/mcp-engine-model-quality

name: mcp-engine-model-quality
description: Use when the user asks for a model audit, model quality review, scorecard, bad-practices or best-practices assessment, or a review of star-schema fit, relationships, DAX maintainability, VertiPaq/storage risk, metadata hygiene, governance signals, or validation gaps in a Power BI semantic model. For diagnosing one slow query, use mcp-engine-dax-performance; for Copilot or natural-language readiness, use mcp-engine-ai-readiness; to execute the remediation backlog, use mcp-engine-refactoring.

PBI Model Quality

Use this skill to assess a connected Power BI semantic model and return a source-backed quality scorecard with prioritized recommendations. This is an assess-only workflow; do not apply model changes.

Start Here

1. Confirm the current model context with SemanticOps MCP tools when needed. 2. Gather metadata before querying data. 3. Use `list_model`, `manage_dependencies`, `run_query`, `manage_tests`, and `manage_model_connection` where available. 4. Use `run_query` only for small aggregated validation, performance analysis, VertiPaq/storage diagnostics, or access tests. 5. Do not dump raw rows or sensitive values. 6. Cite bundled Microsoft Learn and SQLBI source links for material findings.

Workflow

• Read [model-quality-assessment-workflow](references/model-quality-assessment-workflow.md) for the inspection sequence, SemanticOps MCP tool usage, safety rules, and final output order.
• Read [model-quality-scorecard](references/model-quality-scorecard.md) when scoring the model, assigning severity, formatting findings, and building the remediation backlog.
• Read [model-quality-rulebook](references/model-quality-rulebook.md) for source-backed bad/questionable practice checks and recommended remediation language.

Assessment Areas

• Model shape and star-schema fit.
• Relationships and filter propagation risk.
• DAX and semantic layer maintainability.
• Storage and performance risk, including high-cardinality and unnecessary imported data.
• Metadata, naming, descriptions, display folders, and field exposure.
• Governance signals, including roles, sensitive-field exposure, and perspective-vs-security separation.
• Validation and test coverage.

Guardrails

• Do not call write operations from authoring or governance tools during the assessment.
• Treat unavailable Pro diagnostics, browse-only mode, policy denials, or missing tool capabilities as scope limitations, not model defects.
• Keep source-backed guidance nuanced; do not turn "generally recommended" practices into absolute rules when the source allows exceptions.
• Mark inferred findings with lower confidence unless tool evidence confirms them.
• End with concrete remediation steps and validation suggestions, not broad advice.

Output Standard

Return a compact quality assessment unless the user asks for raw detail:

1. Executive score and quality band. 2. Top 3 risks. 3. Category scorecard. 4. Findings grouped by critical, high, medium, and low severity. 5. Prioritized remediation backlog. 6. Validation/test recommendations. 7. Source notes.