Force Fabric MCP Server

Detect issues. Auto-fix problems. Optimize your Fabric tenant.
An MCP server that scans Lakehouses, Warehouses, Eventhouses, and Semantic Models with 120 rules — and can auto-fix 45 of them.

Quick Start • Detect • Auto-Fix • Rules • Architecture

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✨ 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 |

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🚀 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>"

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🔍 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

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🔧 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)

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📋 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

🏠 Lakehouse — 29 Rules (click to expand)

#	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

🏗️ Warehouse — 39 Rules (click to expand)

#	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	—

📊 Eventhouse — 20 Rules per KQL Database (click to expand)

#	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	—

📐 Semantic Model — 32 Rules (click to expand)

#	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	—

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🏗️ 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