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 for the inspection sequence, SemanticOps MCP tool usage, safety rules, and final output order.
• Read model-quality-scorecard when scoring the model, assigning severity, formatting findings, and building the remediation backlog.
• Read model-quality-rulebook 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.