Fabric-Analytics-MCP

# Example Usage and Local API Testing This file contains example usage scenarios for the Microsoft Fabric Analytics MCP Server, including how to test API methods locally with your real workspace data. ## 🔍 Quick Workspace Details Getter **Get workspace details in 30 seconds:** ```bash # Debug version with detailed logging python get_workspace_details_debug.py ``` This focused script will: - ✅ Connect to your workspace - ✅ List all items with full details - ✅ Show summary by item type - ✅ Display IDs for further operations - ✅ Export details to JSON (optional) **Sample Output:** ``` 🏢 MICROSOFT FABRIC WORKSPACE OVERVIEW ============================================================ 📍 Workspace ID: c22f6805-d84a-4143-80b2-0c9e9832e5a2 📊 Fetching workspace items... ✅ Found 15 items in workspace 📋 WORKSPACE CONTENTS SUMMARY: ---------------------------------------- Lakehouse : 3 items Notebook : 8 items Dataset : 2 items Report : 2 items ---------------------------------------- TOTAL : 15 items 📝 DETAILED ITEM LIST: ================================================================================ 🔹 Lakehouse (3 items): -------------------------------------------------- 1. Sales Analytics Lakehouse ID: 683ce7d6-5d0d-4164-b594-d2cc8dbf70ac Created: 2024-12-15T10:30:00Z Modified: 2024-12-18T15:45:00Z 2. Customer Data Lakehouse ID: a1b2c3d4-e5f6-7890-abcd-ef1234567890 Created: 2024-12-10T14:20:00Z ``` ## 🚀 Testing MCP Server API Methods Locally ### Comprehensive API Testing Script ```bash # Run the direct API testing script python test_mcp_api.py ``` This script will: - ✅ Start your MCP server with real credentials - ✅ List all tools available - ✅ Get real workspace items and details - ✅ Test lakehouse operations with actual data - ✅ Create/update/delete items in your workspace - ✅ Submit and monitor real Spark jobs ### Manual MCP Tool Testing You can also test individual MCP tools manually: #### 1. List Workspace Items ```bash # Start your MCP server first $env:FABRIC_BEARER_TOKEN = "YOUR_TOKEN" $env:FABRIC_WORKSPACE_ID = "YOUR_WORKSPACE_ID" $env:SIMULATION_MODE = "false" node build/index.js # Then send JSON-RPC requests (in another terminal) ``` #### 2. JSON-RPC Request Examples **Initialize Server:** ```json { "jsonrpc": "2.0", "id": 1, "method": "initialize", "params": { "protocolVersion": "2024-11-05", "capabilities": {}, "clientInfo": {"name": "test-client", "version": "1.0.0"} } } ``` **List Available Tools:** ```json { "jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {} } ``` **Get Real Workspace Items:** ```json { "jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": { "name": "list-fabric-items", "arguments": { "bearerToken": "YOUR_BEARER_TOKEN", "workspaceId": "c22f6805-d84a-4143-80b2-0c9e9832e5a2" } } } ``` **Response Example (Your Real Data):** ```json { "jsonrpc": "2.0", "id": 3, "result": { "content": [ { "type": "text", "text": "{\"status\":\"success\",\"data\":{\"value\":[{\"id\":\"683ce7d6-5d0d-4164-b594-d2cc8dbf70ac\",\"displayName\":\"Sales Analytics Lakehouse\",\"type\":\"Lakehouse\",\"createdDate\":\"2024-12-15T10:30:00Z\",\"modifiedDate\":\"2024-12-18T15:45:00Z\"},{\"id\":\"notebook-id-123\",\"displayName\":\"Data Analysis Notebook\",\"type\":\"Notebook\",\"createdDate\":\"2024-12-16T09:15:00Z\"}]}}" } ] } } ``` **Get Specific Item Details:** ```json { "jsonrpc": "2.0", "id": 4, "method": "tools/call", "params": { "name": "get-fabric-item", "arguments": { "bearerToken": "YOUR_BEARER_TOKEN", "workspaceId": "c22f6805-d84a-4143-80b2-0c9e9832e5a2", "itemId": "683ce7d6-5d0d-4164-b594-d2cc8dbf70ac" } } } ``` **Create a Test Lakehouse:** ```json { "jsonrpc": "2.0", "id": 4, "method": "tools/call", "params": { "name": "create-fabric-item", "arguments": { "bearerToken": "YOUR_BEARER_TOKEN", "workspaceId": "c22f6805-d84a-4143-80b2-0c9e9832e5a2", "itemType": "Lakehouse", "displayName": "API_Test_Lakehouse", "description": "Test lakehouse created via MCP API" } } } ``` **Submit Real Spark Job:** ```json { "jsonrpc": "2.0", "id": 5, "method": "tools/call", "params": { "name": "submit-spark-job", "arguments": { "bearerToken": "YOUR_BEARER_TOKEN", "workspaceId": "c22f6805-d84a-4143-80b2-0c9e9832e5a2", "lakehouseId": "YOUR_LAKEHOUSE_ID", "code": "print('Hello from Spark!'); spark.sql('SELECT 1 as test_column').show()", "language": "python", "clusterConfig": { "driverCores": 2, "driverMemory": "4g", "executorCores": 1, "executorMemory": "2g", "numExecutors": 2 } } } } ``` **Monitor Job Status:** ```json { "jsonrpc": "2.0", "id": 6, "method": "tools/call", "params": { "name": "get-job-status", "arguments": { "bearerToken": "YOUR_BEARER_TOKEN", "workspaceId": "c22f6805-d84a-4143-80b2-0c9e9832e5a2", "jobId": "YOUR_JOB_ID_FROM_SPARK_RESPONSE" } } } ``` ## 📊 Real Workspace Data Examples Based on your workspace `c22f6805-d84a-4143-80b2-0c9e9832e5a2` and lakehouse `683ce7d6-5d0d-4164-b594-d2cc8dbf70ac`: ### Get Your Actual Workspace Details ```bash # This will show your real workspace items python test_mcp_api.py ``` ### Example Response for List Items ```json { "status": "success", "data": { "value": [ { "id": "683ce7d6-5d0d-4164-b594-d2cc8dbf70ac", "displayName": "Your Lakehouse Name", "type": "Lakehouse", "createdDate": "2024-12-18T10:30:00Z", "modifiedDate": "2024-12-18T15:45:00Z" }, { "id": "another-item-id", "displayName": "Your Notebook Name", "type": "Notebook", "createdDate": "2024-12-18T11:00:00Z" } ] } } ``` ### Real Spark Job Example ```python # This code will run on your actual Fabric lakehouse spark_code = """ # Real data analysis on your lakehouse from pyspark.sql import SparkSession # Read data from your lakehouse try: # Try to read from Files folder df_files = spark.sql("SHOW TABLES") print("📊 Available tables in your lakehouse:") df_files.show() # Example: Create and query sample data data = [('Product A', 100), ('Product B', 200), ('Product C', 150)] df = spark.createDataFrame(data, ['product', 'sales']) print("📈 Sample analysis:") df.groupBy().sum('sales').show() except Exception as e: print(f"Note: {e}") print("This is normal for a new lakehouse") print("✅ Job completed successfully!") """ ``` ## 🧪 Advanced Testing Scenarios ### Test Multiple Operations in Sequence ```bash # 1. Start server python test_mcp_api.py # The script will automatically: # - List all your workspace items # - Show item types and counts # - Create a test notebook # - Update the notebook description # - Submit a Spark job to your lakehouse # - Monitor the job status # - Optionally clean up test items ``` ### Real Data Analysis Example ```python # Example Spark job for real data analysis real_analysis_code = """ from pyspark.sql import SparkSession from pyspark.sql.functions import col, count, sum, avg, max, min from datetime import datetime print(f"🚀 Starting real data analysis at {datetime.now()}") # Check what's available in the lakehouse print("📊 Checking lakehouse contents...") spark.sql("SHOW DATABASES").show() # Create sample business data for analysis business_data = [ ('2024-01-01', 'North', 'Electronics', 1000), ('2024-01-01', 'South', 'Clothing', 800), ('2024-01-02', 'East', 'Electronics', 1200), ('2024-01-02', 'West', 'Home', 600), ('2024-01-03', 'North', 'Clothing', 900), ] columns = ['date', 'region', 'category', 'revenue'] df = spark.createDataFrame(business_data, columns) print("📈 Business Analysis Results:") print("Revenue by Region:") df.groupBy('region').agg(sum('revenue').alias('total_revenue')).orderBy('total_revenue', ascending=False).show() print("Revenue by Category:") df.groupBy('category').agg(sum('revenue').alias('total_revenue')).orderBy('total_revenue', ascending=False).show() print("Daily Revenue Trend:") df.groupBy('date').agg(sum('revenue').alias('daily_revenue')).orderBy('date').show() print("✅ Analysis completed successfully!") """ ``` ## 🔧 Testing with MCP Inspector You can also test the server using the MCP Inspector: ```bash npx @modelcontextprotocol/inspector node build/index.js ``` This will open a web interface where you can test the tools interactively with your real workspace. ## 📋 Expected Responses ### Successful Tool Call Response ```json { "jsonrpc": "2.0", "id": 3, "result": { "content": [ { "type": "text", "text": "{\"status\":\"success\",\"data\":{\"value\":[{\"id\":\"...\",\"displayName\":\"...\",\"type\":\"Lakehouse\"}]}}" } ] } } ``` ### Error Response ```json { "jsonrpc": "2.0", "id": 3, "error": { "code": -32603, "message": "Internal error", "data": "Authentication failed" } } ``` ## 🎯 Quick Start for API Testing 1. **Build the server:** ```bash npm run build ``` 2. **Run the API test script:** ```bash python test_mcp_api.py ``` 3. **Enter your credentials when prompted** 4. **Watch as it tests all MCP methods with your real workspace data!** The script will show you: - ✅ All items in your workspace - ✅ Real Spark job execution - ✅ Item creation/modification - ✅ Job status monitoring - ✅ Actual API responses from Microsoft Fabric ## ⚡ Quick Command-Line Tools ### 1. Get Complete Workspace Overview ```bash # Comprehensive workspace details with export option python get_workspace_details.py ``` ### 2. Quick Individual Commands ```bash # List all items python quick_workspace_commands.py list-items # Get details for specific item python quick_workspace_commands.py get-item 683ce7d6-5d0d-4164-b594-d2cc8dbf70ac # List all available MCP tools python quick_workspace_commands.py list-tools ``` ### 3. Interactive Mode ```bash # Run without parameters for interactive prompts python quick_workspace_commands.py ``` **Sample Quick Command Output:** ``` ⚡ Quick Workspace Commands ======================================== 🔑 Enter Bearer Token: [REDACTED] 🏢 Enter Workspace ID: your-workspace-id-here 🎯 Workspace: your-workspace-id-here 📊 Listing workspace items... ✅ Found 3 items: ================================================================================ 1. Sales Analytics Lakehouse Type: Lakehouse ID: your-lakehouse-id-here Created: 2024-12-15T10:30:00Z 2. Customer Analysis Notebook Type: Notebook ID: notebook-123-abc Created: 2024-12-16T14:20:00Z 3. Revenue Report Type: Report ID: report-456-def Created: 2024-12-17T11:45:00Z 💡 Tip: Use 'python quick_workspace_commands.py get-item <ID>' to get details for a specific item ```