Fabric MCP

# Microsoft Fabric MCP Architecture with LLM Integration ## Complete Architecture Diagram ```mermaid graph TB subgraph "Developer Environment" IDE[IDE/VSCode] DEV[Developer] PROJ[Project Files] end subgraph "AI Layer" LLM[Large Language Model<br/>Claude/GPT/etc.] CONTEXT[Conversation Context] REASONING[AI Reasoning Engine] end subgraph "MCP Layer" MCP[MCP Server] TOOLS[PySpark Tools] HELPERS[PySpark Helpers] TEMPLATES[Template Manager] VALIDATORS[Code Validators] GENERATORS[Code Generators] end subgraph "Microsoft Fabric" API[Fabric API] WS[Workspace] LH[Lakehouse] NB[Notebooks] TABLES[Delta Tables] SPARK[Spark Clusters] end subgraph "Operations Flow" CREATE[Create Notebooks] VALIDATE[Validate Code] GENERATE[Generate Code] ANALYZE[Analyze Performance] DEPLOY[Deploy to Fabric] end %% Developer interactions DEV --> IDE IDE --> PROJ %% LLM interactions IDE <--> LLM LLM <--> CONTEXT LLM --> REASONING %% MCP interactions LLM <--> MCP MCP --> TOOLS TOOLS --> HELPERS TOOLS --> TEMPLATES TOOLS --> VALIDATORS TOOLS --> GENERATORS %% Fabric interactions MCP <--> API API --> WS WS --> LH WS --> NB LH --> TABLES NB --> SPARK %% Operation flows TOOLS --> CREATE TOOLS --> VALIDATE TOOLS --> GENERATE TOOLS --> ANALYZE CREATE --> DEPLOY %% Data flow arrows REASONING -.->|"Intelligent Decisions"| TOOLS CONTEXT -.->|"Project Awareness"| VALIDATORS %% Styling classDef devEnv fill:#e1f5fe classDef aiLayer fill:#fff9c4 classDef mcpLayer fill:#f3e5f5 classDef fabricLayer fill:#e8f5e8 classDef operations fill:#fff3e0 class IDE,DEV,PROJ devEnv class LLM,CONTEXT,REASONING aiLayer class MCP,TOOLS,HELPERS,TEMPLATES,VALIDATORS,GENERATORS mcpLayer class API,WS,LH,NB,TABLES,SPARK fabricLayer class CREATE,VALIDATE,GENERATE,ANALYZE,DEPLOY operations ``` ## Architecture Components ### **1. Developer Environment** - **IDE/VSCode**: Primary development interface with MCP integration - **Developer**: Data engineer/scientist working on PySpark projects - **Project Files**: Local project structure and configuration ### **2. AI Layer** - **Large Language Model**: Claude, GPT, or other LLM providing intelligent assistance - **Conversation Context**: Maintains project context and conversation history - **AI Reasoning Engine**: Makes intelligent decisions about code generation and optimization ### **3. MCP Layer (This Server)** - **MCP Server**: Core server handling tool requests from the LLM - **PySpark Tools**: 11 specialized tools for notebook operations - **PySpark Helpers**: Template management and code generation - **Template Manager**: Pre-built notebook templates for different scenarios - **Code Validators**: Syntax, best practices, and Fabric compatibility checks - **Code Generators**: Intelligent PySpark code generation ### **4. Microsoft Fabric** - **Fabric API**: REST API for all Fabric operations - **Workspace**: Fabric workspace containing resources - **Lakehouse**: Data storage with Delta Lake tables - **Notebooks**: PySpark notebooks for data processing - **Delta Tables**: Structured data storage - **Spark Clusters**: Compute resources for PySpark execution ### **5. Operations Flow** - **Create Notebooks**: Generate notebooks from templates - **Validate Code**: Check syntax, performance, and compatibility - **Generate Code**: Create PySpark snippets for common operations - **Analyze Performance**: Evaluate and optimize notebook performance - **Deploy to Fabric**: Push notebooks and execute in Fabric environment ## Enhanced Interaction Flow with LLM 1. **Developer requests PySpark assistance in IDE** 2. **IDE communicates with LLM (Claude/GPT)** 3. **LLM analyzes request using conversation context and reasoning** 4. **LLM calls MCP server tools based on intelligent analysis** 5. **MCP server processes request using specialized tools** 6. **Tools utilize helpers, templates, and validators** 7. **MCP server calls Fabric API for operations** 8. **Results flow back through MCP to LLM** 9. **LLM processes and formats results intelligently** 10. **Developer receives contextual, intelligent responses in IDE** ## Key Benefits of LLM Integration ### **Intelligent Decision Making** - LLM analyzes developer intent and context - Chooses appropriate tools and templates automatically - Provides contextual recommendations based on project history ### **Natural Language Interface** - Developers can request features in natural language - LLM translates requests to appropriate MCP tool calls - Reduces need to remember specific tool names and parameters ### **Context Awareness** - LLM maintains conversation history and project context - Provides consistent recommendations across sessions - Learns from previous interactions and code patterns ### **Enhanced Code Generation** - LLM combines multiple tool outputs intelligently - Provides explanations and documentation with generated code - Adapts to developer's coding style and preferences ## Example LLM-Enhanced Workflows ### **Scenario 1: Natural Language Request** ``` Developer: "Help me create a PySpark notebook that reads sales data from our lakehouse, cleans it, and creates a summary table with performance optimization." LLM Process: 1. Analyzes intent: notebook creation + data processing + optimization 2. Calls create_fabric_notebook() with ETL template 3. Calls generate_fabric_code() for lakehouse reading 4. Calls validate_fabric_code() for optimization checks 5. Provides complete solution with explanations ``` ### **Scenario 2: Performance Optimization** ``` Developer: "My PySpark notebook is running slowly. Can you help optimize it?" LLM Process: 1. Calls analyze_notebook_performance() on current notebook 2. Calls validate_fabric_code() for anti-pattern detection 3. Calls generate_fabric_code() for optimized alternatives 4. Provides detailed optimization report with before/after comparisons ``` ### **Scenario 3: Best Practices Guidance** ``` Developer: "Is this PySpark code following Fabric best practices?" LLM Process: 1. Calls validate_fabric_code() for compatibility checks 2. Analyzes results with reasoning engine 3. Provides detailed feedback with specific recommendations 4. Suggests alternative approaches using generate_fabric_code() ``` This architecture leverages the power of LLMs to provide intelligent, context-aware assistance while utilizing specialized MCP tools for precise Fabric operations!