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