README - Fabric-Analytics-MCP by santhoshravindran7

Microsoft Fabric Analytics MCP Server

A comprehensive Model Context Protocol (MCP) server that provides analytics capabilities and tools for interacting with Microsoft Fabric data platform. This server enables AI assistants like Claude to seamlessly access, analyze, and monitor Microsoft Fabric resources through standardized MCP protocols, bringing the power of Microsoft Fabric directly to your AI conversations.

🌟 Key Features

🔄 Complete CRUD Operations - Create, read, update, and delete Fabric items
📓 Notebook Management - Create, execute, and manage Fabric notebooks with templates
⚡ Livy API Integration - Full Spark session and batch job management
📊 Spark Application Monitoring - Real-time monitoring across workspaces and items
🤖 Claude Desktop Ready - Plug-and-play integration with Claude Desktop
🔐 Enterprise Authentication - Multiple auth methods (Bearer, Service Principal, Device Code, Interactive)
🛡️ MSAL Integration - Microsoft Authentication Library for secure enterprise access
📈 Analytics & Insights - Generate comprehensive monitoring dashboards
🧪 Comprehensive Testing - Extensive test suite with real API validation
🔄 Token Management - Automatic token validation and expiration handling
☸️ Enterprise Deployment - Full Kubernetes and Azure deployment support
🔄 Docker Support - Containerized deployment with health checks
📊 Monitoring & Observability - Built-in Prometheus metrics and Grafana dashboards
🚀 Azure MCP Server - Native Azure hosting option (preview)

🚀 Deployment Options

🤖 Claude Desktop Integration

Recommended for AI Assistant Usage:

{
  "mcpServers": {
    "fabric-analytics": {
      "command": "node",
      "args": ["C:\\path\\to\\your\\build\\index.js"],
      "cwd": "C:\\path\\to\\your\\project",
      "env": {
        "FABRIC_AUTH_METHOD": "bearer_token",
        "FABRIC_TOKEN": "your_bearer_token_here",
        "FABRIC_WORKSPACE_ID": "your_workspace_id",
        "ENABLE_HEALTH_SERVER": "false"
      }
    }
  }
}

💡 Get Bearer Token: Visit Power BI Embed Setup to generate tokens

⚠️ Important: Tokens expire after ~1 hour and need to be refreshed

🔧 Claude Desktop Authentication Fix

If you experience 60-second timeouts during startup, this is due to interactive authentication flows blocking Claude Desktop's sandboxed environment. Solution:

Use Bearer Token Method (Recommended):
- Set FABRIC_AUTH_METHOD: "bearer_token" in your config
- Provide FABRIC_TOKEN with a valid bearer token
- This bypasses interactive authentication entirely
Alternative - Per-Tool Authentication:
- Provide token directly in tool calls: bearerToken: "your_token_here"
- Or use simulation mode: bearerToken: "simulation"
Troubleshooting:
- Server now has 10-second timeout protection to prevent hanging
- Falls back to simulation mode if authentication fails
- Enhanced error messages provide clear guidance

🎯 Quick Fix: The server automatically prioritizes FABRIC_TOKEN environment variable over interactive authentication flows, preventing Claude Desktop timeouts.

📱 Local Development

# Clone and run locally
git clone https://github.com/santhoshravindran7/Fabric-Analytics-MCP.git
cd Fabric-Analytics-MCP
npm install && npm run build && npm start

🐳 Docker Deployment

# Using Docker Compose
docker-compose up -d

# Or standalone Docker
docker build -t fabric-analytics-mcp .
docker run -p 3000:3000 -e FABRIC_CLIENT_ID=xxx fabric-analytics-mcp

☸️ Azure Kubernetes Service (AKS)

# One-command enterprise deployment
export ACR_NAME="your-registry" FABRIC_CLIENT_ID="xxx" FABRIC_CLIENT_SECRET="yyy" FABRIC_TENANT_ID="zzz"
./scripts/setup-azure-resources.sh && ./scripts/build-and-push.sh && ./scripts/deploy-to-aks.sh

🌐 Azure MCP Server (Preview)

# Serverless deployment on Azure
az mcp server create --name "fabric-analytics-mcp" --repository "santhoshravindran7/Fabric-Analytics-MCP"

📚 Detailed Guides:

🛠️ Tools & Capabilities

🔍 CRUD Operations for Fabric Items

Tool: list-fabric-items
Description: List items in a Microsoft Fabric workspace (Lakehouses, Notebooks, etc.)
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- itemType: Filter by item type (optional)
Tool: create-fabric-item
Description: Create new items in Microsoft Fabric workspace
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- itemType: Type of item (Lakehouse, Notebook, Dataset, Report, Dashboard)
- displayName: Display name for the new item
- description: Optional description
Tool: get-fabric-item
Description: Get detailed information about a specific Microsoft Fabric item
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- itemId: ID of the item to retrieve
Tool: update-fabric-item
Description: Update existing items in Microsoft Fabric workspace
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- itemId: ID of the item to update
- displayName: New display name (optional)
- description: New description (optional)
Tool: delete-fabric-item
Description: Delete items from Microsoft Fabric workspace
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- itemId: ID of the item to delete

🔍 Query Fabric Dataset (Enhanced)

Tool: query-fabric-dataset
Description: Execute SQL or KQL queries against Microsoft Fabric datasets
Parameters:
- bearerToken: Microsoft Fabric bearer token (optional - uses simulation if not provided)
- workspaceId: Microsoft Fabric workspace ID
- datasetName: Name of the dataset to query
- query: SQL or KQL query to execute

🚀 Execute Fabric Notebook

Tool: execute-fabric-notebook
Description: Execute a notebook in Microsoft Fabric workspace
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- notebookId: ID of the notebook to execute
- parameters: Optional parameters to pass to the notebook

📊 Get Analytics Metrics

Tool: get-fabric-metrics
Description: Retrieve performance and usage metrics for Microsoft Fabric items
Parameters:
- workspaceId: Microsoft Fabric workspace ID
- itemId: Item ID (dataset, report, etc.)
- timeRange: Time range for metrics (1h, 24h, 7d, 30d)
- metrics: List of metrics to analyze

🔧 Analyze Data Model

Tool: analyze-fabric-model
Description: Analyze a Microsoft Fabric data model and get optimization recommendations
Parameters:
- workspaceId: Microsoft Fabric workspace ID
- itemId: Item ID to analyze

📋 Generate Analytics Report

Tool: generate-fabric-report
Description: Generate comprehensive analytics reports for Microsoft Fabric workspaces
Parameters:
- workspaceId: Microsoft Fabric workspace ID
- reportType: Type of report (performance, usage, health, summary)

🚀 Livy API Integration (Sessions & Batch Jobs)

Session Management

Tool: create-livy-session
Description: Create a new Livy session for interactive Spark/SQL execution
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
- sessionConfig: Optional session configuration
Tool: get-livy-session
Description: Get details of a Livy session
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
- sessionId: Livy session ID
Tool: list-livy-sessions
Description: List all Livy sessions in a lakehouse
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
Tool: delete-livy-session
Description: Delete a Livy session
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
- sessionId: Livy session ID

Statement Execution

Tool: execute-livy-statement
Description: Execute SQL or Spark statements in a Livy session
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
- sessionId: Livy session ID
- code: SQL or Spark code to execute
- kind: Statement type (sql, spark, etc.)
Tool: get-livy-statement
Description: Get status and results of a Livy statement
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
- sessionId: Livy session ID
- statementId: Statement ID

Batch Job Management

Tool: create-livy-batch
Description: Create a new Livy batch job for long-running operations
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
- batchConfig: Batch job configuration
Tool: get-livy-batch
Description: Get details of a Livy batch job
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
- batchId: Batch job ID
Tool: list-livy-batches
Description: List all Livy batch jobs in a lakehouse
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
Tool: delete-livy-batch
Description: Delete a Livy batch job
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Microsoft Fabric lakehouse ID
- batchId: Batch job ID

📊 Spark Application Monitoring

Workspace-Level Monitoring

Tool: get-workspace-spark-applications
Description: Get all Spark applications in a Microsoft Fabric workspace
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- continuationToken: Optional token for pagination

Item-Specific Monitoring

Tool: get-notebook-spark-applications
Description: Get all Spark applications for a specific notebook
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- notebookId: Notebook ID
- continuationToken: Optional token for pagination
Tool: get-lakehouse-spark-applications
Description: Get all Spark applications for a specific lakehouse
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- lakehouseId: Lakehouse ID
- continuationToken: Optional token for pagination
Tool: get-spark-job-definition-applications
Description: Get all Spark applications for a specific Spark Job Definition
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- sparkJobDefinitionId: Spark Job Definition ID
- continuationToken: Optional token for pagination

Application Management

Tool: get-spark-application-details
Description: Get detailed information about a specific Spark application
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- livyId: Livy session ID
Tool: cancel-spark-application
Description: Cancel a running Spark application
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- livyId: Livy session ID

Monitoring Dashboard

Tool: get-spark-monitoring-dashboard
Description: Generate a comprehensive monitoring dashboard with analytics
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID

📓 Notebook Management

The MCP server provides comprehensive notebook management capabilities with predefined templates and custom notebook support.

Create Notebook from Template

Tool: create-fabric-notebook
Description: Create new Fabric notebooks from predefined templates or custom definitions
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- displayName: Display name for the new notebook
- template: Template type (blank, sales_analysis, nyc_taxi_analysis, data_exploration, machine_learning, custom)
- customNotebook: Custom notebook definition (required if template is 'custom')
- environmentId: Optional environment ID to attach
- lakehouseId: Optional default lakehouse ID
- lakehouseName: Optional default lakehouse name

Available Templates:

blank: Basic notebook with minimal setup
sales_analysis: Comprehensive sales data analysis with sample dataset
nyc_taxi_analysis: NYC taxi trip data analysis with sample dataset
data_exploration: Structured data exploration template
machine_learning: Complete ML workflow template
custom: Use your own notebook definition

Get Notebook Definition

Tool: get-fabric-notebook-definition
Description: Retrieve the notebook definition (cells, metadata) from an existing Fabric notebook
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- notebookId: ID of the notebook to retrieve
- format: Format to return (ipynb or fabricGitSource)

Update Notebook Definition

Tool: update-fabric-notebook-definition
Description: Update the notebook definition (cells, metadata) of an existing Fabric notebook
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- notebookId: ID of the notebook to update
- notebookDefinition: Updated notebook definition object

Execute Notebook

Tool: run-fabric-notebook
Description: Execute a Fabric notebook on-demand with optional parameters and configuration
Parameters:
- bearerToken: Microsoft Fabric bearer token
- workspaceId: Microsoft Fabric workspace ID
- notebookId: ID of the notebook to run
- parameters: Optional notebook parameters (key-value pairs with types)
- configuration: Optional execution configuration (environment, lakehouse, pools, etc.)

Features:

📓 Base64 encoded notebook payload support
🔧 Comprehensive metadata management
🌐 Environment and lakehouse integration
🎛️ Parameterized notebook execution
⚡ Spark configuration support
🔤 Support for multiple programming languages (Python, Scala, SQL, R)

🚀 Quick Start

Prerequisites

Node.js 18+ and npm
Microsoft Fabric workspace access
Claude Desktop (for AI integration)

Installation & Setup

Clone and Install
```
git clone https://github.com/santhoshravindran7/Fabric-Analytics-MCP.git
cd Fabric-Analytics-MCP
npm install
npm run build    # ✅ All configuration files included!
```
📝 Note: All essential configuration files (tsconfig.json, jest.config.json, etc.) are now properly included in the repository. Previous build issues have been resolved.

Configure Claude Desktop

Add to your Claude Desktop config:

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "fabric-analytics": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PROJECT/build/index.js"]
    }
  }
}

Start Using

Restart Claude Desktop and try these queries: - "List all items in my Fabric workspace [your-workspace-id]"
- "Create a new lakehouse called 'Analytics Hub'"
- "Show me all running Spark applications"
- "Execute this SQL query: SELECT * FROM my_table LIMIT 10"

🧪 Development & Testing

Running the Server

npm start        # Production mode
npm run dev      # Development mode with auto-reload

Testing Livy API Integration

For comprehensive testing of Spark functionality, install Python dependencies:

pip install -r livy_requirements.txt

Available Test Scripts:

livy_api_test.ipynb - Interactive notebook for step-by-step testing
comprehensive_livy_test.py - Full-featured test with error handling
spark_monitoring_test.py - Spark application monitoring tests
mcp_spark_monitoring_demo.py - MCP server integration demo

Claude Desktop Integration

Add this configuration to your Claude Desktop config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "fabric-analytics": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PROJECT/build/index.js"]
    }
  }
}

🎉 You're ready! Restart Claude Desktop and start asking questions about your Microsoft Fabric data!

Livy API Testing Setup

For testing the Livy API functionality, additional Python dependencies are required:

# Install Python dependencies for Livy API testing
pip install -r livy_requirements.txt

Available Test Scripts:

livy_api_test.ipynb - Interactive Jupyter notebook for step-by-step testing
comprehensive_livy_test.py - Full-featured test with error handling
simple_livy_test.py - Simple test following example patterns
livy_batch_test.py - Batch job testing capabilities
livy_setup.py - Quick setup and configuration helper

Usage

Running the Server

npm start

Development Mode

npm run dev

Testing with Claude Desktop

Add the following configuration to your Claude Desktop config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "fabric-analytics": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PROJECT/build/index.js"]
    }
  }
}

💬 Example Queries

Once connected to Claude Desktop, you can ask natural language questions like:

CRUD Operations:

"List all Lakehouses in my workspace"
"Create a new Notebook called 'Data Analysis'"
"Update the description of my lakehouse"
"Delete the test notebook from my workspace"

Notebook Management:

"Create a sales analysis notebook with sample data"
"Generate a new NYC taxi analysis notebook"
"Create a machine learning notebook template"
"Get the definition of my existing notebook"
"Run my notebook with specific parameters"
"Update my notebook with new cells"

Data Operations:

"Query the sales dataset to get total revenue by region"
"Execute my analytics notebook with today's date"

Analytics:

"Get performance metrics for the last 24 hours"
"Analyze my data model and provide optimization recommendations"
"Generate a usage report for my workspace"

Livy API Operations:

"Create a Livy session for interactive Spark analysis"
"Execute SQL query 'SELECT * FROM my_table LIMIT 10'"
"Run Spark code to show all tables"
"Monitor my batch job progress"

Spark Application Monitoring:

"Show me all Spark applications in my workspace"
"What's the status of my notebook Spark jobs?"
"Generate a comprehensive Spark monitoring dashboard"
"Show me recent failed applications"
"Cancel the problematic Spark application"

🔐 Authentication

This MCP server supports multiple authentication methods powered by Microsoft Authentication Library (MSAL):

🤖 For Claude Desktop: Use Bearer Token Authentication (Method #1) for the best experience and compatibility.

🔧 Claude Desktop Fix: Recent updates prevent authentication timeouts by prioritizing bearer tokens and adding timeout protection for interactive authentication flows.

🎫 1. Bearer Token Authentication (Recommended for Claude Desktop)

Perfect for AI assistants and interactive usage:

For Claude Desktop:

Visit Power BI Embed Setup
Generate a bearer token for your workspace
Add to your claude_desktop_config.json
No timeout issues - bypasses interactive authentication entirely

For Testing:

# All test scripts will prompt for authentication method
python enhanced_auth_test.py

🤖 2. Service Principal Authentication (Recommended for Production)

Use Azure AD application credentials:

Client ID (Application ID)
Client Secret
Tenant ID (Directory ID)

Environment Variables Setup:

export FABRIC_AUTH_METHOD="service_principal"
export FABRIC_CLIENT_ID="your-app-client-id"
export FABRIC_CLIENT_SECRET="your-app-client-secret"
export FABRIC_TENANT_ID="your-tenant-id"
export FABRIC_DEFAULT_WORKSPACE_ID="your-workspace-id"

Claude Desktop Configuration:

{
  "mcpServers": {
    "fabric-analytics": {
      "command": "node",
      "args": ["/path/to/build/index.js"],
      "env": {
        "FABRIC_AUTH_METHOD": "service_principal",
        "FABRIC_CLIENT_ID": "your-client-id",
        "FABRIC_CLIENT_SECRET": "your-client-secret",
        "FABRIC_TENANT_ID": "your-tenant-id"
      }
    }
  }
}

📱 3. Device Code Authentication

export FABRIC_AUTH_METHOD="device_code"
export FABRIC_CLIENT_ID="your-client-id"
export FABRIC_TENANT_ID="your-tenant-id"

🌐 4. Interactive Authentication

Automatic browser-based authentication:

export FABRIC_AUTH_METHOD="interactive"
export FABRIC_CLIENT_ID="your-client-id"
export FABRIC_TENANT_ID="your-tenant-id"

🔧 Complete Authentication Setup

📚 Detailed Guides:

- Complete Azure AD setup
- Ready-to-use configurations

🔍 Authentication Testing

Check your authentication status:

"Check my Fabric authentication status"
"What authentication method am I using?"
"Test my Microsoft Fabric authentication setup"

🔒 Security Best Practices

Never commit authentication tokens to version control
Use Service Principal authentication for production deployments
Device Code flow is perfect for CI/CD and headless environments
Interactive authentication is ideal for development and testing
All tokens are automatically validated and include expiration checking

Note: The MCP server seamlessly handles token validation and provides clear error messages for authentication issues.

☸️ Azure Kubernetes Service (AKS) Deployment

Deploy the MCP server as a scalable service on Azure Kubernetes Service for enterprise production use.

🚀 Quick AKS Deployment

Prerequisites

Azure CLI installed and configured
Docker installed
kubectl installed
Azure subscription with AKS permissions

1. Build and Push Docker Image

# Build the Docker image
npm run docker:build

# Tag and push to Azure Container Registry
npm run docker:push

2. Deploy to AKS

# Create Azure resources and deploy
./scripts/deploy-to-aks.sh

3. Access the MCP Server

Once deployed, your MCP server will be available at:

https://your-aks-cluster.region.cloudapp.azure.com/mcp

🏗️ Architecture Overview

The AKS deployment includes:

Horizontal Pod Autoscaler (3-10 pods based on CPU/memory)
Azure Load Balancer for high availability
SSL/TLS termination with Azure Application Gateway
ConfigMaps for environment configuration
Secrets for secure credential storage
Health checks and readiness probes
Resource limits and quality of service guarantees

📁 Deployment Files

All Kubernetes manifests are located in the /k8s directory:

namespace.yaml - Dedicated namespace
deployment.yaml - Application deployment with scaling
service.yaml - Load balancer service
ingress.yaml - External access and SSL
configmap.yaml - Configuration management
secret.yaml - Secure credential storage
hpa.yaml - Horizontal Pod Autoscaler

🔧 Configuration

Configure the deployment by setting these environment variables:

export AZURE_SUBSCRIPTION_ID="your-subscription-id"
export AZURE_RESOURCE_GROUP="fabric-mcp-rg"
export AKS_CLUSTER_NAME="fabric-mcp-cluster"
export ACR_NAME="fabricmcpregistry"
export DOMAIN_NAME="your-domain.com"

🔐 Production Security

The AKS deployment includes enterprise-grade security:

Non-root container execution
Read-only root filesystem
Secret management via Azure Key Vault integration
Network policies for traffic isolation
RBAC with minimal required permissions
Pod security standards enforcement

📊 Monitoring & Scaling

Azure Monitor integration for logs and metrics
Application Insights for performance monitoring
Prometheus metrics endpoint for custom monitoring
Auto-scaling based on CPU (70%) and memory (80%) thresholds
Health checks for automatic pod restart

🔄 CI/CD Integration

The deployment scripts support:

Azure DevOps pipelines
GitHub Actions workflows
Automated testing before deployment
Blue-green deployments for zero downtime
Rollback capabilities for quick recovery

📚 Detailed Guide: See for complete setup instructions.

🌐 Azure Model Context Protocol Server (Preview)

Microsoft Azure now offers a preview service for hosting MCP servers natively. This eliminates the need for custom infrastructure management.

🚀 Azure MCP Server Deployment

Prerequisites

Azure subscription with MCP preview access
Azure CLI with MCP extensions

Deploy to Azure MCP Service

# Login to Azure
az login

# Enable MCP preview features
az extension add --name mcp-preview

# Deploy the MCP server
az mcp server create \
  --name "fabric-analytics-mcp" \
  --resource-group "your-rg" \
  --source-type "github" \
  --repository "santhoshravindran7/Fabric-Analytics-MCP" \
  --branch "main" \
  --auth-method "service-principal"

Configure Authentication

# Set up service principal authentication
az mcp server config set \
  --name "fabric-analytics-mcp" \
  --setting "FABRIC_CLIENT_ID=your-client-id" \
  --setting "FABRIC_CLIENT_SECRET=your-secret" \
  --setting "FABRIC_TENANT_ID=your-tenant-id"

Access Your MCP Server

# Get the server endpoint
az mcp server show --name "fabric-analytics-mcp" --query "endpoint"

🔧 Azure MCP Server Features

Automatic scaling based on usage
Built-in monitoring and logging
Integrated security with Azure AD
Zero infrastructure management
Global CDN for low latency
Automatic SSL/TLS certificates

💰 Cost Optimization

Azure MCP Server offers:

Pay-per-request pricing model
Automatic hibernation during idle periods
Resource sharing across multiple clients
No minimum infrastructure costs

📚 Learn More: Azure MCP Server Documentation

Note: Azure MCP Server is currently in preview. Check Azure Preview Terms for service availability and limitations.

🏗️ Architecture

This MCP server is built with:

TypeScript for type-safe development
MCP SDK for Model Context Protocol implementation
Zod for schema validation and input sanitization
Node.js runtime environment

⚙️ Configuration

The server uses the following configuration files:

tsconfig.json - TypeScript compiler configuration
package.json - Node.js package configuration
.vscode/mcp.json - MCP server configuration for VS Code

🔧 Development

Project Structure

├── src/
│   ├── index.ts              # Main MCP server implementation
│   └── fabric-client.ts      # Microsoft Fabric API client
├── build/                    # Compiled JavaScript output
├── tests/                    # Test scripts and notebooks
├── .vscode/                  # VS Code configuration
├── package.json
├── tsconfig.json
└── README.md

Adding New Tools

To add new tools to the server:

Define the input schema using Zod
Implement the tool using server.tool()
Add error handling and validation
Update documentation

API Integration

This server includes:

✅ Production Ready:

Full Microsoft Fabric Livy API integration
Spark session lifecycle management
Statement execution with SQL and Spark support
Batch job management for long-running operations
Comprehensive error handling and retry logic
Real-time polling and result retrieval

🧪 Demonstration Features:

CRUD operations (configurable for real APIs)
Analytics and metrics (extensible framework)
Data model analysis (template implementation)

🧪 Testing

Prerequisites

# Install Python dependencies for API testing
pip install -r livy_requirements.txt

Available Test Scripts

livy_api_test.ipynb - Interactive Jupyter notebook for step-by-step testing
comprehensive_livy_test.py - Full-featured test with error handling
simple_livy_test.py - Simple test following example patterns
livy_batch_test.py - Batch job testing capabilities
spark_monitoring_test.py - Spark application monitoring tests

Quick Testing

Interactive Testing:
```
jupyter notebook livy_api_test.ipynb
```

Command Line Testing:

python simple_livy_test.py
python spark_monitoring_test.py

Comprehensive Testing:

🤝 Contributing

We welcome contributions! Here's how to get started:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes and add tests if applicable
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Follow TypeScript best practices
Add JSDoc comments for new functions
Update tests for any new functionality
Update documentation as needed
See for detailed guidelines

🔒 Security

Never commit authentication tokens to version control
Use environment variables for sensitive configuration
Follow Microsoft Fabric security best practices
Report security issues privately via GitHub security advisories
See for our full security policy

📝 License

This project is licensed under the MIT License - see the file for details.

Support

For issues and questions:

📖 Check the MCP documentation
📚 Review Microsoft Fabric API documentation
🐛 Open an issue in this repository
💬 Join the community discussions

Acknowledgments

Microsoft Fabric Analytics team for the comprehensive data platform and analytics capabilities
Microsoft Fabric Platform teams for the robust API platform and infrastructure
Bogdan Crivat and Chris Finlan for the inspiring brainstorming conversation that gave me the idea to open-source this project
Anthropic for the Model Context Protocol specification

This project began as my weekend hack project exploring AI integration with Microsoft Fabric. During a casual conversation with Chris and Bogdan about making AI tooling more accessible. What started as a personal experiment over a weekend is now available for everyone to build upon.