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:

1. List connections: connection_operations(operation: "ListConnections")
2. If no connection, check for local instances: connection_operations(operation: "ListLocalInstances")
3. Connect to the model (Desktop or Fabric)
4. Get model overview: model_operations(operation: "Get")
5. List tables: table_operations(operation: "List")
6. List relationships: relationship_operations(operation: "List")
7. List measures: measure_operations(operation: "List")

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
• Relationship configuration: See RELATIONSHIPS.md
• DAX measures and naming: See MEASURES-DAX.md
• Performance optimization: See PERFORMANCE.md
• Row-level security: See RLS.md

Quick Reference: Model Quality Checklist

MCP Tools Reference

Use these Power BI Modeling MCP operations:

Common Tasks

Add Measure with Description

measure_operations(
  operation: "Create",
  definitions: [{
    name: "Total Sales",
    tableName: "Sales",
    expression: "SUM(Sales[Amount])",
    formatString: "$#,##0",
    description: "Sum of all sales amounts"
  }]
)

Update Column Description

column_operations(
  operation: "Update",
  definitions: [{
    tableName: "Customer",
    name: "CustomerKey",
    description: "Unique identifier for customer dimension",
    isHidden: true
  }]
)

Create Relationship

relationship_operations(
  operation: "Create",
  definitions: [{
    fromTable: "Sales",
    fromColumn: "CustomerKey",
    toTable: "Customer",
    toColumn: "CustomerKey",
    crossFilteringBehavior: "OneDirection"
  }]
)

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