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

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

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