Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relation

Power BI Semantic Modeling Guide users in building optimized, well documented Power BI semantic models following Microsoft best practices. When to Use This Skill Use this skill when users ask about: Creating or optimizing Power BI semantic models Designing star schemas (dimension/fact tables) Writing DAX measures or calculated columns Configuring table relationships (cardinality, cross filter) Implementing row level security (RLS) Naming conventions for tables, columns, measures Adding descriptions and documentation to models Performance tuning and optimization Calculation groups and field parameters Model validation and best practice checks Trigger phrases: "create a measure", "add relationship", "star schema", "optimize model", "DAX formula", "RLS", "naming convention", "model documentation", "cardinality", "cross filter" Prerequisites Required Tools Power BI Modeling MCP Server : Required for connecting to and modifying semantic models Enables: connection operations, table operations, measure operations, relationship operations, etc. Must be configured and running to interact with models Optional Dependencies Microsoft Learn MCP Server : Recommended for researching latest best practices Enables: microsoft docs search, microsoft docs fetch Use for complex scenarios, new features, and official documentation Workflow 1. Connect and Analyze First Before providing any modeling guidance, always examine the current model state: 2. Evaluate Model Health After connecting, assess the model against best practices: Star Schema : Are tables properly classified as dimension or fact? Relationships : Correct cardinality? Minimal bidirectional filters? Naming : Human readable, consistent naming conventions? Documentation : Do tables, columns, measures have descriptions? Measures : Explicit measures for key calculations? Hidden Fields : Are technical columns hidden from report view? 3. Provide Targeted Guidance Based on analysis, guide improvements using references: Star schema design: See [STAR SCHEMA.md](references/STAR SCHEMA.md) Relationship configuration: See [RELATIONSHIPS.md](references/RELATIONSHIPS.md) DAX measures and naming: See [MEASURES DAX.md](references/MEASURES DAX.md) Performance optimization: See [PERFORMANCE.md](references/PERFORMANCE.md) Row level security: See [RLS.md](references/RLS.md) Quick Reference: Model Quality Checklist Area Best Practice Tables Clear dimension vs fact classification Naming Human readable: Customer Name not CUST NM Descriptions All tables, columns, measures documented Measures Explicit DAX measures for business metrics Relationships One to many from dimension to fact Cross filter Single direction unless specifically needed Hidden fields Hide technical keys, IDs from report view Date table Dedicated marked date table MCP Tools Reference Use these Power BI Modeling MCP operations: Operation Category Key Operations connection operations Connect, ListConnections, ListLocalInstances, ConnectFabric model operations Get, GetStats, ExportTMDL table operations List, Get, Create, Update, GetSchema column operations List, Get, Create, Update (descriptions, hidden, format) measure operations List, Get, Create, Update, Move relationship operations List, Get, Create, Update, Activate, Deactivate dax query operations Execute, Validate calculation group operations List, Create, Update security role operations List, Create, Update, GetEffectivePermissions Common Tasks Add Measure with Description Update Column Description Create Relationship When to Use Microsoft Learn MCP Research current best practices using microsoft docs search for: Latest DAX function documentation New Power BI features and capabilities Complex modeling scenarios (SCD Type 2, many to many) Performance optimization techniques Security implementation patterns

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