Optional list of item IDs to exclude. dry_run: If True, inspect items without creating git commits.

Returns: Dict with committed items, commit SHA, and repository status.

Deploy a local Git repository with PBIP projects to a Fabric workspace.

Use this tool when the user asks to:

• Synchronize or publish a local Git repository or branch to a Fabric workspace.

• Update workspace items based on version-controlled PBIP files.

Args: repo_path: Path to the local Git repository. workspace_id: Target Fabric workspace ID (UUID). branch_or_commit: Git ref to sync (default: "HEAD"). conflict_resolution: Conflict handling strategy ("manual", etc.). dry_run: If True, calculate changes without publishing.

Returns: Dict with synchronized items, skipped items, and deployment results.

Apply Microsoft Purview information protection sensitivity labels to items.

Use this tool when the user asks to:

• Classify or protect Power BI items (reports, semantic models, dashboards).

• Set Purview sensitivity labels (Confidential, General, Highly Confidential).

Args: items: List of dicts specifying item IDs and types (e.g. [{"id": "...", "type": "Report"}]). label_id: Microsoft Purview label GUID. label_name: Display name of the sensitivity label. admin_scopes: Optional list of administrative authorization scopes. redact_names: Whether to redact item names in returned logs for security. dry_run: If True, validate permissions without applying labels.

Returns: Dict with updated items, failed items, and compliance status.

Diagnose orchestrator health, detected modeling engines, and storage readiness.

Use this tool when the user asks to:

• Check if the Power BI MCP orchestrator is running properly.

• See which external tools/engines are installed (Tabular Editor, DAX optimizer, etc.).

• Get installation or setup instructions for missing components.

Args: include_engine_details: Whether to return full diagnostic info and remediation tips for each engine.

Returns: Dict with system status, engine availability matrix, active store counts, and remediation advice.