How do I use Force Fabric MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Force Fabric MCP Server Scan my warehouse for performance bottlenecks and security issues." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Force Fabric MCP Server by tmdaidevs

✨ Key Features

🔍 Detect — 120 Rules Across 4 Fabric Items

Item	Rules	What's Scanned
🏠 Lakehouse	29	SQL Endpoint + OneLake Delta Log (VACUUM history, file sizes, partitioning, retention)
🏗️ Warehouse	39	Schema, query performance, security (PII, RLS), database config
📊 Eventhouse	20/db	Extent fragmentation, caching/retention/merge/encoding/partitioning policies, ingestion, query performance, materialized views, stored functions
📐 Semantic Model	32	DAX expression anti-patterns, model structure, COLUMNSTATISTICS BPA
	120 total

🔧 Fix — 45 Auto-Fixable Issues

Item	Auto-Fixes	Method
🏗️ Warehouse	12 fixes	SQL DDL executed directly
🏠 Lakehouse	14 fixes	REST API (3) + Notebook Spark SQL (11)
📐 Semantic Model	12 fixes	model.bim REST API (6) + Notebook sempy (6)
📊 Eventhouse	7 fixes	KQL management commands (with dry-run preview)
	45 total

📊 Unified Output

Every scan returns a clean results table — only issues shown, passed rules counted in summary:

29 rules — ✅ 18 passed | 🔴 1 failed | 🟡 10 warning

| Rule | Status | Finding | Recommendation |
|------|--------|---------|----------------|
| LH-007 Key Columns Are NOT NULL | 🔴 | 16 key column(s) allow NULL: table.finding_id, ... | Add NOT NULL constraints |
| LH-017 Regular VACUUM Executed | 🟡 | 4 table(s) need VACUUM: table1, table2, ... | Run VACUUM weekly |

🚀 Quick Start

Prerequisites

Node.js 18+
Azure CLI with az login completed
Fabric capacity with items to scan

Install

git clone https://github.com/tmdaidevs/Force-Fabric-MCP-Server.git
cd Force-Fabric-MCP-Server
npm install
npm run build

Configure VS Code

Add to .vscode/mcp.json in your project:

{
  "servers": {
    "fabric-optimization": {
      "type": "stdio",
      "command": "node",
      "args": ["dist/index.js"],
      "cwd": "/path/to/Force-Fabric-MCP-Server"
    }
  }
}

Use

1. "Login to Fabric with azure_cli"
2. "List all lakehouses in workspace <id>"
3. "Scan lakehouse <id> in workspace <id>"
4. "Fix warehouse <id> in workspace <id>"

🔍 Detect & Scan

Available Scan Tools

Tool	What It Does
`lakehouse_optimization_recommendations`	Scans SQL Endpoint + reads Delta Log files from OneLake
`warehouse_optimization_recommendations`	Connects via SQL and runs 39 diagnostic queries
`warehouse_analyze_query_patterns`	Focused analysis of slow/frequent/failed queries
`eventhouse_optimization_recommendations`	Runs KQL diagnostics on each KQL database
`semantic_model_optimization_recommendations`	Executes DAX + MDSCHEMA DMVs for BPA analysis

Data Sources Used

                          ┌─────────────────────────────────────┐
                          │         Fabric REST API             │
                          │  Workspaces, Items, Metadata        │
                          └──────────────┬──────────────────────┘
                                         │
          ┌──────────────┬───────────────┼───────────────┬──────────────┐
          ▼              ▼               ▼               ▼              ▼
   ┌─────────────┐ ┌──────────┐ ┌──────────────┐ ┌──────────┐ ┌──────────────┐
   │  SQL Client │ │ KQL REST │ │ OneLake ADLS │ │ DAX API  │ │ MDSCHEMA DMV │
   │  (tedious)  │ │   API    │ │  Gen2 API    │ │executeQry│ │  via REST    │
   └──────┬──────┘ └────┬─────┘ └──────┬───────┘ └────┬─────┘ └──────┬───────┘
          │              │              │              │              │
    Lakehouse SQL   Eventhouse    Delta Log JSON   Semantic     Semantic
    Warehouse SQL   KQL DBs       File Metadata    Model DAX    Model Meta

🔧 Auto-Fix

Warehouse Fixes (`warehouse_fix`)

Run all safe fixes or specify individual rule IDs:

Rule ID	What It Fixes	SQL Command
WH-001	Missing primary keys	`ALTER TABLE ADD CONSTRAINT PK NOT ENFORCED`
WH-008	Stale statistics (>30 days)	`UPDATE STATISTICS [table]`
WH-009	Disabled constraints	`ALTER TABLE WITH CHECK CHECK CONSTRAINT ALL`
WH-016	Missing audit columns	`ALTER TABLE ADD created_at DATETIME2 DEFAULT GETDATE()`
WH-018	Unmasked sensitive data	`ALTER COLUMN ADD MASKED WITH (FUNCTION='...')`
WH-026	Auto-update statistics off	`ALTER DATABASE SET AUTO_UPDATE_STATISTICS ON`
WH-027	Result set caching off	`ALTER DATABASE SET RESULT_SET_CACHING ON`
WH-028	Snapshot isolation off	`ALTER DATABASE SET ALLOW_SNAPSHOT_ISOLATION ON`
WH-029	Page verify not CHECKSUM	`ALTER DATABASE SET PAGE_VERIFY CHECKSUM`
WH-030	ANSI settings off	`ALTER DATABASE SET ANSI_NULLS ON; ...`
WH-032	Missing statistics	`UPDATE STATISTICS [table]`
WH-036	NOT NULL without defaults	`ALTER TABLE ADD DEFAULT ... FOR column`

Eventhouse Fixes (`eventhouse_fix`)

Supports dry-run mode (dryRun: true) to preview commands without executing them.

Rule ID	What It Fixes	KQL Command
EH-002	Fragmented extents	`.merge table ['name']`
EH-004	Missing caching policy	`.alter table/database policy caching hot = 30d`
EH-005	Missing retention policy	`.alter table/database policy retention softdelete = 365d`
EH-006	Unhealthy materialized views	`.enable materialized-view ['name']`
EH-014	Missing ingestion batching	`.alter table/database policy ingestionbatching ...`
EH-016	Large tables without partitioning	`.alter table policy partitioning ...`
EH-017	Suboptimal merge policy	`.alter table policy merge ...`

Lakehouse Fixes (`lakehouse_run_table_maintenance`)

Fix	Parameters
OPTIMIZE with V-Order	`optimizeSettings: { vOrder: true }`
Z-Order by columns	`optimizeSettings: { zOrderColumns: ["col1", "col2"] }`
VACUUM stale files	`vacuumSettings: { retentionPeriod: "7.00:00:00" }`

Semantic Model Fixes (`semantic_model_fix`)

Downloads model.bim, applies modifications, uploads back:

Fix ID	What It Fixes	Method
SM-FIX-FORMAT	Add format strings to measures without one	model.bim
SM-FIX-DESC	Add descriptions to visible tables	model.bim
SM-FIX-HIDDEN	Set IsAvailableInMDX=false on hidden columns	model.bim
SM-FIX-DATE	Mark date/calendar tables as Date table	model.bim
SM-FIX-KEY	Set IsKey=true on PK columns in relationships	model.bim
SM-FIX-AUTODATE	Remove auto-date tables	model.bim

📓 Notebook-Based Fixes

For fixes that require Spark SQL, the MCP server creates a temporary Notebook, runs it, and deletes it:

1. POST /notebooks              → Create temp notebook with fix code
2. POST /items/{id}/jobs        → Execute notebook
3. GET  /items/{id}/jobs/{job}  → Poll until complete
4. DELETE /notebooks/{id}       → Clean up

Lakehouse Notebook Fixes

Rule	Spark SQL Command
LH-003	`CONVERT TO DELTA spark_catalog.lakehouse.table`
LH-005	`DROP TABLE lakehouse.table`
LH-009	`ALTER TABLE lakehouse.table RENAME COLUMN old TO new`
LH-014	`ALTER TABLE t ADD COLUMN created_at TIMESTAMP DEFAULT current_timestamp()`
LH-020	`ALTER TABLE t SET TBLPROPERTIES ('delta.autoOptimize.optimizeWrite'='true')`
LH-021	`ALTER TABLE t SET TBLPROPERTIES ('delta.logRetentionDuration'='interval 30 days')`
LH-024	`ALTER TABLE t SET TBLPROPERTIES ('delta.dataSkippingNumIndexedCols'='32')`
LH-S04	`ALTER TABLE t ADD COLUMN id BIGINT`

Semantic Model Notebook Fixes (via sempy_labs)

Fix	sempy Code
Remove Calculated Columns	`tom.remove_column(table, column)`
Remove Calculated Tables	`tom.remove_table(table)`
Fix Bi-directional Relationships	`rel.CrossFilteringBehavior = OneDirection`
Fix RLS Expressions	`table_permission.FilterExpression = ...`
Sync DirectLake Schema	`labs.update_direct_lake_model_lakehouse_schema()`
Refresh Model	`fabric.refresh_dataset(dataset, workspace)`

📋 Rule Reference

Summary

Category	HIGH	MEDIUM	LOW	INFO	Total	Auto-Fix
🏠 Lakehouse	5	14	9	1	29	14 (3 REST + 11 Notebook)
🏗️ Warehouse	8	17	12	0	39	12 (SQL DDL)
📊 Eventhouse	4	7	3	3	20	7 (KQL + dry-run)
📐 Semantic Model	7	14	9	0	32	12 (6 model.bim + 6 Notebook)
Total	24	52	33	4	120	45

#	Rule	Category	Severity	Auto-Fix
LH-001	SQL Endpoint Active	Availability	HIGH	—
LH-002	Medallion Architecture Naming	Maintainability	LOW	—
LH-003	All Tables Use Delta Format	Performance	HIGH	📓 Notebook
LH-004	Table Maintenance Recommended	Performance	MEDIUM	🔧 REST API
LH-005	No Empty Tables	Data Quality	MEDIUM	📓 Notebook
LH-006	No Over-Provisioned String Columns	Performance	MEDIUM	—
LH-007	Key Columns Are NOT NULL	Data Quality	HIGH	—
LH-008	No Float/Real Precision Issues	Data Quality	MEDIUM	—
LH-009	Column Naming Convention	Maintainability	LOW	📓 Notebook
LH-010	Date Columns Use Proper Types	Data Quality	MEDIUM	—
LH-011	Numeric Columns Use Proper Types	Data Quality	MEDIUM	—
LH-012	No Excessively Wide Tables	Maintainability	LOW	—
LH-013	Schema Has NOT NULL Constraints	Data Quality	MEDIUM	—
LH-014	Tables Have Audit Columns	Maintainability	LOW	📓 Notebook
LH-015	Consistent Date Types Per Table	Data Quality	LOW	—
LH-S01	No Unprotected Sensitive Data	Security	HIGH	—
LH-S02	Large Tables Identified	Performance	INFO	—
LH-S03	No Deprecated Data Types	Maintainability	HIGH	—
LH-S04	All Tables Have Key Columns	Data Quality	MEDIUM	📓 Notebook
LH-016	Large Tables Are Partitioned	Performance	MEDIUM	—
LH-017	Regular VACUUM Executed	Maintenance	MEDIUM	🔧 REST API
LH-018	Regular OPTIMIZE Executed	Performance	MEDIUM	🔧 REST API
LH-019	No Small File Problem	Performance	HIGH	🔧 REST API
LH-020	Auto-Optimize Enabled	Performance	MEDIUM	📓 Notebook
LH-021	Retention Policy Configured	Maintenance	LOW	📓 Notebook
LH-022	Delta Log Version Count Reasonable	Performance	LOW	🔧 REST API
LH-023	Low Write Amplification	Performance	MEDIUM	—
LH-024	Data Skipping Configured	Performance	LOW	📓 Notebook
LH-025	Z-Order on Large Tables	Performance	MEDIUM	🔧 REST API

#	Rule	Category	Severity	Auto-Fix
WH-001	Primary Keys Defined	Data Quality	HIGH	🔧 SQL
WH-002	No Deprecated Data Types	Maintainability	HIGH	—
WH-003	No Float/Real Precision Issues	Data Quality	MEDIUM	—
WH-004	No Over-Provisioned Columns	Performance	MEDIUM	—
WH-005	Column Naming Convention	Maintainability	LOW	—
WH-006	Table Naming Convention	Maintainability	LOW	—
WH-007	No SELECT * in Views	Maintainability	LOW	—
WH-008	Statistics Are Fresh	Performance	MEDIUM	🔧 SQL
WH-009	No Disabled Constraints	Data Quality	MEDIUM	🔧 SQL
WH-010	Key Columns Are NOT NULL	Data Quality	HIGH	—
WH-011	No Empty Tables	Maintainability	MEDIUM	—
WH-012	No Excessively Wide Tables	Maintainability	MEDIUM	—
WH-013	Consistent Date Types	Data Quality	LOW	—
WH-014	Foreign Keys Defined	Maintainability	MEDIUM	—
WH-015	No Large BLOB Columns	Performance	MEDIUM	—
WH-016	Tables Have Audit Columns	Maintainability	LOW	🔧 SQL
WH-017	No Circular Foreign Keys	Data Quality	HIGH	—
WH-018	Sensitive Data Protected	Security	HIGH	🔧 SQL
WH-019	Row-Level Security	Security	MEDIUM	—
WH-020	Minimal db_owner Privileges	Security	MEDIUM	—
WH-021	No Over-Complex Views	Maintainability	LOW	—
WH-022	Minimal Cross-Schema Dependencies	Maintainability	LOW	—
WH-023	No Very Slow Queries	Performance	HIGH	—
WH-024	No Frequently Slow Queries	Performance	HIGH	—
WH-025	No Recent Query Failures	Reliability	MEDIUM	—
WH-026	AUTO_UPDATE_STATISTICS Enabled	Performance	HIGH	🔧 SQL
WH-027	Result Set Caching Enabled	Performance	MEDIUM	🔧 SQL
WH-028	Snapshot Isolation Enabled	Concurrency	MEDIUM	🔧 SQL
WH-029	Page Verify CHECKSUM	Reliability	MEDIUM	🔧 SQL
WH-030	ANSI Settings Correct	Standards	LOW	🔧 SQL
WH-031	Database ONLINE	Availability	HIGH	—
WH-032	All Tables Have Statistics	Performance	MEDIUM	🔧 SQL
WH-033	Optimal Data Types	Performance	MEDIUM	—
WH-034	No Near-Empty Tables	Maintainability	LOW	—
WH-035	Stored Procedures Documented	Maintainability	LOW	—
WH-036	NOT NULL Columns Have Defaults	Data Quality	MEDIUM	🔧 SQL
WH-037	Consistent String Types	Maintainability	LOW	—
WH-038	Schemas Are Documented	Maintainability	LOW	—
WH-039	Query Performance Healthy	Performance	MEDIUM	—

#	Rule	Category	Severity	Auto-Fix
EH-001	Query Endpoint Available	Availability	HIGH	—
EH-002	No Extent Fragmentation	Performance	HIGH	🔧 KQL
EH-003	Good Compression Ratio	Performance	MEDIUM	—
EH-004	Caching Policy Configured	Performance	MEDIUM	🔧 KQL
EH-005	Retention Policy Configured	Data Management	MEDIUM	🔧 KQL
EH-006	Materialized Views Healthy	Reliability	HIGH	🔧 KQL
EH-007	Data Is Fresh	Data Quality	MEDIUM	—
EH-008	No Slow Query Patterns	Performance	HIGH	—
EH-009	No Recent Failed Commands	Reliability	MEDIUM	—
EH-010	No Ingestion Failures	Reliability	HIGH	—
EH-011	Streaming Ingestion Config	Performance	INFO	—
EH-012	Continuous Exports Healthy	Reliability	MEDIUM	—
EH-013	Hot Cache Coverage	Performance	MEDIUM	—
EH-014	Ingestion Batching Configured	Performance	LOW	🔧 KQL
EH-015	Update Policies Configured	Data Management	INFO	—
EH-016	Partitioning on Large Tables	Performance	MEDIUM	🔧 KQL
EH-017	Merge Policy Configured	Performance	LOW	🔧 KQL
EH-018	Encoding Policy for Poorly Compressed	Performance	MEDIUM	—
EH-019	Row Order Policy	Performance	LOW	—
EH-020	Stored Functions Inventory	Data Management	INFO	—

#	Rule	Category	Severity	Auto-Fix
SM-001	Avoid IFERROR Function	DAX	MEDIUM	📓 Notebook
SM-002	Use DIVIDE Function	DAX	MEDIUM	📓 Notebook
SM-003	No EVALUATEANDLOG in Production	DAX	HIGH	📓 Notebook
SM-004	Use TREATAS not INTERSECT	DAX	MEDIUM	—
SM-005	No Duplicate Measure Definitions	DAX	LOW	—
SM-006	Filter by Columns Not Tables	DAX	MEDIUM	📓 Notebook
SM-007	Avoid Adding 0 to Measures	DAX	LOW	—
SM-008	Measures Have Documentation	Maintenance	LOW	🔧 model.bim + 📓
SM-009	Model Has Tables	Maintenance	HIGH	—
SM-010	Model Has Date Table	Performance	MEDIUM	🔧 model.bim
SM-011	Avoid 1-(x/y) Syntax	DAX	MEDIUM	—
SM-012	No Direct Measure References	DAX	LOW	—
SM-013	Avoid Nested CALCULATE	DAX	MEDIUM	—
SM-014	Use SUM Instead of SUMX	DAX	LOW	—
SM-015	Measures Have Format String	Formatting	LOW	🔧 model.bim
SM-016	Avoid FILTER(ALL(...))	DAX	MEDIUM	—
SM-017	Measure Naming Convention	Formatting	LOW	—
SM-018	Reasonable Table Count	Performance	LOW	—
SM-B01	No High Cardinality Text Columns	Data Types	HIGH	—
SM-B02	No Description/Comment Columns	Data Types	HIGH	—
SM-B03	No GUID/UUID Columns	Data Types	HIGH	—
SM-B04	No Constant Columns	Data Types	MEDIUM	—
SM-B05	No Booleans Stored as Text	Data Types	MEDIUM	—
SM-B06	No Dates Stored as Text	Data Types	MEDIUM	—
SM-B07	No Numbers Stored as Text	Data Types	MEDIUM	—
SM-B08	Integer Keys Not String Keys	Data Types	MEDIUM	—
SM-B09	No Excessively Wide Tables	Data Types	MEDIUM	—
SM-B10	No Extremely Wide Tables	Data Types	HIGH	—
SM-B11	No Multiple High-Cardinality Columns	Data Types	HIGH	—
SM-B12	No Single Column Tables	Data Types	LOW	—
SM-B13	No High-Precision Timestamps	Data Types	MEDIUM	—
SM-B14	No Low Cardinality in Fact Tables	Data Types	LOW	—

🏗️ Architecture

src/
├── index.ts                    MCP server entry point (stdio transport)
├── auth/
│   └── fabricAuth.ts           Azure auth (CLI, browser, device code, SP)
├── clients/
│   ├── fabricClient.ts         Fabric REST API + DAX + model.bim CRUD
│   ├── sqlClient.ts            SQL via tedious (Lakehouse + Warehouse)
│   ├── kqlClient.ts            KQL/Kusto REST API (Eventhouse)
│   ├── onelakeClient.ts        OneLake ADLS Gen2 + Delta Log parser
│   └── xmlaClient.ts           XMLA SOAP client (experimental)
└── tools/
    ├── ruleEngine.ts           Shared RuleResult type + unified renderer
    ├── auth.ts                 auth_login, auth_status, auth_logout
    ├── workspace.ts            workspace_list
    ├── lakehouse.ts            29 rules + table maintenance
    ├── warehouse.ts            39 rules + 12 auto-fixes
    ├── eventhouse.ts           20 rules + 7 auto-fixes (with dry-run)
    └── semanticModel.ts        32 rules + 6 auto-fixes (model.bim)

🔐 Authentication

Method	Use Case
`azure_cli`	Recommended — uses your `az login` session
`interactive_browser`	Opens browser for interactive login
`device_code`	Headless/remote environments
`vscode`	Uses VS Code Azure account
`service_principal`	CI/CD (requires tenantId, clientId, clientSecret)
`default`	Auto-detect best available method

📄 License

MIT

Force Fabric MCP Server

✨ Key Features

🔍 Detect — 120 Rules Across 4 Fabric Items

🔧 Fix — 45 Auto-Fixable Issues

📊 Unified Output

🚀 Quick Start

Prerequisites

Install

Configure VS Code

Use

🔍 Detect & Scan

Available Scan Tools

Data Sources Used

🔧 Auto-Fix

Warehouse Fixes (`warehouse_fix`)

Eventhouse Fixes (`eventhouse_fix`)

Lakehouse Fixes (`lakehouse_run_table_maintenance`)

Semantic Model Fixes (`semantic_model_fix`)

📓 Notebook-Based Fixes

Lakehouse Notebook Fixes

Semantic Model Notebook Fixes (via sempy_labs)

📋 Rule Reference

Summary

🏗️ Architecture

🔐 Authentication

📄 License

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API

✨ Key Features

🔍 Detect — 120 Rules Across 4 Fabric Items

🔧 Fix — 45 Auto-Fixable Issues

📊 Unified Output

🚀 Quick Start

Prerequisites

Install

Configure VS Code

Use

🔍 Detect & Scan

Available Scan Tools

Data Sources Used

🔧 Auto-Fix

Warehouse Fixes (warehouse_fix)

Eventhouse Fixes (eventhouse_fix)

Lakehouse Fixes (lakehouse_run_table_maintenance)

Semantic Model Fixes (semantic_model_fix)

📓 Notebook-Based Fixes

Lakehouse Notebook Fixes

Semantic Model Notebook Fixes (via sempy_labs)

📋 Rule Reference

Summary

🏗️ Architecture

🔐 Authentication

📄 License

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API

Warehouse Fixes (`warehouse_fix`)

Eventhouse Fixes (`eventhouse_fix`)

Lakehouse Fixes (`lakehouse_run_table_maintenance`)

Semantic Model Fixes (`semantic_model_fix`)