Microsoft Fabric Development Expert

Microsoft Fabric Development Expert

Expert guidance for Microsoft Fabric using the Fabric MCP Server. Access comprehensive API specifications, item definitions, best practices, and OneLake management capabilities - all running locally without connecting to live environments.

Core Capabilities

1. API Discovery - Enumerate and access Fabric workload APIs
2. Schema Access - Get JSON schemas for item definitions
3. Best Practices - Access guidance and examples
4. OneLake Management - File and item operations
5. Local-First - All tools run locally for reference and development

Quick Reference - MCP Tools

API Access Tools

OneLake Tools

API Discovery Tools

publicapis_list

List all Microsoft Fabric workload types that have public API specifications.

When to use:

• Starting Fabric development
• Exploring available workloads
• Finding workload-specific APIs

Workload Types Include:

• Lakehouses
• Data Pipelines
• Semantic Models (Power BI)
• Notebooks
• Spark Job Definitions
• Warehouses
• KQL Databases
• Eventhouse
• Real-Time Intelligence
• ML Models
• ML Experiments

publicapis_get

Retrieve complete OpenAPI/​Swagger specification for a specific workload.

Parameters:

Workload Examples:

• DataPipeline
• Lakehouse
• SemanticModel
• Notebook
• SparkJobDefinition
• Warehouse
• KQLDatabase

publicapis_platform_get

Access OpenAPI specifications for Microsoft Fabric platform-level APIs.

Platform APIs Include:

• Workspace management
• Item management (generic)
• Permission management
• Capacity operations
• Deployment pipelines
• Git integration

publicapis_bestpractices_get

Get embedded best practice documentation for Fabric development.

Parameters:

Topics Include:

• Pagination patterns
• Error handling
• Retry/​backoff strategies
• Authentication
• Rate limiting
• API versioning
• Request/​response patterns

publicapis_bestpractices_examples_get

Retrieve example API request/​response files for workloads.

Parameters:

Example Types:

• create - Creation requests
• update - Update operations
• get - Retrieval operations
• list - Listing operations
• delete - Deletion operations

publicapis_bestpractices_itemdefinition_get

Access JSON schema definitions for items within workload APIs.

Parameters:

Common Item Types:

• Lakehouse: lakehouse
• Pipeline: datapipeline, activity
• Semantic Model: semanticmodel, dataset
• Notebook: notebook
• Warehouse: warehouse
• KQL Database: kqldatabase

OneLake Management Tools

onelake download file

Download files from OneLake to local disk.

Parameters:

Item Format: Can be:

• GUID: 550e8400-e29b-41d4-a716-446655440000
• Name.Type: MyLakehouse.Lakehouse

onelake upload file

Upload local files to OneLake storage.

Parameters:

onelake file list

List files in OneLake via hierarchical endpoint.

Parameters:

onelake item create

Create new Fabric items (Lakehouses, notebooks, pipelines, etc.).

Parameters:

Item Types:

• Lakehouse
• DataPipeline
• Notebook
• Warehouse
• KQLDatabase
• SemanticModel
• SparkJobDefinition

Development Workflows

Workflow 1: Discover and Use Fabric APIs

1. publicapis_list
   - See all available workloads

2. publicapis_get
   workload: "DataPipeline"
   - Get complete API specification

3. publicapis_bestpractices_itemdefinition_get
   workload: "DataPipeline"
   item_type: "datapipeline"
   - Get schema for pipeline definition

4. publicapis_bestpractices_examples_get
   workload: "DataPipeline"
   example_type: "create"
   - See example API calls

5. Implement based on specs and examples

Workflow 2: Create and Configure Lakehouse

1. onelake item create
   workspace: "MyWorkspace"
   item_type: "Lakehouse"
   display_name: "DataLakehouse"

2. onelake directory create
   item: "DataLakehouse.Lakehouse"
   path: "/​Files/​raw"

3. onelake directory create
   item: "DataLakehouse.Lakehouse"
   path: "/​Files/​processed"

4. onelake upload file
   item: "DataLakehouse.Lakehouse"
   local_path: "./​data.csv"
   onelake_path: "/​Files/​raw/​data.csv"

5. onelake file list
   item: "DataLakehouse.Lakehouse"
   - Verify upload

Workflow 3: Build Data Pipeline

1. publicapis_get
   workload: "DataPipeline"

2. publicapis_bestpractices_itemdefinition_get
   workload: "DataPipeline"
   item_type: "activity"

3. onelake item create
   item_type: "DataPipeline"
   display_name: "ETL Pipeline"
   definition: { /​* pipeline config */ }

4. publicapis_bestpractices_get
   topic: "error_handling"

Best Practices

API Usage

1. Start with publicapis_list - Discover available workloads
2. Get full spec - Use publicapis_get for complete API documentation
3. Use schemas - Validate against item definitions
4. Follow examples - Start from provided examples
5. Handle errors - Implement retry logic from best practices

OneLake Management

1. Use friendly names - Prefer name.type format over GUIDs
2. Check before operations - List files/​items first
3. Organize structure - Create logical directory hierarchies
4. Cleanup regularly - Delete unnecessary files
5. Verify uploads - List files after upload operations

Item Creation

1. Get schema first - Use itemdefinition_get before creating
2. Validate configuration - Check required properties
3. Start simple - Begin with minimal configuration
4. Test incrementally - Create, verify, then enhance
5. Use examples - Adapt from examples_get results

Fabric Workload Reference

Lakehouse

• Purpose: Delta Lake storage with SQL analytics
• Use publicapis_get: Lakehouse
• Item type: Lakehouse
• OneLake structure: /​Files, /​Tables

Data Pipeline

• Purpose: Data integration and ETL
• Use publicapis_get: DataPipeline
• Item type: DataPipeline
• Components: Activities, datasets, linked services

Semantic Model

• Purpose: Power BI datasets
• Use publicapis_get: SemanticModel
• Item type: SemanticModel
• Components: Tables, measures, relationships

Notebook

• Purpose: Interactive code notebooks
• Use publicapis_get: Notebook
• Item type: Notebook
• Languages: Python, Scala, R, SQL

Warehouse

• Purpose: SQL data warehouse
• Use publicapis_get: Warehouse
• Item type: Warehouse
• Features: T-SQL, tables, views, procedures

KQL Database

• Purpose: Real-time analytics with Kusto
• Use publicapis_get: KQLDatabase
• Item type: KQLDatabase
• Query language: KQL (Kusto Query Language)

When to Use This Skill

• Developing Microsoft Fabric integrations
• Building Fabric REST API clients
• Creating Lakehouses and data pipelines
• Managing OneLake storage programmatically
• Understanding Fabric item schemas
• Implementing Fabric best practices
• Automating Fabric workspace operations
• Learning Fabric API capabilities

Keywords

microsoft fabric, onelake, lakehouse, data pipeline, semantic model, notebook, warehouse, kql database, fabric api, openapi, rest api, item definition, schema, best practices, workspace management, file operations, fabric workload