
A comprehensive Python-based MCP (Model Context Protocol) server for interacting with Microsoft Fabric APIs, featuring advanced PySpark notebook development, testing, and optimization capabilities...
概览
A comprehensive Python-based MCP (Model Context Protocol) server for interacting with Microsoft Fabric APIs, featuring advanced PySpark notebook development, testing, and optimization capabilities with LLM integration. - ✅ Workspace, lakehouse, warehouse, and table management - ✅ Delta table schemas and metadata retrieval - ✅ SQL query execution and data loading - ✅ Report and semantic model operations - 📓 with 6 specialized templates - 🔧 for common PySpark operations - ✅ with syntax and best practices checking - 🎯 and compatibility checks - 📊 with scoring and optimization recommendations - 🚀 and execution insights - 🤖 for PySpark development - 🧠 with conversation memory - 🎨 and explanations - 📈 based on project patterns - - for authentication - (from astral): Installation instructions - : Installation instructions - for MCP inspector: Installation instructions This starts the server with inspector at http://localhost:6274.
README
以下为原文 README 节选,完整内容请查看原文。
Microsoft Fabric MCP Server
A comprehensive Python-based MCP (Model Context Protocol) server for interacting with Microsoft Fabric APIs, featuring advanced PySpark notebook development, testing, and optimization capabilities with LLM integration.
🚀 Features
Core Fabric Operations
- ✅ Workspace, lakehouse, warehouse, and table management
- ✅ Delta table schemas and metadata retrieval
- ✅ SQL query execution and data loading
- ✅ Report and semantic model operations
Advanced PySpark Development
- 📓 Intelligent notebook creation with 6 specialized templates
- 🔧 Smart code generation for common PySpark operations
- ✅ Comprehensive validation with syntax and best practices checking
- 🎯 Fabric-specific optimizations and compatibility checks
- 📊 Performance analysis with scoring and optimization recommendations
- 🚀 Real-time monitoring and execution insights
LLM Integration
- 🤖 Natural language interface for PySpark development
- 🧠 Context-aware assistance with conversation memory
- 🎨 Intelligent code formatting and explanations
- 📈 Smart optimization suggestions based on project patterns
🏗️ Architecture
graph TB
subgraph "Developer Environment"
IDE[IDE/VSCode]
DEV[Developer]
PROJ[Project Files]
end
subgraph "AI Layer"
LLM[Large Language ModelClaude/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
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手?先看 mcpskill.org 教程 →