chore: import upstream snapshot with attribution
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# Streamlining AI Workflows: Building an MCP Server with Microsoft Foundry Toolkit
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[](https://spec.modelcontextprotocol.io/specification/2025-11-25/)
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[](https://python.org)
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[](https://code.visualstudio.com/)
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## 🎯 Overview
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[](https://youtu.be/r34Csn3rkeQ)
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_(Click the image above to view video of this lesson)_
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Welcome to the **Model Context Protocol (MCP) Workshop**! This comprehensive hands-on workshop combines two cutting-edge technologies to revolutionize AI application development:
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- **🔗 Model Context Protocol (MCP)**: An open standard for seamless AI-tool integration
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- **🛠️ Microsoft Foundry Toolkit Extension for VS Code**: Microsoft's powerful AI development extension
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### 🎓 What You'll Learn
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By the end of this workshop, you'll master the art of building intelligent applications that bridge AI models with real-world tools and services. From automated testing to custom API integrations, you'll gain practical skills to solve complex business challenges.
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## 🏗️ Technology Stack
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### 🔌 Model Context Protocol (MCP)
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MCP is the **"USB-C for AI"** - a universal standard that connects AI models to external tools and data sources.
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**✨ Key Features:**
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- 🔄 **Standardized Integration**: Universal interface for AI-tool connections
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- 🏛️ **Flexible Architecture**: Local & remote servers via stdio/SSE transport
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- 🧰 **Rich Ecosystem**: Tools, prompts, and resources in one protocol
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- 🔒 **Enterprise-Ready**: Built-in security and reliability
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**🎯 Why MCP Matters:**
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Just like USB-C eliminated cable chaos, MCP eliminates the complexity of AI integrations. One protocol, infinite possibilities.
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### 🤖 Microsoft Foundry Toolkit Extension for VS Code
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Microsoft's flagship AI development extension that transforms VS Code into an AI powerhouse.
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**🚀 Core Capabilities:**
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- 📦 **Model Catalog**: Access models from Azure AI, GitHub, Hugging Face, Ollama
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- ⚡ **Local Inference**: ONNX-optimized CPU/GPU/NPU execution
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- 🏗️ **Agent Builder**: Visual AI agent development with MCP integration
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- 🎭 **Multi-Modal**: Text, vision, and structured output support
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**💡 Development Benefits:**
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- Zero-config model deployment
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- Visual prompt engineering
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- Real-time testing playground
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- Seamless MCP server integration
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## 📚 Learning Journey
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### [🚀 Module 1: Microsoft Foundry Toolkit Fundamentals](./lab1/README.md)
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**Duration**: 15 minutes
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- 🛠️ Install and configure Microsoft Foundry Toolkit for VS Code
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- 🗂️ Explore the Model Catalog (100+ models from GitHub, ONNX, OpenAI, Anthropic, Google)
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- 🎮 Master the Interactive Playground for real-time model testing
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- 🤖 Build your first AI agent with Agent Builder
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- 📊 Evaluate model performance with built-in metrics (F1, relevance, similarity, coherence)
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- ⚡ Learn batch processing and multi-modal support capabilities
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**🎯 Learning Outcome**: Create a functional AI agent with comprehensive understanding of Microsoft Foundry Toolkit capabilities
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### [🌐 Module 2: MCP with Microsoft Foundry Toolkit Fundamentals](./lab2/README.md)
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**Duration**: 20 minutes
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- 🧠 Master Model Context Protocol (MCP) architecture and concepts
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- 🌐 Explore Microsoft's MCP server ecosystem
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- 🤖 Build a browser automation agent using Playwright MCP server
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- 🔧 Integrate MCP servers with Microsoft Foundry Toolkit Agent Builder
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- 📊 Configure and test MCP tools within your agents
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- 🚀 Export and deploy MCP-powered agents for production use
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**🎯 Learning Outcome**: Deploy an AI agent supercharged with external tools through MCP
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### [🔧 Module 3: Advanced MCP Development with Microsoft Foundry Toolkit](./lab3/README.md)
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**Duration**: 20 minutes
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- 💻 Create custom MCP servers using Microsoft Foundry Toolkit
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- 🐍 Configure and use the latest MCP Python SDK (v1.9.3)
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- 🔍 Set up and utilize MCP Inspector for debugging
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- 🛠️ Build a Weather MCP Server with professional debugging workflows
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- 🧪 Debug MCP servers in both Agent Builder and Inspector environments
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**🎯 Learning Outcome**: Develop and debug custom MCP servers with modern tooling
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### [🐙 Module 4: Practical MCP Development - Custom GitHub Clone Server](./lab4/README.md)
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**Duration**: 30 minutes
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- 🏗️ Build a real-world GitHub Clone MCP Server for development workflows
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- 🔄 Implement smart repository cloning with validation and error handling
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- 📁 Create intelligent directory management and VS Code integration
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- 🤖 Use GitHub Copilot Agent Mode with custom MCP tools
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- 🛡️ Apply production-ready reliability and cross-platform compatibility
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**🎯 Learning Outcome**: Deploy a production-ready MCP server that streamlines real development workflows
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## 💡 Real-World Applications & Impact
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### 🏢 Enterprise Use Cases
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#### 🔄 DevOps Automation
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Transform your development workflow with intelligent automation:
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- **Smart Repository Management**: AI-driven code review and merge decisions
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- **Intelligent CI/CD**: Automated pipeline optimization based on code changes
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- **Issue Triage**: Automatic bug classification and assignment
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#### 🧪 Quality Assurance Revolution
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Elevate testing with AI-powered automation:
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- **Intelligent Test Generation**: Create comprehensive test suites automatically
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- **Visual Regression Testing**: AI-powered UI change detection
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- **Performance Monitoring**: Proactive issue identification and resolution
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#### 📊 Data Pipeline Intelligence
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Build smarter data processing workflows:
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- **Adaptive ETL Processes**: Self-optimizing data transformations
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- **Anomaly Detection**: Real-time data quality monitoring
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- **Intelligent Routing**: Smart data flow management
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#### 🎧 Customer Experience Enhancement
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Create exceptional customer interactions:
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- **Context-Aware Support**: AI agents with access to customer history
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- **Proactive Issue Resolution**: Predictive customer service
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- **Multi-Channel Integration**: Unified AI experience across platforms
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## 🛠️ Prerequisites & Setup
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### 💻 System Requirements
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| Component | Requirement | Notes |
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|-----------|-------------|-------|
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| **Operating System** | Windows 10+, macOS 10.15+, Linux | Any modern OS |
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| **Visual Studio Code** | Latest stable version | Required for Microsoft Foundry Toolkit |
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| **Node.js** | v18.0+ and npm | For MCP server development |
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| **Python** | 3.10+ | Optional for Python MCP servers |
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| **Memory** | 8GB RAM minimum | 16GB recommended for local models |
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### 🔧 Development Environment
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#### Recommended VS Code Extensions
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- **Microsoft Foundry Toolkit** (ms-windows-ai-studio.windows-ai-studio)
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- **Python** (ms-python.python)
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- **Python Debugger** (ms-python.debugpy)
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- **GitHub Copilot** (GitHub.copilot) - Optional but helpful
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#### Optional Tools
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- **uv**: Modern Python package manager
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- **MCP Inspector**: Visual debugging tool for MCP servers
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- **Playwright**: For web automation examples
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## 🎖️ Learning Outcomes & Certification Path
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### 🏆 Skill Mastery Checklist
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By completing this workshop, you will achieve mastery in:
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#### 🎯 Core Competencies
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- [ ] **MCP Protocol Mastery**: Deep understanding of architecture and implementation patterns
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- [ ] **Microsoft Foundry Toolkit Proficiency**: Expert-level usage of Microsoft Foundry Toolkit for rapid development
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- [ ] **Custom Server Development**: Build, deploy, and maintain production MCP servers
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- [ ] **Tool Integration Excellence**: Seamlessly connect AI with existing development workflows
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- [ ] **Problem-Solving Application**: Apply learned skills to real business challenges
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#### 🔧 Technical Skills
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- [ ] Set up and configure Microsoft Foundry Toolkit in VS Code
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- [ ] Design and implement custom MCP servers
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- [ ] Integrate GitHub Models with MCP architecture
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- [ ] Build automated testing workflows with Playwright
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- [ ] Deploy AI agents for production use
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- [ ] Debug and optimize MCP server performance
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#### 🚀 Advanced Capabilities
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- [ ] Architect enterprise-scale AI integrations
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- [ ] Implement security best practices for AI applications
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- [ ] Design scalable MCP server architectures
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- [ ] Create custom tool chains for specific domains
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- [ ] Mentor others in AI-native development
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## 📖 Additional Resources
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- [MCP Specification (2025-11-25)](https://spec.modelcontextprotocol.io/specification/2025-11-25/)
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- [Microsoft Foundry Toolkit GitHub Repository](https://github.com/microsoft/vscode-ai-toolkit)
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- [Sample MCP Servers Collection](https://github.com/modelcontextprotocol/servers)
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- [Best Practices Guide](https://modelcontextprotocol.io/docs/best-practices)
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- [OWASP MCP Top 10](https://microsoft.github.io/mcp-azure-security-guide/mcp/) - Security best practices
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---
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**🚀 Ready to revolutionize your AI development workflow?**
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Let's build the future of intelligent applications together with MCP and Microsoft Foundry Toolkit!
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## What's Next
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Continue to: [Module 11: MCP Server Hands-On Labs](../11-MCPServerHandsOnLabs/README.md)
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@@ -0,0 +1,273 @@
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# 🚀 Module 1: Microsoft Foundry Toolkit Fundamentals
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[]()
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[]()
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[]()
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## 📋 Learning Objectives
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By the end of this module, you will be able to:
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- ✅ Install and configure Microsoft Foundry Toolkit Extension for VS Code
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- ✅ Navigate the Model Catalog and understand different model sources
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- ✅ Use the Playground for model testing and experimentation
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- ✅ Create custom AI agents using Agent Builder
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- ✅ Compare model performance across different providers
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- ✅ Apply best practices for prompt engineering
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## 🧠 Introduction to Microsoft Foundry Toolkit
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The **Microsoft Foundry Toolkit Extension for VS Code** is Microsoft's flagship extension that transforms VS Code into a comprehensive AI development environment. It bridges the gap between AI research and practical application development, making generative AI accessible to developers of all skill levels.
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### 🌟 Key Capabilities
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| Feature | Description | Use Case |
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|---------|-------------|----------|
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| **🗂️ Model Catalog** | Access 100+ models from GitHub, ONNX, OpenAI, Anthropic, Google | Model discovery and selection |
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| **🔌 BYOM Support** | Integrate your own models (local/remote) | Custom model deployment |
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| **🎮 Interactive Playground** | Real-time model testing with chat interface | Rapid prototyping and testing |
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| **📎 Multi-Modal Support** | Handle text, images, and attachments | Complex AI applications |
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| **⚡ Batch Processing** | Run multiple prompts simultaneously | Efficient testing workflows |
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| **📊 Model Evaluation** | Built-in metrics (F1, relevance, similarity, coherence) | Performance assessment |
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### 🎯 Why Microsoft Foundry Toolkit Matters
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- **🚀 Accelerated Development**: From idea to prototype in minutes
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- **🔄 Unified Workflow**: One interface for multiple AI providers
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- **🧪 Easy Experimentation**: Compare models without complex setup
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- **📈 Production Ready**: Seamless transition from prototype to deployment
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## 🛠️ Prerequisites & Setup
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### 📦 Install Microsoft Foundry Toolkit Extension
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**Step 1: Access Extensions Marketplace**
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1. Open Visual Studio Code
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2. Navigate to the Extensions view (`Ctrl+Shift+X` or `Cmd+Shift+X`)
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3. Search for "Microsoft Foundry Toolkit"
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**Step 2: Choose Your Version**
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- **🟢 Release**: Recommended for production use
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- **🔶 Pre-release**: Early access to cutting-edge features
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**Step 3: Install and Activate**
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### ✅ Verification Checklist
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- [ ] Microsoft Foundry Toolkit icon appears in the VS Code sidebar
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- [ ] Extension is enabled and activated
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- [ ] No installation errors in the output panel
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## 🧪 Hands-on Exercise 1: Exploring GitHub Models
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**🎯 Objective**: Master the Model Catalog and test your first AI model
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### 📊 Step 1: Navigate the Model Catalog
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|
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The Model Catalog is your gateway to the AI ecosystem. It aggregates models from multiple providers, making it easy to discover and compare options.
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**🔍 Navigation Guide:**
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Click on **MODELS - Catalog** in the Microsoft Foundry Toolkit sidebar
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**💡 Pro Tip**: Look for models with specific capabilities that match your use case (e.g., code generation, creative writing, analysis).
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**⚠️ Note**: GitHub-hosted models (i.e. GitHub Models) are free to use but are subject to rate limits on requests and tokens. If you want to access non-GitHub models (that is, external models hosted via Azure AI or other endpoints), you'll need to supply the appropriate API key or authentication.
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### 🚀 Step 2: Add and Configure Your First Model
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**Model Selection Strategy:**
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- **GPT-4.1**: Best for complex reasoning and analysis
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- **Phi-4-mini**: Lightweight, fast responses for simple tasks
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**🔧 Configuration Process:**
|
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1. Select **OpenAI GPT-4.1** from the catalog
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2. Click **Add to My Models** - this registers the model for use
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3. Choose **Try in Playground** to launch the testing environment
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4. Wait for model initialization (first-time setup may take a moment)
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|
||||
|
||||
|
||||
**⚙️ Understanding Model Parameters:**
|
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- **Temperature**: Controls creativity (0 = deterministic, 1 = creative)
|
||||
- **Max Tokens**: Maximum response length
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- **Top-p**: Nucleus sampling for response diversity
|
||||
|
||||
### 🎯 Step 3: Master the Playground Interface
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|
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The Playground is your AI experimentation lab. Here's how to maximize its potential:
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|
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**🎨 Prompt Engineering Best Practices:**
|
||||
1. **Be Specific**: Clear, detailed instructions yield better results
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2. **Provide Context**: Include relevant background information
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3. **Use Examples**: Show the model what you want with examples
|
||||
4. **Iterate**: Refine prompts based on initial results
|
||||
|
||||
**🧪 Testing Scenarios:**
|
||||
```markdown
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||||
# Example 1: Code Generation
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"Write a Python function that calculates the factorial of a number using recursion. Include error handling and docstrings."
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||||
|
||||
# Example 2: Creative Writing
|
||||
"Write a professional email to a client explaining a project delay, maintaining a positive tone while being transparent about challenges."
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||||
|
||||
# Example 3: Data Analysis
|
||||
"Analyze this sales data and provide insights: [paste your data]. Focus on trends, anomalies, and actionable recommendations."
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```
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|
||||
|
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|
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### 🏆 Challenge Exercise: Model Performance Comparison
|
||||
|
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**🎯 Goal**: Compare different models using identical prompts to understand their strengths
|
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|
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**📋 Instructions:**
|
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1. Add **Phi-4-mini** to your workspace
|
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2. Use the same prompt for both GPT-4.1 and Phi-4-mini
|
||||
|
||||
|
||||
|
||||
3. Compare response quality, speed, and accuracy
|
||||
4. Document your findings in the results section
|
||||
|
||||
|
||||
|
||||
**💡 Key Insights to Discover:**
|
||||
- When to use LLM vs SLM
|
||||
- Cost vs. performance trade-offs
|
||||
- Specialized capabilities of different models
|
||||
|
||||
## 🤖 Hands-on Exercise 2: Building Custom Agents with Agent Builder
|
||||
|
||||
**🎯 Objective**: Create specialized AI agents tailored for specific tasks and workflows
|
||||
|
||||
### 🏗️ Step 1: Understanding Agent Builder
|
||||
|
||||
Agent Builder is where Microsoft Foundry Toolkit truly shines. It allows you to create purpose-built AI assistants that combine the power of large language models with custom instructions, specific parameters, and specialized knowledge.
|
||||
|
||||
**🧠 Agent Architecture Components:**
|
||||
- **Core Model**: The foundation LLM (GPT-4, Groks, Phi, etc.)
|
||||
- **System Prompt**: Defines agent personality and behavior
|
||||
- **Parameters**: Fine-tuned settings for optimal performance
|
||||
- **Tools Integration**: Connect to external APIs and MCP services
|
||||
- **Memory**: Conversation context and session persistence
|
||||
|
||||
|
||||
|
||||
### ⚙️ Step 2: Agent Configuration Deep Dive
|
||||
|
||||
**🎨 Creating Effective System Prompts:**
|
||||
```markdown
|
||||
# Template Structure:
|
||||
## Role Definition
|
||||
You are a [specific role] with expertise in [domain].
|
||||
|
||||
## Capabilities
|
||||
- List specific abilities
|
||||
- Define scope of knowledge
|
||||
- Clarify limitations
|
||||
|
||||
## Behavior Guidelines
|
||||
- Response style (formal, casual, technical)
|
||||
- Output format preferences
|
||||
- Error handling approach
|
||||
|
||||
## Examples
|
||||
Provide 2-3 examples of ideal interactions
|
||||
```
|
||||
|
||||
*Of course, you can also use Generate System Prompt to use AI to help you generate and optimize prompts*
|
||||
|
||||
**🔧 Parameter Optimization:**
|
||||
| Parameter | Recommended Range | Use Case |
|
||||
|-----------|------------------|----------|
|
||||
| **Temperature** | 0.1-0.3 | Technical/factual responses |
|
||||
| **Temperature** | 0.7-0.9 | Creative/brainstorming tasks |
|
||||
| **Max Tokens** | 500-1000 | Concise responses |
|
||||
| **Max Tokens** | 2000-4000 | Detailed explanations |
|
||||
|
||||
### 🐍 Step 3: Practical Exercise - Python Programming Agent
|
||||
|
||||
**🎯 Mission**: Create a specialized Python coding assistant
|
||||
|
||||
**📋 Configuration Steps:**
|
||||
|
||||
1. **Model Selection**: Choose **Claude 3.5 Sonnet** (excellent for code)
|
||||
|
||||
2. **System Prompt Design**:
|
||||
```markdown
|
||||
# Python Programming Expert Agent
|
||||
|
||||
## Role
|
||||
You are a senior Python developer with 10+ years of experience. You excel at writing clean, efficient, and well-documented Python code.
|
||||
|
||||
## Capabilities
|
||||
- Write production-ready Python code
|
||||
- Debug complex issues
|
||||
- Explain code concepts clearly
|
||||
- Suggest best practices and optimizations
|
||||
- Provide complete working examples
|
||||
|
||||
## Response Format
|
||||
- Always include docstrings
|
||||
- Add inline comments for complex logic
|
||||
- Suggest testing approaches
|
||||
- Mention relevant libraries when applicable
|
||||
|
||||
## Code Quality Standards
|
||||
- Follow PEP 8 style guidelines
|
||||
- Use type hints where appropriate
|
||||
- Handle exceptions gracefully
|
||||
- Write readable, maintainable code
|
||||
```
|
||||
|
||||
3. **Parameter Configuration**:
|
||||
- Temperature: 0.2 (for consistent, reliable code)
|
||||
- Max Tokens: 2000 (detailed explanations)
|
||||
- Top-p: 0.9 (balanced creativity)
|
||||
|
||||
|
||||
|
||||
### 🧪 Step 4: Testing Your Python Agent
|
||||
|
||||
**Test Scenarios:**
|
||||
1. **Basic Function**: "Create a function to find prime numbers"
|
||||
2. **Complex Algorithm**: "Implement a binary search tree with insert, delete, and search methods"
|
||||
3. **Real-world Problem**: "Build a web scraper that handles rate limiting and retries"
|
||||
4. **Debugging**: "Fix this code [paste buggy code]"
|
||||
|
||||
**🏆 Success Criteria:**
|
||||
- ✅ Code runs without errors
|
||||
- ✅ Includes proper documentation
|
||||
- ✅ Follows Python best practices
|
||||
- ✅ Provides clear explanations
|
||||
- ✅ Suggests improvements
|
||||
|
||||
## 🎓 Module 1 Wrap-Up & Next Steps
|
||||
|
||||
### 📊 Knowledge Check
|
||||
|
||||
Test your understanding:
|
||||
- [ ] Can you explain the difference between models in the catalog?
|
||||
- [ ] Have you successfully created and tested a custom agent?
|
||||
- [ ] Do you understand how to optimize parameters for different use cases?
|
||||
- [ ] Can you design effective system prompts?
|
||||
|
||||
### 📚 Additional Resources
|
||||
|
||||
- **Microsoft Foundry Toolkit Documentation**: [Official Microsoft Docs](https://github.com/microsoft/vscode-ai-toolkit)
|
||||
- **Prompt Engineering Guide**: [Best Practices](https://platform.openai.com/docs/guides/prompt-engineering)
|
||||
- **Models in Microsoft Foundry Toolkit**: [Models in Develpment](https://github.com/microsoft/vscode-ai-toolkit/blob/main/doc/models.md)
|
||||
|
||||
**🎉 Congratulations!** You've mastered the fundamentals of Microsoft Foundry Toolkit and are ready to build more advanced AI applications!
|
||||
|
||||
### 🔜 Continue to Next Module
|
||||
|
||||
Ready for more advanced capabilities? Continue to **[Module 2: MCP with Microsoft Foundry Toolkit Fundamentals](../lab2/README.md)** where you'll learn how to:
|
||||
- Connect your agents to external tools using Model Context Protocol (MCP)
|
||||
- Build browser automation agents with Playwright
|
||||
- Integrate MCP servers with your Microsoft Foundry Toolkit agents
|
||||
- Supercharge your agents with external data and capabilities
|
||||
|
||||
@@ -0,0 +1,371 @@
|
||||
# 🌐 Module 2: MCP with Microsoft Foundry Toolkit Fundamentals
|
||||
|
||||
[]()
|
||||
[]()
|
||||
[]()
|
||||
|
||||
## 📋 Learning Objectives
|
||||
|
||||
By the end of this module, you will be able to:
|
||||
- ✅ Understand Model Context Protocol (MCP) architecture and benefits
|
||||
- ✅ Explore Microsoft's MCP server ecosystem
|
||||
- ✅ Integrate MCP servers with Microsoft Foundry Toolkit Agent Builder
|
||||
- ✅ Build a functional browser automation agent using Playwright MCP
|
||||
- ✅ Configure and test MCP tools within your agents
|
||||
- ✅ Export and deploy MCP-powered agents for production use
|
||||
|
||||
## 🎯 Building on Module 1
|
||||
|
||||
In Module 1, we mastered Microsoft Foundry Toolkit basics and created our first Python Agent. Now we'll **supercharge** your agents by connecting them to external tools and services through the revolutionary **Model Context Protocol (MCP)**.
|
||||
|
||||
Think of this as upgrading from a basic calculator to a full computer - your AI agents will gain the ability to:
|
||||
- 🌐 Browse and interact with websites
|
||||
- 📁 Access and manipulate files
|
||||
- 🔧 Integrate with enterprise systems
|
||||
- 📊 Process real-time data from APIs
|
||||
|
||||
## 🧠 Understanding Model Context Protocol (MCP)
|
||||
|
||||
### 🔍 What is MCP?
|
||||
|
||||
Model Context Protocol (MCP) is the **"USB-C for AI applications"** - a revolutionary open standard that connects Large Language Models (LLMs) to external tools, data sources, and services. Just as USB-C eliminated cable chaos by providing one universal connector, MCP eliminates AI integration complexity with one standardized protocol.
|
||||
|
||||
### 🎯 The Problem MCP Solves
|
||||
|
||||
**Before MCP:**
|
||||
- 🔧 Custom integrations for every tool
|
||||
- 🔄 Vendor lock-in with proprietary solutions
|
||||
- 🔒 Security vulnerabilities from ad-hoc connections
|
||||
- ⏱️ Months of development for basic integrations
|
||||
|
||||
**With MCP:**
|
||||
- ⚡ Plug-and-play tool integration
|
||||
- 🔄 Vendor-agnostic architecture
|
||||
- 🛡️ Built-in security best practices
|
||||
- 🚀 Minutes to add new capabilities
|
||||
|
||||
### 🏗️ MCP Architecture Deep Dive
|
||||
|
||||
MCP follows a **client-server architecture** that creates a secure, scalable ecosystem:
|
||||
|
||||
```mermaid
|
||||
graph TB
|
||||
A[AI Application/Agent] --> B[MCP Client]
|
||||
B --> C[MCP Server 1: Files]
|
||||
B --> D[MCP Server 2: Web APIs]
|
||||
B --> E[MCP Server 3: Database]
|
||||
B --> F[MCP Server N: Custom Tools]
|
||||
|
||||
C --> G[Local File System]
|
||||
D --> H[External APIs]
|
||||
E --> I[Database Systems]
|
||||
F --> J[Enterprise Systems]
|
||||
```
|
||||
|
||||
**🔧 Core Components:**
|
||||
|
||||
| Component | Role | Examples |
|
||||
|-----------|------|----------|
|
||||
| **MCP Hosts** | Applications that consume MCP services | Claude Desktop, VS Code, Microsoft Foundry Toolkit |
|
||||
| **MCP Clients** | Protocol handlers (1:1 with servers) | Built into host applications |
|
||||
| **MCP Servers** | Expose capabilities via standard protocol | Playwright, Files, Azure, GitHub |
|
||||
| **Transport Layer** | Communication methods | stdio, HTTP, WebSockets |
|
||||
|
||||
|
||||
## 🏢 Microsoft's MCP Server Ecosystem
|
||||
|
||||
Microsoft leads the MCP ecosystem with a comprehensive suite of enterprise-grade servers that address real-world business needs.
|
||||
|
||||
### 🌟 Featured Microsoft MCP Servers
|
||||
|
||||
#### 1. ☁️ Azure MCP Server
|
||||
**🔗 Repository**: [azure/azure-mcp](https://github.com/azure/azure-mcp)
|
||||
**🎯 Purpose**: Comprehensive Azure resource management with AI integration
|
||||
|
||||
**✨ Key Features:**
|
||||
- Declarative infrastructure provisioning
|
||||
- Real-time resource monitoring
|
||||
- Cost optimization recommendations
|
||||
- Security compliance checking
|
||||
|
||||
**🚀 Use Cases:**
|
||||
- Infrastructure-as-Code with AI assistance
|
||||
- Automated resource scaling
|
||||
- Cloud cost optimization
|
||||
- DevOps workflow automation
|
||||
|
||||
#### 2. 📊 Microsoft Dataverse MCP
|
||||
**📚 Documentation**: [Microsoft Dataverse Integration](https://go.microsoft.com/fwlink/?linkid=2320176)
|
||||
**🎯 Purpose**: Natural language interface for business data
|
||||
|
||||
**✨ Key Features:**
|
||||
- Natural language database queries
|
||||
- Business context understanding
|
||||
- Custom prompt templates
|
||||
- Enterprise data governance
|
||||
|
||||
**🚀 Use Cases:**
|
||||
- Business intelligence reporting
|
||||
- Customer data analysis
|
||||
- Sales pipeline insights
|
||||
- Compliance data queries
|
||||
|
||||
#### 3. 🌐 Playwright MCP Server
|
||||
**🔗 Repository**: [microsoft/playwright-mcp](https://github.com/microsoft/playwright-mcp)
|
||||
**🎯 Purpose**: Browser automation and web interaction capabilities
|
||||
|
||||
**✨ Key Features:**
|
||||
- Cross-browser automation (Chrome, Firefox, Safari)
|
||||
- Intelligent element detection
|
||||
- Screenshot and PDF generation
|
||||
- Network traffic monitoring
|
||||
|
||||
**🚀 Use Cases:**
|
||||
- Automated testing workflows
|
||||
- Web scraping and data extraction
|
||||
- UI/UX monitoring
|
||||
- Competitive analysis automation
|
||||
|
||||
#### 4. 📁 Files MCP Server
|
||||
**🔗 Repository**: [microsoft/files-mcp-server](https://github.com/microsoft/files-mcp-server)
|
||||
**🎯 Purpose**: Intelligent file system operations
|
||||
|
||||
**✨ Key Features:**
|
||||
- Declarative file management
|
||||
- Content synchronization
|
||||
- Version control integration
|
||||
- Metadata extraction
|
||||
|
||||
**🚀 Use Cases:**
|
||||
- Documentation management
|
||||
- Code repository organization
|
||||
- Content publishing workflows
|
||||
- Data pipeline file handling
|
||||
|
||||
#### 5. 📝 MarkItDown MCP Server
|
||||
**🔗 Repository**: [microsoft/markitdown](https://github.com/microsoft/markitdown)
|
||||
**🎯 Purpose**: Advanced Markdown processing and manipulation
|
||||
|
||||
**✨ Key Features:**
|
||||
- Rich Markdown parsing
|
||||
- Format conversion (MD ↔ HTML ↔ PDF)
|
||||
- Content structure analysis
|
||||
- Template processing
|
||||
|
||||
**🚀 Use Cases:**
|
||||
- Technical documentation workflows
|
||||
- Content management systems
|
||||
- Report generation
|
||||
- Knowledge base automation
|
||||
|
||||
#### 6. 📈 Clarity MCP Server
|
||||
**📦 Package**: [@microsoft/clarity-mcp-server](https://www.npmjs.com/package/@microsoft/clarity-mcp-server)
|
||||
**🎯 Purpose**: Web analytics and user behavior insights
|
||||
|
||||
**✨ Key Features:**
|
||||
- Heatmap data analysis
|
||||
- User session recordings
|
||||
- Performance metrics
|
||||
- Conversion funnel analysis
|
||||
|
||||
**🚀 Use Cases:**
|
||||
- Website optimization
|
||||
- User experience research
|
||||
- A/B testing analysis
|
||||
- Business intelligence dashboards
|
||||
|
||||
### 🌍 Community Ecosystem
|
||||
|
||||
Beyond Microsoft's servers, the MCP ecosystem includes:
|
||||
- **🐙 GitHub MCP**: Repository management and code analysis
|
||||
- **🗄️ Database MCPs**: PostgreSQL, MySQL, MongoDB integrations
|
||||
- **☁️ Cloud Provider MCPs**: AWS, GCP, Digital Ocean tools
|
||||
- **📧 Communication MCPs**: Slack, Teams, Email integrations
|
||||
|
||||
## 🛠️ Hands-On Lab: Building a Browser Automation Agent
|
||||
|
||||
**🎯 Project Goal**: Create an intelligent browser automation agent using Playwright MCP server that can navigate websites, extract information, and perform complex web interactions.
|
||||
|
||||
### 🚀 Phase 1: Agent Foundation Setup
|
||||
|
||||
#### Step 1: Initialize Your Agent
|
||||
1. **Open Microsoft Foundry Toolkit Agent Builder**
|
||||
2. **Create New Agent** with the following configuration:
|
||||
- **Name**: `BrowserAgent`
|
||||
- **Model**: Choose GPT-4o
|
||||
|
||||
|
||||
|
||||
|
||||
### 🔧 Phase 2: MCP Integration Workflow
|
||||
|
||||
#### Step 3: Add MCP Server Integration
|
||||
1. **Navigate to Tools Section** in Agent Builder
|
||||
2. **Click "Add Tool"** to open the integration menu
|
||||
3. **Select "MCP Server"** from available options
|
||||
|
||||
|
||||
|
||||
**🔍 Understanding Tool Types:**
|
||||
- **Built-in Tools**: Pre-configured Microsoft Foundry Toolkit functions
|
||||
- **MCP Servers**: External service integrations
|
||||
- **Custom APIs**: Your own service endpoints
|
||||
- **Function Calling**: Direct model function access
|
||||
|
||||
#### Step 4: MCP Server Selection
|
||||
1. **Choose "MCP Server"** option to proceed
|
||||
|
||||
|
||||
2. **Browse MCP Catalog** to explore available integrations
|
||||
|
||||
|
||||
|
||||
### 🎮 Phase 3: Playwright MCP Configuration
|
||||
|
||||
#### Step 5: Select and Configure Playwright
|
||||
1. **Click "Use Featured MCP Servers"** to access Microsoft's verified servers
|
||||
2. **Select "Playwright"** from the featured list
|
||||
3. **Accept Default MCP ID** or customize for your environment
|
||||
|
||||
|
||||
|
||||
#### Step 6: Enable Playwright Capabilities
|
||||
**🔑 Critical Step**: Select **ALL** available Playwright methods for maximum functionality
|
||||
|
||||
|
||||
|
||||
**🛠️ Essential Playwright Tools:**
|
||||
- **Navigation**: `goto`, `goBack`, `goForward`, `reload`
|
||||
- **Interaction**: `click`, `fill`, `press`, `hover`, `drag`
|
||||
- **Extraction**: `textContent`, `innerHTML`, `getAttribute`
|
||||
- **Validation**: `isVisible`, `isEnabled`, `waitForSelector`
|
||||
- **Capture**: `screenshot`, `pdf`, `video`
|
||||
- **Network**: `setExtraHTTPHeaders`, `route`, `waitForResponse`
|
||||
|
||||
#### Step 7: Verify Integration Success
|
||||
**✅ Success Indicators:**
|
||||
- All tools appear in Agent Builder interface
|
||||
- No error messages in the integration panel
|
||||
- Playwright server status shows "Connected"
|
||||
|
||||
|
||||
|
||||
**🔧 Troubleshooting Common Issues:**
|
||||
- **Connection Failed**: Check internet connectivity and firewall settings
|
||||
- **Missing Tools**: Ensure all capabilities were selected during setup
|
||||
- **Permission Errors**: Verify VS Code has necessary system permissions
|
||||
|
||||
### 🎯 Phase 4: Advanced Prompt Engineering
|
||||
|
||||
#### Step 8: Design Intelligent System Prompts
|
||||
Create sophisticated prompts that leverage Playwright's full capabilities:
|
||||
|
||||
```markdown
|
||||
# Web Automation Expert System Prompt
|
||||
|
||||
## Core Identity
|
||||
You are an advanced web automation specialist with deep expertise in browser automation, web scraping, and user experience analysis. You have access to Playwright tools for comprehensive browser control.
|
||||
|
||||
## Capabilities & Approach
|
||||
### Navigation Strategy
|
||||
- Always start with screenshots to understand page layout
|
||||
- Use semantic selectors (text content, labels) when possible
|
||||
- Implement wait strategies for dynamic content
|
||||
- Handle single-page applications (SPAs) effectively
|
||||
|
||||
### Error Handling
|
||||
- Retry failed operations with exponential backoff
|
||||
- Provide clear error descriptions and solutions
|
||||
- Suggest alternative approaches when primary methods fail
|
||||
- Always capture diagnostic screenshots on errors
|
||||
|
||||
### Data Extraction
|
||||
- Extract structured data in JSON format when possible
|
||||
- Provide confidence scores for extracted information
|
||||
- Validate data completeness and accuracy
|
||||
- Handle pagination and infinite scroll scenarios
|
||||
|
||||
### Reporting
|
||||
- Include step-by-step execution logs
|
||||
- Provide before/after screenshots for verification
|
||||
- Suggest optimizations and alternative approaches
|
||||
- Document any limitations or edge cases encountered
|
||||
|
||||
## Ethical Guidelines
|
||||
- Respect robots.txt and rate limiting
|
||||
- Avoid overloading target servers
|
||||
- Only extract publicly available information
|
||||
- Follow website terms of service
|
||||
```
|
||||
|
||||
#### Step 9: Create Dynamic User Prompts
|
||||
Design prompts that demonstrate various capabilities:
|
||||
|
||||
**🌐 Web Analysis Example:**
|
||||
```markdown
|
||||
Navigate to github.com/kinfey and provide a comprehensive analysis including:
|
||||
1. Repository structure and organization
|
||||
2. Recent activity and contribution patterns
|
||||
3. Documentation quality assessment
|
||||
4. Technology stack identification
|
||||
5. Community engagement metrics
|
||||
6. Notable projects and their purposes
|
||||
|
||||
Include screenshots at key steps and provide actionable insights.
|
||||
```
|
||||
|
||||
|
||||
|
||||
### 🚀 Phase 5: Execution and Testing
|
||||
|
||||
#### Step 10: Execute Your First Automation
|
||||
1. **Click "Run"** to launch the automation sequence
|
||||
2. **Monitor Real-time Execution**:
|
||||
- Chrome browser launches automatically
|
||||
- Agent navigates to target website
|
||||
- Screenshots capture each major step
|
||||
- Analysis results stream in real-time
|
||||
|
||||
|
||||
|
||||
#### Step 11: Analyze Results and Insights
|
||||
Review comprehensive analysis in Agent Builder's interface:
|
||||
|
||||
|
||||
|
||||
### 🌟 Phase 6: Advanced Capabilities and Deployment
|
||||
|
||||
#### Step 12: Export and Production Deployment
|
||||
Agent Builder supports multiple deployment options:
|
||||
|
||||
|
||||
|
||||
## 🎓 Module 2 Summary & Next Steps
|
||||
|
||||
### 🏆 Achievement Unlocked: MCP Integration Master
|
||||
|
||||
**✅ Skills Mastered:**
|
||||
- [ ] Understanding MCP architecture and benefits
|
||||
- [ ] Navigating Microsoft's MCP server ecosystem
|
||||
- [ ] Integrating Playwright MCP with Microsoft Foundry Toolkit
|
||||
- [ ] Building sophisticated browser automation agents
|
||||
- [ ] Advanced prompt engineering for web automation
|
||||
|
||||
### 📚 Additional Resources
|
||||
|
||||
- **🔗 MCP Specification**: [Official Protocol Documentation](https://modelcontextprotocol.io/)
|
||||
- **🛠️ Playwright API**: [Complete Method Reference](https://playwright.dev/docs/api/class-playwright)
|
||||
- **🏢 Microsoft MCP Servers**: [Enterprise Integration Guide](https://github.com/microsoft/mcp-servers)
|
||||
- **🌍 Community Examples**: [MCP Server Gallery](https://github.com/modelcontextprotocol/servers)
|
||||
|
||||
**🎉 Congratulations!** You've successfully mastered MCP integration and can now build production-ready AI agents with external tool capabilities!
|
||||
|
||||
|
||||
### 🔜 Continue to Next Module
|
||||
|
||||
Ready to take your MCP skills to the next level? Proceed to **[Module 3: Advanced MCP Development with Microsoft Foundry Toolkit](../lab3/README.md)** where you'll learn how to:
|
||||
- Create your own custom MCP servers
|
||||
- Configure and use the latest MCP Python SDK
|
||||
- Set up the MCP Inspector for debugging
|
||||
- Master advanced MCP server development workflows
|
||||
- Build a Weather MCP Server from scratch
|
||||
@@ -0,0 +1,208 @@
|
||||
"""Connect model with mcp tools in Python
|
||||
# Run this python script
|
||||
> pip install mcp azure-ai-inference
|
||||
> python <this-script-path>.py
|
||||
"""
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
from typing import Dict, Optional
|
||||
from contextlib import AsyncExitStack
|
||||
|
||||
from mcp import ClientSession, StdioServerParameters
|
||||
from mcp.client.stdio import stdio_client
|
||||
from mcp.client.sse import sse_client
|
||||
|
||||
|
||||
from azure.ai.inference import ChatCompletionsClient
|
||||
from azure.ai.inference.models import AssistantMessage, SystemMessage, UserMessage, ToolMessage
|
||||
from azure.ai.inference.models import ImageContentItem, ImageUrl, TextContentItem
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
class MCPClient:
|
||||
def __init__(self):
|
||||
# Initialize session and client objects
|
||||
self._servers = {}
|
||||
self._tool_to_server_map = {}
|
||||
self.exit_stack = AsyncExitStack()
|
||||
# To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings.
|
||||
# Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens
|
||||
self.azureai = ChatCompletionsClient(
|
||||
endpoint = "https://models.inference.ai.azure.com",
|
||||
credential = AzureKeyCredential(os.environ["GITHUB_TOKEN"]),
|
||||
api_version = "2024-08-01-preview",
|
||||
)
|
||||
|
||||
async def connect_stdio_server(self, server_id: str, command: str, args: list[str], env: Dict[str, str]):
|
||||
"""Connect to an MCP server using STDIO transport
|
||||
|
||||
Args:
|
||||
server_id: Unique identifier for this server connection
|
||||
command: Command to run the MCP server
|
||||
args: Arguments for the command
|
||||
env: Optional environment variables
|
||||
"""
|
||||
server_params = StdioServerParameters(
|
||||
command=command,
|
||||
args=args,
|
||||
env=env
|
||||
)
|
||||
|
||||
stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
|
||||
stdio, write = stdio_transport
|
||||
session = await self.exit_stack.enter_async_context(ClientSession(stdio, write))
|
||||
await session.initialize()
|
||||
|
||||
# Register the server
|
||||
await self._register_server(server_id, session)
|
||||
|
||||
async def connect_sse_server(self, server_id: str, url: str, headers: Dict[str, str]):
|
||||
"""Connect to an MCP server using SSE transport
|
||||
|
||||
Args:
|
||||
server_id: Unique identifier for this server connection
|
||||
url: URL of the SSE server
|
||||
headers: Optional HTTP headers
|
||||
"""
|
||||
sse_context = await self.exit_stack.enter_async_context(sse_client(url=url, headers=headers))
|
||||
read, write = sse_context
|
||||
session = await self.exit_stack.enter_async_context(ClientSession(read, write))
|
||||
await session.initialize()
|
||||
|
||||
# Register the server
|
||||
await self._register_server(server_id, session)
|
||||
|
||||
async def _register_server(self, server_id: str, session: ClientSession):
|
||||
"""Register a server and its tools in the client
|
||||
|
||||
Args:
|
||||
server_id: Unique identifier for this server
|
||||
session: Connected ClientSession
|
||||
"""
|
||||
# List available tools
|
||||
response = await session.list_tools()
|
||||
tools = response.tools
|
||||
|
||||
# Store server connection info
|
||||
self._servers[server_id] = {
|
||||
"session": session,
|
||||
"tools": tools
|
||||
}
|
||||
|
||||
# Update tool-to-server mapping
|
||||
for tool in tools:
|
||||
self._tool_to_server_map[tool.name] = server_id
|
||||
|
||||
print(f"\nConnected to server '{server_id}' with tools:", [tool.name for tool in tools])
|
||||
|
||||
async def chatWithTools(self, messages: list[any]) -> str:
|
||||
"""Chat with model and using tools
|
||||
Args:
|
||||
messages: Messages to send to the model
|
||||
"""
|
||||
if not self._servers:
|
||||
raise ValueError("No MCP servers connected. Connect to at least one server first.")
|
||||
|
||||
# Collect tools from all connected servers
|
||||
available_tools = []
|
||||
for server_id, server_info in self._servers.items():
|
||||
for tool in server_info["tools"]:
|
||||
available_tools.append({
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": tool.name,
|
||||
"description": tool.description,
|
||||
"parameters": tool.inputSchema
|
||||
},
|
||||
})
|
||||
|
||||
while True:
|
||||
|
||||
# Call model
|
||||
response = self.azureai.complete(
|
||||
messages = messages,
|
||||
model = "gpt-4o",
|
||||
tools=available_tools,
|
||||
response_format = "text",
|
||||
temperature = 1,
|
||||
top_p = 1,
|
||||
)
|
||||
hasToolCall = False
|
||||
|
||||
if response.choices[0].message.tool_calls:
|
||||
for tool in response.choices[0].message.tool_calls:
|
||||
hasToolCall = True
|
||||
tool_name = tool.function.name
|
||||
tool_args = json.loads(tool.function.arguments)
|
||||
messages.append(
|
||||
AssistantMessage(
|
||||
tool_calls = [{
|
||||
"id": tool.id,
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": tool.function.name,
|
||||
"arguments": tool.function.arguments,
|
||||
}
|
||||
}]
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
# Find the appropriate server for this tool
|
||||
if tool_name in self._tool_to_server_map:
|
||||
server_id = self._tool_to_server_map[tool_name]
|
||||
server_session = self._servers[server_id]["session"]
|
||||
|
||||
# Execute tool call on the appropriate server
|
||||
result = await server_session.call_tool(tool_name, tool_args)
|
||||
print(f"[Server '{server_id}' call tool '{tool_name}' with args {tool_args}]: {result.content}")
|
||||
|
||||
messages.append(
|
||||
ToolMessage(
|
||||
tool_call_id = tool.id,
|
||||
content = str(result.content)
|
||||
)
|
||||
)
|
||||
else:
|
||||
messages.append(
|
||||
AssistantMessage(
|
||||
content = response.choices[0].message.content
|
||||
)
|
||||
)
|
||||
print(f"[Model Response]: {response.choices[0].message.content}")
|
||||
|
||||
if not hasToolCall:
|
||||
break
|
||||
|
||||
async def cleanup(self):
|
||||
"""Clean up resources"""
|
||||
await self.exit_stack.aclose()
|
||||
await asyncio.sleep(1)
|
||||
|
||||
async def main():
|
||||
client = MCPClient()
|
||||
messages = [
|
||||
SystemMessage(content = "You are my browser automation assistant, helping me operate the browser"),
|
||||
UserMessage(content = [
|
||||
TextContentItem(text = "Navigation to github.com/kinfey"),
|
||||
]),
|
||||
]
|
||||
try:
|
||||
await client.connect_stdio_server(
|
||||
"mcp-mbnn4zlx",
|
||||
"npx",
|
||||
[
|
||||
"-y",
|
||||
"@playwright/mcp@latest",
|
||||
],
|
||||
{
|
||||
}
|
||||
)
|
||||
await client.chatWithTools(messages)
|
||||
except Exception as e:
|
||||
print(f"\nError: {str(e)}")
|
||||
finally:
|
||||
await client.cleanup()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,393 @@
|
||||
# 🔧 Module 3: Advanced MCP Development with Microsoft Foundry Toolkit
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## 🎯 Learning Objectives
|
||||
|
||||
By the end of this lab, you will be able to:
|
||||
|
||||
- ✅ Create custom MCP servers using the Microsoft Foundry Toolkit
|
||||
- ✅ Configure and use the latest MCP Python SDK (v1.9.3)
|
||||
- ✅ Set up and utilize the MCP Inspector for debugging
|
||||
- ✅ Debug MCP servers in both Agent Builder and Inspector environments
|
||||
- ✅ Understand advanced MCP server development workflows
|
||||
|
||||
## 📋 Prerequisites
|
||||
|
||||
- Completion of Lab 2 (MCP Fundamentals)
|
||||
- VS Code with Microsoft Foundry Toolkit extension installed
|
||||
- Python 3.10+ environment
|
||||
- Node.js and npm for Inspector setup
|
||||
|
||||
## 🏗️ What You'll Build
|
||||
|
||||
In this lab, you'll create a **Weather MCP Server** that demonstrates:
|
||||
- Custom MCP server implementation
|
||||
- Integration with Microsoft Foundry Toolkit Agent Builder
|
||||
- Professional debugging workflows
|
||||
- Modern MCP SDK usage patterns
|
||||
|
||||
---
|
||||
|
||||
## 🔧 Core Components Overview
|
||||
|
||||
### 🐍 MCP Python SDK
|
||||
The Model Context Protocol Python SDK provides the foundation for building custom MCP servers. You'll use version 1.9.3 with enhanced debugging capabilities.
|
||||
|
||||
### 🔍 MCP Inspector
|
||||
A powerful debugging tool that provides:
|
||||
- Real-time server monitoring
|
||||
- Tool execution visualization
|
||||
- Network request/response inspection
|
||||
- Interactive testing environment
|
||||
|
||||
---
|
||||
|
||||
## 📖 Step-by-Step Implementation
|
||||
|
||||
### Step 1: Create a WeatherAgent in Agent Builder
|
||||
|
||||
1. **Launch Agent Builder** in VS Code through the Microsoft Foundry Toolkit extension
|
||||
2. **Create a new agent** with the following configuration:
|
||||
- Agent Name: `WeatherAgent`
|
||||
|
||||
|
||||
|
||||
### Step 2: Initialize MCP Server Project
|
||||
|
||||
1. **Navigate to Tools** → **Add Tool** in Agent Builder
|
||||
2. **Select "MCP Server"** from the available options
|
||||
3. **Choose "Create A new MCP Server"**
|
||||
4. **Select the `python-weather` template**
|
||||
5. **Name your server:** `weather_mcp`
|
||||
|
||||
|
||||
|
||||
### Step 3: Open and Examine the Project
|
||||
|
||||
1. **Open the generated project** in VS Code
|
||||
2. **Review the project structure:**
|
||||
```
|
||||
weather_mcp/
|
||||
├── src/
|
||||
│ ├── __init__.py
|
||||
│ └── server.py
|
||||
├── inspector/
|
||||
│ ├── package.json
|
||||
│ └── package-lock.json
|
||||
├── .vscode/
|
||||
│ ├── launch.json
|
||||
│ └── tasks.json
|
||||
├── pyproject.toml
|
||||
└── README.md
|
||||
```
|
||||
|
||||
### Step 4: Upgrade to Latest MCP SDK
|
||||
|
||||
> **🔍 Why Upgrade?** We want to use the latest MCP SDK (v1.9.3) and Inspector service (0.14.0) for enhanced features and better debugging capabilities.
|
||||
|
||||
#### 4a. Update Python Dependencies
|
||||
|
||||
**Edit `pyproject.toml`:** update [./code/weather_mcp/pyproject.toml](./code/weather_mcp/pyproject.toml)
|
||||
|
||||
|
||||
#### 4b. Update Inspector Configuration
|
||||
|
||||
**Edit `inspector/package.json`:** update [./code/weather_mcp/inspector/package.json](./code/weather_mcp/inspector/package.json)
|
||||
|
||||
#### 4c. Update Inspector Dependencies
|
||||
|
||||
**Edit `inspector/package-lock.json`:** update [./code/weather_mcp/inspector/package-lock.json](./code/weather_mcp/inspector/package-lock.json)
|
||||
|
||||
> **📝 Note:** This file contains extensive dependency definitions. Below is the essential structure - the full content ensures proper dependency resolution.
|
||||
|
||||
|
||||
> **⚡ Full Package Lock:** The complete package-lock.json contains ~3000 lines of dependency definitions. The above shows the key structure - use the provided file for complete dependency resolution.
|
||||
|
||||
### Step 5: Configure VS Code Debugging
|
||||
|
||||
*Note: Please copy the file in the specified path to replace the corresponding local file*
|
||||
|
||||
#### 5a. Update Launch Configuration
|
||||
|
||||
**Edit `.vscode/launch.json`:**
|
||||
|
||||
```json
|
||||
{
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
{
|
||||
"name": "Attach to Local MCP",
|
||||
"type": "debugpy",
|
||||
"request": "attach",
|
||||
"connect": {
|
||||
"host": "localhost",
|
||||
"port": 5678
|
||||
},
|
||||
"presentation": {
|
||||
"hidden": true
|
||||
},
|
||||
"internalConsoleOptions": "neverOpen",
|
||||
"postDebugTask": "Terminate All Tasks"
|
||||
},
|
||||
{
|
||||
"name": "Launch Inspector (Edge)",
|
||||
"type": "msedge",
|
||||
"request": "launch",
|
||||
"url": "http://localhost:6274?timeout=60000&serverUrl=http://localhost:3001/sse#tools",
|
||||
"cascadeTerminateToConfigurations": [
|
||||
"Attach to Local MCP"
|
||||
],
|
||||
"presentation": {
|
||||
"hidden": true
|
||||
},
|
||||
"internalConsoleOptions": "neverOpen"
|
||||
},
|
||||
{
|
||||
"name": "Launch Inspector (Chrome)",
|
||||
"type": "chrome",
|
||||
"request": "launch",
|
||||
"url": "http://localhost:6274?timeout=60000&serverUrl=http://localhost:3001/sse#tools",
|
||||
"cascadeTerminateToConfigurations": [
|
||||
"Attach to Local MCP"
|
||||
],
|
||||
"presentation": {
|
||||
"hidden": true
|
||||
},
|
||||
"internalConsoleOptions": "neverOpen"
|
||||
}
|
||||
],
|
||||
"compounds": [
|
||||
{
|
||||
"name": "Debug in Agent Builder",
|
||||
"configurations": [
|
||||
"Attach to Local MCP"
|
||||
],
|
||||
"preLaunchTask": "Open Agent Builder",
|
||||
},
|
||||
{
|
||||
"name": "Debug in Inspector (Edge)",
|
||||
"configurations": [
|
||||
"Launch Inspector (Edge)",
|
||||
"Attach to Local MCP"
|
||||
],
|
||||
"preLaunchTask": "Start MCP Inspector",
|
||||
"stopAll": true
|
||||
},
|
||||
{
|
||||
"name": "Debug in Inspector (Chrome)",
|
||||
"configurations": [
|
||||
"Launch Inspector (Chrome)",
|
||||
"Attach to Local MCP"
|
||||
],
|
||||
"preLaunchTask": "Start MCP Inspector",
|
||||
"stopAll": true
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
**Edit `.vscode/tasks.json`:**
|
||||
|
||||
```
|
||||
{
|
||||
"version": "2.0.0",
|
||||
"tasks": [
|
||||
{
|
||||
"label": "Start MCP Server",
|
||||
"type": "shell",
|
||||
"command": "python -m debugpy --listen 127.0.0.1:5678 src/__init__.py sse",
|
||||
"isBackground": true,
|
||||
"options": {
|
||||
"cwd": "${workspaceFolder}",
|
||||
"env": {
|
||||
"PORT": "3001"
|
||||
}
|
||||
},
|
||||
"problemMatcher": {
|
||||
"pattern": [
|
||||
{
|
||||
"regexp": "^.*$",
|
||||
"file": 0,
|
||||
"location": 1,
|
||||
"message": 2
|
||||
}
|
||||
],
|
||||
"background": {
|
||||
"activeOnStart": true,
|
||||
"beginsPattern": ".*",
|
||||
"endsPattern": "Application startup complete|running"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"label": "Start MCP Inspector",
|
||||
"type": "shell",
|
||||
"command": "npm run dev:inspector",
|
||||
"isBackground": true,
|
||||
"options": {
|
||||
"cwd": "${workspaceFolder}/inspector",
|
||||
"env": {
|
||||
"CLIENT_PORT": "6274",
|
||||
"SERVER_PORT": "6277",
|
||||
}
|
||||
},
|
||||
"problemMatcher": {
|
||||
"pattern": [
|
||||
{
|
||||
"regexp": "^.*$",
|
||||
"file": 0,
|
||||
"location": 1,
|
||||
"message": 2
|
||||
}
|
||||
],
|
||||
"background": {
|
||||
"activeOnStart": true,
|
||||
"beginsPattern": "Starting MCP inspector",
|
||||
"endsPattern": "Proxy server listening on port"
|
||||
}
|
||||
},
|
||||
"dependsOn": [
|
||||
"Start MCP Server"
|
||||
]
|
||||
},
|
||||
{
|
||||
"label": "Open Agent Builder",
|
||||
"type": "shell",
|
||||
"command": "echo ${input:openAgentBuilder}",
|
||||
"presentation": {
|
||||
"reveal": "never"
|
||||
},
|
||||
"dependsOn": [
|
||||
"Start MCP Server"
|
||||
],
|
||||
},
|
||||
{
|
||||
"label": "Terminate All Tasks",
|
||||
"command": "echo ${input:terminate}",
|
||||
"type": "shell",
|
||||
"problemMatcher": []
|
||||
}
|
||||
],
|
||||
"inputs": [
|
||||
{
|
||||
"id": "openAgentBuilder",
|
||||
"type": "command",
|
||||
"command": "ai-mlstudio.agentBuilder",
|
||||
"args": {
|
||||
"initialMCPs": [ "local-server-weather_mcp" ],
|
||||
"triggeredFrom": "vsc-tasks"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "terminate",
|
||||
"type": "command",
|
||||
"command": "workbench.action.tasks.terminate",
|
||||
"args": "terminateAll"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Running and Testing Your MCP Server
|
||||
|
||||
### Step 6: Install Dependencies
|
||||
|
||||
After making the configuration changes, run the following commands:
|
||||
|
||||
**Install Python dependencies:**
|
||||
```bash
|
||||
uv sync
|
||||
```
|
||||
|
||||
**Install Inspector dependencies:**
|
||||
```bash
|
||||
cd inspector
|
||||
npm install
|
||||
```
|
||||
|
||||
### Step 7: Debug with Agent Builder
|
||||
|
||||
1. **Press F5** or use the **"Debug in Agent Builder"** configuration
|
||||
2. **Select the compound configuration** from the debug panel
|
||||
3. **Wait for the server to start** and Agent Builder to open
|
||||
4. **Test your weather MCP server** with natural language queries
|
||||
|
||||
Input prompt like this
|
||||
|
||||
SYSTEM_PROMPT
|
||||
|
||||
```
|
||||
You are my weather assistant
|
||||
```
|
||||
|
||||
USER_PROMPT
|
||||
|
||||
```
|
||||
How's the weather like in Seattle
|
||||
```
|
||||
|
||||
|
||||
|
||||
### Step 8: Debug with MCP Inspector
|
||||
|
||||
1. **Use the "Debug in Inspector"** configuration (Edge or Chrome)
|
||||
2. **Open the Inspector interface** at `http://localhost:6274`
|
||||
3. **Explore the interactive testing environment:**
|
||||
- View available tools
|
||||
- Test tool execution
|
||||
- Monitor network requests
|
||||
- Debug server responses
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Key Learning Outcomes
|
||||
|
||||
By completing this lab, you have:
|
||||
|
||||
- [x] **Created a custom MCP server** using Microsoft Foundry Toolkit templates
|
||||
- [x] **Upgraded to the latest MCP SDK** (v1.9.3) for enhanced functionality
|
||||
- [x] **Configured professional debugging workflows** for both Agent Builder and Inspector
|
||||
- [x] **Set up the MCP Inspector** for interactive server testing
|
||||
- [x] **Mastered VS Code debugging configurations** for MCP development
|
||||
|
||||
## 🔧 Advanced Features Explored
|
||||
|
||||
| Feature | Description | Use Case |
|
||||
|---------|-------------|----------|
|
||||
| **MCP Python SDK v1.9.3** | Latest protocol implementation | Modern server development |
|
||||
| **MCP Inspector 0.14.0** | Interactive debugging tool | Real-time server testing |
|
||||
| **VS Code Debugging** | Integrated development environment | Professional debugging workflow |
|
||||
| **Agent Builder Integration** | Direct Microsoft Foundry Toolkit connection | End-to-end agent testing |
|
||||
|
||||
## 📚 Additional Resources
|
||||
|
||||
- [MCP Python SDK Documentation](https://modelcontextprotocol.io/docs/sdk/python)
|
||||
- [Microsoft Foundry Toolkit Extension Guide](https://code.visualstudio.com/docs/ai/ai-toolkit)
|
||||
- [VS Code Debugging Documentation](https://code.visualstudio.com/docs/editor/debugging)
|
||||
- [Model Context Protocol Specification](https://modelcontextprotocol.io/docs/concepts/architecture)
|
||||
|
||||
---
|
||||
|
||||
**🎉 Congratulations!** You've successfully completed Lab 3 and can now create, debug, and deploy custom MCP servers using professional development workflows.
|
||||
|
||||
### 🔜 Continue to Next Module
|
||||
|
||||
Ready to apply your MCP skills to a real-world development workflow? Continue to **[Module 4: Practical MCP Development - Custom GitHub Clone Server](../lab4/README.md)** where you'll:
|
||||
- Build a production-ready MCP server that automates GitHub repository operations
|
||||
- Implement GitHub repository cloning functionality via MCP
|
||||
- Integrate custom MCP servers with VS Code and GitHub Copilot Agent Mode
|
||||
- Test and deploy custom MCP servers in production environments
|
||||
- Learn practical workflow automation for developers
|
||||
|
||||
|
||||
|
||||
+7
@@ -0,0 +1,7 @@
|
||||
# python virtual environment
|
||||
.venv/
|
||||
__pycache__/
|
||||
*.egg-info/
|
||||
|
||||
# others
|
||||
node_modules/
|
||||
+1
@@ -0,0 +1 @@
|
||||
3.10
|
||||
+66
@@ -0,0 +1,66 @@
|
||||
# Weather MCP Server
|
||||
|
||||
This is a sample MCP Server in Python implementing weather tools with mock responses. It can be used as a scaffold for your own MCP Server. It includes the following features:
|
||||
|
||||
- **Weather Tool**: A tool that provides mocked weather information based on the given location.
|
||||
- **Connect to Agent Builder**: A feature that allows you to connect the MCP server to the Agent Builder for testing and debugging.
|
||||
- **Debug in [MCP Inspector](https://github.com/modelcontextprotocol/inspector)**: A feature that allows you to debug the MCP Server using the MCP Inspector.
|
||||
|
||||
## Get started with the Weather MCP Server template
|
||||
|
||||
> **Prerequisites**
|
||||
>
|
||||
> To run the MCP Server in your local dev machine, you will need:
|
||||
>
|
||||
> - [Python](https://www.python.org/)
|
||||
> - (*Optional - if you prefer uv*) [uv](https://github.com/astral-sh/uv)
|
||||
> - [Python Debugger Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.debugpy)
|
||||
|
||||
## Prepare environment
|
||||
|
||||
There are two approaches to set up the environment for this project. You can choose either one based on your preference.
|
||||
|
||||
> Note: Reload VSCode or terminal to ensure the virtual environment python is used after creating the virtual environment.
|
||||
|
||||
| Approach | Steps |
|
||||
| -------- | ----- |
|
||||
| Using `uv` | 1. Create virtual environment: `uv venv` <br>2. Run VSCode Command "***Python: Select Interpreter***" and select the python from created virtual environment <br>3. Install dependencies (include dev dependencies): `uv pip install -r pyproject.toml --extra dev` |
|
||||
| Using `pip` | 1. Create virtual environment: `python -m venv .venv` <br>2. Run VSCode Command "***Python: Select Interpreter***" and select the python from created virtual environment<br>3. Install dependencies (include dev dependencies): `pip install -e .[dev]` |
|
||||
|
||||
After setting up the environment, you can run the server in your local dev machine via Agent Builder as the MCP Client to get started:
|
||||
1. Open VS Code Debug panel. Select `Debug in Agent Builder` or press `F5` to start debugging the MCP server.
|
||||
2. Use Microsoft Foundry Toolkit Agent Builder to test the server with [this prompt](vscode://ms-windows-ai-studio.windows-ai-studio/open_prompt_builder?model_id=github/gpt-4.1-mini&system_prompt=You%20are%20a%20weather%20forecast%20professional%20that%20can%20tell%20weather%20information%20based%20on%20given%20location&user_prompt=What%20is%20the%20weather%20in%20Shanghai?&track_from=vsc_md&mcp=weather_mcp). Server will be auto-connected to the Agent Builder.
|
||||
3. Click `Run` to test the server with the prompt.
|
||||
|
||||
**Congratulations**! You have successfully run the Weather MCP Server in your local dev machine via Agent Builder as the MCP Client.
|
||||
|
||||
|
||||
## What's included in the template
|
||||
|
||||
| Folder / File| Contents |
|
||||
| ------------ | -------------------------------------------- |
|
||||
| `.vscode` | VSCode files for debugging |
|
||||
| `.aitk` | Configurations for Microsoft Foundry Toolkit |
|
||||
| `src` | The source code for the weather mcp server |
|
||||
|
||||
## How to debug the Weather MCP Server
|
||||
|
||||
> Notes:
|
||||
> - [MCP Inspector](https://github.com/modelcontextprotocol/inspector) is a visual developer tool for testing and debugging MCP servers.
|
||||
> - All debugging modes support breakpoints, so you can add breakpoints to the tool implementation code.
|
||||
|
||||
| Debug Mode | Description | Steps to debug |
|
||||
| ---------- | ----------- | --------------- |
|
||||
| Agent Builder | Debug the MCP server in the Agent Builder via Microsoft Foundry Toolkit. | 1. Open VS Code Debug panel. Select `Debug in Agent Builder` and press `F5` to start debugging the MCP server.<br>2. Use Microsoft Foundry Toolkit Agent Builder to test the server with [this prompt](vscode://ms-windows-ai-studio.windows-ai-studio/open_prompt_builder?model_id=github/gpt-4.1-mini&system_prompt=You%20are%20a%20weather%20forecast%20professional%20that%20can%20tell%20weather%20information%20based%20on%20given%20location&user_prompt=What%20is%20the%20weather%20in%20Shanghai?&track_from=vsc_md&mcp=weather_mcp). Server will be auto-connected to the Agent Builder.<br>3. Click `Run` to test the server with the prompt. |
|
||||
| MCP Inspector | Debug the MCP server using the MCP Inspector. | 1. Install [Node.js](https://nodejs.org/)<br> 2. Set up Inspector: `cd inspector` && `npm install` <br> 3. Open VS Code Debug panel. Select `Debug SSE in Inspector (Edge)` or `Debug SSE in Inspector (Chrome)`. Press F5 to start debugging.<br> 4. When MCP Inspector launches in the browser, click the `Connect` button to connect this MCP server.<br> 5. Then you can `List Tools`, select a tool, input parameters, and `Run Tool` to debug your server code.<br> |
|
||||
|
||||
## Default Ports and customizations
|
||||
|
||||
| Debug Mode | Ports | Definitions | Customizations | Note |
|
||||
| ---------- | ----- | ------------ | -------------- |-------------- |
|
||||
| Agent Builder | 3001 | [tasks.json](.vscode/tasks.json) | Edit [launch.json](.vscode/launch.json), [tasks.json](.vscode/tasks.json), [\_\_init\_\_.py](src/__init__.py), [mcp.json](.aitk/mcp.json) to change above ports. | N/A |
|
||||
| MCP Inspector | 3001 (Server); 5173 and 3000 (Inspector) | [tasks.json](.vscode/tasks.json) | Edit [launch.json](.vscode/launch.json), [tasks.json](.vscode/tasks.json), [\_\_init\_\_.py](src/__init__.py), [mcp.json](.aitk/mcp.json) to change above ports.| N/A |
|
||||
|
||||
## Feedback
|
||||
|
||||
If you have any feedback or suggestions for this template, please open an issue on the [Microsoft Foundry Toolkit GitHub repository](https://github.com/microsoft/vscode-ai-toolkit/issues)
|
||||
+3814
File diff suppressed because it is too large
Load Diff
+14
@@ -0,0 +1,14 @@
|
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{
|
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"name": "inspector-placeholder",
|
||||
"version": "0.0.1",
|
||||
"description": "This is just a placeholder to launch the inspector.",
|
||||
"scripts": {
|
||||
"dev:inspector": "mcp-inspector"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@modelcontextprotocol/inspector": "0.22.0"
|
||||
},
|
||||
"overrides": {
|
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"shell-quote": "1.8.4"
|
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}
|
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}
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
[project]
|
||||
name = "weather_mcp"
|
||||
version = "0.1.0"
|
||||
description = "A simple MCP weather server"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"mcp>=1.26.0"
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = ["debugpy==1.8.8"]
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
import os
|
||||
import sys
|
||||
from server import server
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""Main entry point"""
|
||||
transport_type = sys.argv[1] if len(sys.argv) > 1 else None
|
||||
server.settings.log_level = os.environ.get("LOG_LEVEL", "DEBUG")
|
||||
if transport_type == "sse":
|
||||
port = int(os.environ.get("PORT", 3001))
|
||||
server.settings.port = port
|
||||
server.settings.host = "127.0.0.1"
|
||||
server.run(transport="sse")
|
||||
elif transport_type == "stdio":
|
||||
server.run(transport="stdio")
|
||||
else:
|
||||
print("Invalid transport type. Use 'sse' or 'stdio'.")
|
||||
sys.exit(1)
|
||||
+26
@@ -0,0 +1,26 @@
|
||||
import json
|
||||
import random
|
||||
from mcp.server.fastmcp import FastMCP
|
||||
|
||||
# Initialize FastMCP server
|
||||
server = FastMCP("weather_mcp")
|
||||
|
||||
@server.tool()
|
||||
async def get_weather(location: str) -> str:
|
||||
"""Get weather for a location.
|
||||
|
||||
Args:
|
||||
location: Location to get weather for, e.g., city name, state, or coordinates
|
||||
|
||||
"""
|
||||
if not location:
|
||||
return "Location is required."
|
||||
|
||||
# mock weather data
|
||||
conditions = [ "Sunny", "Rainy", "Cloudy", "Snowy" ]
|
||||
weather = {
|
||||
"location": location,
|
||||
"temperature": f"{random.randint(10, 90)}°F",
|
||||
"condition": random.choice(conditions),
|
||||
}
|
||||
return json.dumps(weather, ensure_ascii=False)
|
||||
+759
@@ -0,0 +1,759 @@
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||||
version = 1
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revision = 3
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requires-python = ">=3.10"
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|
||||
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|
||||
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|
||||
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|
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||||
{ url = "https://files.pythonhosted.org/packages/13/64/b4d76f227d5c45a7e0b796c674fd81b0a6c4fbd48dc29271857d8219571c/rpds_py-0.30.0-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:dff13836529b921e22f15cb099751209a60009731a68519630a24d61f0b1b30a", size = 573981, upload-time = "2025-11-30T20:24:32.934Z" },
|
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|
||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sniffio"
|
||||
version = "1.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a2/87/a6771e1546d97e7e041b6ae58d80074f81b7d5121207425c964ddf5cfdbd/sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc", size = 20372, upload-time = "2024-02-25T23:20:04.057Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235, upload-time = "2024-02-25T23:20:01.196Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sse-starlette"
|
||||
version = "2.3.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/8c/f4/989bc70cb8091eda43a9034ef969b25145291f3601703b82766e5172dfed/sse_starlette-2.3.6.tar.gz", hash = "sha256:0382336f7d4ec30160cf9ca0518962905e1b69b72d6c1c995131e0a703b436e3", size = 18284, upload-time = "2025-05-30T13:34:12.914Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/81/05/78850ac6e79af5b9508f8841b0f26aa9fd329a1ba00bf65453c2d312bcc8/sse_starlette-2.3.6-py3-none-any.whl", hash = "sha256:d49a8285b182f6e2228e2609c350398b2ca2c36216c2675d875f81e93548f760", size = 10606, upload-time = "2025-05-30T13:34:11.703Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "starlette"
|
||||
version = "1.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/eb/e3/7c1dc7381d9f8ab7d854328ebfa884e62cb3f3d8549ddfd37c7814f42afa/starlette-1.3.1.tar.gz", hash = "sha256:05d0213193f2fbaae60e2ecb593b4add4262ad4e46536b54abe36f11a71724e0", size = 2703240, upload-time = "2026-06-12T09:23:11.602Z" }
|
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wheels = [
|
||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.14.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d1/bc/51647cd02527e87d05cb083ccc402f93e441606ff1f01739a62c8ad09ba5/typing_extensions-4.14.0.tar.gz", hash = "sha256:8676b788e32f02ab42d9e7c61324048ae4c6d844a399eebace3d4979d75ceef4", size = 107423, upload-time = "2025-06-02T14:52:11.399Z" }
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wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/69/e0/552843e0d356fbb5256d21449fa957fa4eff3bbc135a74a691ee70c7c5da/typing_extensions-4.14.0-py3-none-any.whl", hash = "sha256:a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af", size = 43839, upload-time = "2025-06-02T14:52:10.026Z" },
|
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]
|
||||
|
||||
[[package]]
|
||||
name = "typing-inspection"
|
||||
version = "0.4.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
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dependencies = [
|
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{ name = "typing-extensions" },
|
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]
|
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sdist = { url = "https://files.pythonhosted.org/packages/f8/b1/0c11f5058406b3af7609f121aaa6b609744687f1d158b3c3a5bf4cc94238/typing_inspection-0.4.1.tar.gz", hash = "sha256:6ae134cc0203c33377d43188d4064e9b357dba58cff3185f22924610e70a9d28", size = 75726, upload-time = "2025-05-21T18:55:23.885Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/17/69/cd203477f944c353c31bade965f880aa1061fd6bf05ded0726ca845b6ff7/typing_inspection-0.4.1-py3-none-any.whl", hash = "sha256:389055682238f53b04f7badcb49b989835495a96700ced5dab2d8feae4b26f51", size = 14552, upload-time = "2025-05-21T18:55:22.152Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "uvicorn"
|
||||
version = "0.34.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "click" },
|
||||
{ name = "h11" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.11'" },
|
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]
|
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sdist = { url = "https://files.pythonhosted.org/packages/de/ad/713be230bcda622eaa35c28f0d328c3675c371238470abdea52417f17a8e/uvicorn-0.34.3.tar.gz", hash = "sha256:35919a9a979d7a59334b6b10e05d77c1d0d574c50e0fc98b8b1a0f165708b55a", size = 76631, upload-time = "2025-06-01T07:48:17.531Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/6d/0d/8adfeaa62945f90d19ddc461c55f4a50c258af7662d34b6a3d5d1f8646f6/uvicorn-0.34.3-py3-none-any.whl", hash = "sha256:16246631db62bdfbf069b0645177d6e8a77ba950cfedbfd093acef9444e4d885", size = 62431, upload-time = "2025-06-01T07:48:15.664Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "weather-mcp"
|
||||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "mcp" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
dev = [
|
||||
{ name = "debugpy" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "debugpy", marker = "extra == 'dev'", specifier = "==1.8.8" },
|
||||
{ name = "mcp", specifier = ">=1.26.0" },
|
||||
]
|
||||
provides-extras = ["dev"]
|
||||
@@ -0,0 +1,208 @@
|
||||
# 🐙 Module 4: Practical MCP Development - Custom GitHub Clone Server
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
> **⚡ Quick Start:** Build a production-ready MCP server that automates GitHub repository cloning and VS Code integration in just 30 minutes!
|
||||
|
||||
## 🎯 Learning Objectives
|
||||
|
||||
By the end of this lab, you will be able to:
|
||||
|
||||
- ✅ Create a custom MCP server for real-world development workflows
|
||||
- ✅ Implement GitHub repository cloning functionality via MCP
|
||||
- ✅ Integrate custom MCP servers with VS Code and Agent Builder
|
||||
- ✅ Use GitHub Copilot Agent Mode with custom MCP tools
|
||||
- ✅ Test and deploy custom MCP servers in production environments
|
||||
|
||||
## 📋 Prerequisites
|
||||
|
||||
- Completion of Labs 1-3 (MCP fundamentals and advanced development)
|
||||
- GitHub Copilot subscription ([free signup available](https://github.com/github-copilot/signup))
|
||||
- VS Code with Microsoft Foundry Toolkit and GitHub Copilot extensions
|
||||
- Git CLI installed and configured
|
||||
|
||||
## 🏗️ Project Overview
|
||||
|
||||
### **Real-World Development Challenge**
|
||||
As developers, we frequently use GitHub to clone repositories and open them in VS Code or VS Code Insiders. This manual process involves:
|
||||
1. Opening terminal/command prompt
|
||||
2. Navigating to the desired directory
|
||||
3. Running `git clone` command
|
||||
4. Opening VS Code in the cloned directory
|
||||
|
||||
**Our MCP solution streamlines this into a single intelligent command!**
|
||||
|
||||
### **What You'll Build**
|
||||
A **GitHub Clone MCP Server** (`git_mcp_server`) that provides:
|
||||
|
||||
| Feature | Description | Benefit |
|
||||
|---------|-------------|---------|
|
||||
| 🔄 **Smart Repository Cloning** | Clone GitHub repos with validation | Automated error checking |
|
||||
| 📁 **Intelligent Directory Management** | Check and create directories safely | Prevents overwriting |
|
||||
| 🚀 **Cross-Platform VS Code Integration** | Open projects in VS Code/Insiders | Seamless workflow transition |
|
||||
| 🛡️ **Robust Error Handling** | Handle network, permission, and path issues | Production-ready reliability |
|
||||
|
||||
---
|
||||
|
||||
## 📖 Step-by-Step Implementation
|
||||
|
||||
### Step 1: Create GitHub Agent in Agent Builder
|
||||
|
||||
1. **Launch Agent Builder** through the Microsoft Foundry Toolkit extension
|
||||
2. **Create a new agent** with the following configuration:
|
||||
```
|
||||
Agent Name: GitHubAgent
|
||||
```
|
||||
|
||||
3. **Initialize custom MCP server:**
|
||||
- Navigate to **Tools** → **Add Tool** → **MCP Server**
|
||||
- Select **"Create A new MCP Server"**
|
||||
- Choose **Python template** for maximum flexibility
|
||||
- **Server Name:** `git_mcp_server`
|
||||
|
||||
### Step 2: Configure GitHub Copilot Agent Mode
|
||||
|
||||
1. **Open GitHub Copilot** in VS Code (Ctrl/Cmd + Shift + P → "GitHub Copilot: Open")
|
||||
2. **Select Agent Model** in the Copilot interface
|
||||
3. **Choose Claude 3.7 model** for enhanced reasoning capabilities
|
||||
4. **Enable MCP integration** for tool access
|
||||
|
||||
> **💡 Pro Tip:** Claude 3.7 provides superior understanding of development workflows and error handling patterns.
|
||||
|
||||
### Step 3: Implement Core MCP Server Functionality
|
||||
|
||||
**Use the following detailed prompt with GitHub Copilot Agent Mode:**
|
||||
|
||||
```
|
||||
Create two MCP tools with the following comprehensive requirements:
|
||||
|
||||
🔧 TOOL A: clone_repository
|
||||
Requirements:
|
||||
- Clone any GitHub repository to a specified local folder
|
||||
- Return the absolute path of the successfully cloned project
|
||||
- Implement comprehensive validation:
|
||||
✓ Check if target directory already exists (return error if exists)
|
||||
✓ Validate GitHub URL format (https://github.com/user/repo)
|
||||
✓ Verify git command availability (prompt installation if missing)
|
||||
✓ Handle network connectivity issues
|
||||
✓ Provide clear error messages for all failure scenarios
|
||||
|
||||
🚀 TOOL B: open_in_vscode
|
||||
Requirements:
|
||||
- Open specified folder in VS Code or VS Code Insiders
|
||||
- Cross-platform compatibility (Windows/Linux/macOS)
|
||||
- Use direct application launch (not terminal commands)
|
||||
- Auto-detect available VS Code installations
|
||||
- Handle cases where VS Code is not installed
|
||||
- Provide user-friendly error messages
|
||||
|
||||
Additional Requirements:
|
||||
- Follow MCP 1.9.3 best practices
|
||||
- Include proper type hints and documentation
|
||||
- Implement logging for debugging purposes
|
||||
- Add input validation for all parameters
|
||||
- Include comprehensive error handling
|
||||
```
|
||||
|
||||
### Step 4: Test Your MCP Server
|
||||
|
||||
#### 4a. Test in Agent Builder
|
||||
|
||||
1. **Launch the debug configuration** for Agent Builder
|
||||
2. **Configure your agent with this system prompt:**
|
||||
|
||||
```
|
||||
SYSTEM_PROMPT:
|
||||
You are my intelligent coding repository assistant. You help developers efficiently clone GitHub repositories and set up their development environment. Always provide clear feedback about operations and handle errors gracefully.
|
||||
```
|
||||
|
||||
3. **Test with realistic user scenarios:**
|
||||
|
||||
```
|
||||
USER_PROMPT EXAMPLES:
|
||||
|
||||
Scenario : Basic Clone and Open
|
||||
"Clone {Your GitHub Repo link such as https://github.com/kinfey/GHCAgentWorkshop
|
||||
} and save to {The global path you specify}, then open it with VS Code Insiders"
|
||||
```
|
||||
|
||||
|
||||
|
||||
**Expected Results:**
|
||||
- ✅ Successful cloning with path confirmation
|
||||
- ✅ Automatic VS Code launch
|
||||
- ✅ Clear error messages for invalid scenarios
|
||||
- ✅ Proper handling of edge cases
|
||||
|
||||
#### 4b. Test in MCP Inspector
|
||||
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
**🎉 Congratulations!** You've successfully created a practical, production-ready MCP server that solves real development workflow challenges. Your custom GitHub clone server demonstrates the power of MCP for automating and enhancing developer productivity.
|
||||
|
||||
### 🏆 Achievement Unlocked:
|
||||
- ✅ **MCP Developer** - Created custom MCP server
|
||||
- ✅ **Workflow Automator** - Streamlined development processes
|
||||
- ✅ **Integration Expert** - Connected multiple development tools
|
||||
- ✅ **Production Ready** - Built deployable solutions
|
||||
|
||||
---
|
||||
|
||||
## 🎓 Workshop Completion: Your Journey with Model Context Protocol
|
||||
|
||||
**Dear Workshop Participant,**
|
||||
|
||||
Congratulations on completing all four modules of the Model Context Protocol workshop! You've come a long way from understanding basic Microsoft Foundry Toolkit concepts to building production-ready MCP servers that solve real-world development challenges.
|
||||
|
||||
### 🚀 Your Learning Path Recap:
|
||||
|
||||
**[Module 1](../lab1/README.md)**: You began by exploring Microsoft Foundry Toolkit fundamentals, model testing, and creating your first AI agent.
|
||||
|
||||
**[Module 2](../lab2/README.md)**: You learned MCP architecture, integrated Playwright MCP, and built your first browser automation agent.
|
||||
|
||||
**[Module 3](../lab3/README.md)**: You advanced to custom MCP server development with the Weather MCP server and mastered debugging tools.
|
||||
|
||||
**[Module 4](../lab4/README.md)**: You've now applied everything to create a practical GitHub repository workflow automation tool.
|
||||
|
||||
### 🌟 What You've Mastered:
|
||||
|
||||
- ✅ **Microsoft Foundry Toolkit Ecosystem**: Models, agents, and integration patterns
|
||||
- ✅ **MCP Architecture**: Client-server design, transport protocols, and security
|
||||
- ✅ **Developer Tools**: From Playground to Inspector to production deployment
|
||||
- ✅ **Custom Development**: Building, testing, and deploying your own MCP servers
|
||||
- ✅ **Practical Applications**: Solving real-world workflow challenges with AI
|
||||
|
||||
### 🔮 Your Next Steps:
|
||||
|
||||
1. **Build Your Own MCP Server**: Apply these skills to automate your unique workflows
|
||||
2. **Join the MCP Community**: Share your creations and learn from others
|
||||
3. **Explore Advanced Integration**: Connect MCP servers to enterprise systems
|
||||
4. **Contribute to Open Source**: Help improve MCP tooling and documentation
|
||||
|
||||
Remember, this workshop is just the beginning. The Model Context Protocol ecosystem is rapidly evolving, and you're now equipped to be at the forefront of AI-powered development tools.
|
||||
|
||||
**Thank you for your participation and dedication to learning!**
|
||||
|
||||
We hope this workshop has sparked ideas that will transform how you build and interact with AI tools in your development journey.
|
||||
|
||||
**Happy coding!**
|
||||
|
||||
---
|
||||
|
||||
## What's Next
|
||||
|
||||
Congratulations on completing all labs in Module 10!
|
||||
|
||||
- Back to: [Module 10 Overview](../README.md)
|
||||
- Continue to: [Module 11: MCP Server Hands-On Labs](../../11-MCPServerHandsOnLabs/README.md)
|
||||
|
||||
|
||||
+7
@@ -0,0 +1,7 @@
|
||||
# python virtual environment
|
||||
.venv/
|
||||
__pycache__/
|
||||
*.egg-info/
|
||||
|
||||
# others
|
||||
node_modules/
|
||||
+1
@@ -0,0 +1 @@
|
||||
3.10
|
||||
+103
@@ -0,0 +1,103 @@
|
||||
# Weather MCP Server
|
||||
|
||||
This is a sample MCP Server in Python implementing weather tools with mock responses. It can be used as a scaffold for your own MCP Server. It includes the following features:
|
||||
|
||||
- **Weather Tool**: A tool that provides mocked weather information based on the given location.
|
||||
- **Git Clone Tool**: A tool that clones a git repository to a specified folder.
|
||||
- **VS Code Open Tool**: A tool that opens a folder in VS Code or VS Code Insiders.
|
||||
- **Connect to Agent Builder**: A feature that allows you to connect the MCP server to the Agent Builder for testing and debugging.
|
||||
- **Debug in [MCP Inspector](https://github.com/modelcontextprotocol/inspector)**: A feature that allows you to debug the MCP Server using the MCP Inspector.
|
||||
|
||||
## Get started with the Weather MCP Server template
|
||||
|
||||
> **Prerequisites**
|
||||
>
|
||||
> To run the MCP Server in your local dev machine, you will need:
|
||||
>
|
||||
> - [Python](https://www.python.org/)
|
||||
> - [Git](https://git-scm.com/) (Required for git_clone_repo tool)
|
||||
> - [VS Code](https://code.visualstudio.com/) or [VS Code Insiders](https://code.visualstudio.com/insiders/) (Required for open_in_vscode tool)
|
||||
> - (*Optional - if you prefer uv*) [uv](https://github.com/astral-sh/uv)
|
||||
> - [Python Debugger Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.debugpy)
|
||||
|
||||
## Prepare environment
|
||||
|
||||
There are two approaches to set up the environment for this project. You can choose either one based on your preference.
|
||||
|
||||
> Note: Reload VSCode or terminal to ensure the virtual environment python is used after creating the virtual environment.
|
||||
|
||||
| Approach | Steps |
|
||||
| -------- | ----- |
|
||||
| Using `uv` | 1. Create virtual environment: `uv venv` <br>2. Run VSCode Command "***Python: Select Interpreter***" and select the python from created virtual environment <br>3. Install dependencies (include dev dependencies): `uv pip install -r pyproject.toml --extra dev` |
|
||||
| Using `pip` | 1. Create virtual environment: `python -m venv .venv` <br>2. Run VSCode Command "***Python: Select Interpreter***" and select the python from created virtual environment<br>3. Install dependencies (include dev dependencies): `pip install -e .[dev]` |
|
||||
|
||||
After setting up the environment, you can run the server in your local dev machine via Agent Builder as the MCP Client to get started:
|
||||
1. Open VS Code Debug panel. Select `Debug in Agent Builder` or press `F5` to start debugging the MCP server.
|
||||
2. Use AI Toolkit Agent Builder to test the server with [this prompt](vscode://ms-windows-ai-studio.windows-ai-studio/open_prompt_builder?model_id=github/gpt-4.1-mini&system_prompt=You%20are%20a%20weather%20forecast%20professional%20that%20can%20tell%20weather%20information%20based%20on%20given%20location&user_prompt=What%20is%20the%20weather%20in%20Shanghai?&track_from=vsc_md&mcp=github_mcp_server). Server will be auto-connected to the Agent Builder.
|
||||
3. Click `Run` to test the server with the prompt.
|
||||
|
||||
**Congratulations**! You have successfully run the Weather MCP Server in your local dev machine via Agent Builder as the MCP Client.
|
||||
|
||||
|
||||
## What's included in the template
|
||||
|
||||
| Folder / File| Contents |
|
||||
| ------------ | -------------------------------------------- |
|
||||
| `.vscode` | VSCode files for debugging |
|
||||
| `.aitk` | Configurations for AI Toolkit |
|
||||
| `src` | The source code for the weather mcp server |
|
||||
|
||||
## How to debug the Weather MCP Server
|
||||
|
||||
> Notes:
|
||||
> - [MCP Inspector](https://github.com/modelcontextprotocol/inspector) is a visual developer tool for testing and debugging MCP servers.
|
||||
> - All debugging modes support breakpoints, so you can add breakpoints to the tool implementation code.
|
||||
|
||||
## Available Tools
|
||||
|
||||
### Weather Tool
|
||||
The `get_weather` tool provides mocked weather information for a specified location.
|
||||
|
||||
| Parameter | Type | Description |
|
||||
| --------- | ---- | ----------- |
|
||||
| `location` | string | Location to get weather for (e.g., city name, state, or coordinates) |
|
||||
|
||||
### Git Clone Tool
|
||||
The `git_clone_repo` tool clones a git repository to a specified folder.
|
||||
|
||||
| Parameter | Type | Description |
|
||||
| --------- | ---- | ----------- |
|
||||
| `repo_url` | string | URL of the git repository to clone |
|
||||
| `target_folder` | string | Path to the folder where the repository should be cloned |
|
||||
|
||||
The tool returns a JSON object with:
|
||||
- `success`: Boolean indicating if the operation was successful
|
||||
- `target_folder` or `error`: The path of the cloned repository or an error message
|
||||
|
||||
### VS Code Open Tool
|
||||
The `open_in_vscode` tool opens a folder in VS Code or VS Code Insiders application.
|
||||
|
||||
| Parameter | Type | Description |
|
||||
| --------- | ---- | ----------- |
|
||||
| `folder_path` | string | Path to the folder to open |
|
||||
| `use_insiders` | boolean (optional) | Whether to use VS Code Insiders instead of regular VS Code |
|
||||
|
||||
The tool returns a JSON object with:
|
||||
- `success`: Boolean indicating if the operation was successful
|
||||
- `message` or `error`: A confirmation message or an error message
|
||||
|
||||
| Debug Mode | Description | Steps to debug |
|
||||
| ---------- | ----------- | --------------- |
|
||||
| Agent Builder | Debug the MCP server in the Agent Builder via AI Toolkit. | 1. Open VS Code Debug panel. Select `Debug in Agent Builder` and press `F5` to start debugging the MCP server.<br>2. Use AI Toolkit Agent Builder to test the server with [this prompt](vscode://ms-windows-ai-studio.windows-ai-studio/open_prompt_builder?model_id=github/gpt-4.1-mini&system_prompt=You%20are%20a%20weather%20forecast%20professional%20that%20can%20tell%20weather%20information%20based%20on%20given%20location&user_prompt=What%20is%20the%20weather%20in%20Shanghai?&track_from=vsc_md&mcp=github_mcp_server). Server will be auto-connected to the Agent Builder.<br>3. Click `Run` to test the server with the prompt. |
|
||||
| MCP Inspector | Debug the MCP server using the MCP Inspector. | 1. Install [Node.js](https://nodejs.org/)<br> 2. Set up Inspector: `cd inspector` && `npm install` <br> 3. Open VS Code Debug panel. Select `Debug SSE in Inspector (Edge)` or `Debug SSE in Inspector (Chrome)`. Press F5 to start debugging.<br> 4. When MCP Inspector launches in the browser, click the `Connect` button to connect this MCP server.<br> 5. Then you can `List Tools`, select a tool, input parameters, and `Run Tool` to debug your server code.<br> |
|
||||
|
||||
## Default Ports and customizations
|
||||
|
||||
| Debug Mode | Ports | Definitions | Customizations | Note |
|
||||
| ---------- | ----- | ------------ | -------------- |-------------- |
|
||||
| Agent Builder | 3001 | [tasks.json](.vscode/tasks.json) | Edit [launch.json](.vscode/launch.json), [tasks.json](.vscode/tasks.json), [\_\_init\_\_.py](src/__init__.py), [mcp.json](.aitk/mcp.json) to change above ports. | N/A |
|
||||
| MCP Inspector | 3001 (Server); 5173 and 3000 (Inspector) | [tasks.json](.vscode/tasks.json) | Edit [launch.json](.vscode/launch.json), [tasks.json](.vscode/tasks.json), [\_\_init\_\_.py](src/__init__.py), [mcp.json](.aitk/mcp.json) to change above ports.| N/A |
|
||||
|
||||
## Feedback
|
||||
|
||||
If you have any feedback or suggestions for this template, please open an issue on the [AI Toolkit GitHub repository](https://github.com/microsoft/vscode-ai-toolkit/issues)
|
||||
+3814
File diff suppressed because it is too large
Load Diff
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"name": "inspector-placeholder",
|
||||
"version": "0.0.1",
|
||||
"description": "This is just a placeholder to launch the inspector.",
|
||||
"scripts": {
|
||||
"dev:inspector": "mcp-inspector"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@modelcontextprotocol/inspector": "0.22.0"
|
||||
},
|
||||
"overrides": {
|
||||
"shell-quote": "1.8.4"
|
||||
}
|
||||
}
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
[project]
|
||||
name = "github_mcp_server"
|
||||
version = "0.1.0"
|
||||
description = "A simple MCP weather server"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"mcp>=1.26.0"
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = ["debugpy==1.8.8"]
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
import os
|
||||
import sys
|
||||
from server import server
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""Main entry point"""
|
||||
transport_type = sys.argv[1] if len(sys.argv) > 1 else None
|
||||
server.settings.log_level = os.environ.get("LOG_LEVEL", "DEBUG")
|
||||
if transport_type == "sse":
|
||||
port = int(os.environ.get("PORT", 3001))
|
||||
server.settings.port = port
|
||||
server.settings.host = "127.0.0.1"
|
||||
server.run(transport="sse")
|
||||
elif transport_type == "stdio":
|
||||
server.run(transport="stdio")
|
||||
else:
|
||||
print("Invalid transport type. Use 'sse' or 'stdio'.")
|
||||
sys.exit(1)
|
||||
+167
@@ -0,0 +1,167 @@
|
||||
import json
|
||||
import random
|
||||
import os
|
||||
import platform
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
from mcp.server.fastmcp import FastMCP
|
||||
|
||||
# Initialize FastMCP server
|
||||
server = FastMCP("github_mcp_server")
|
||||
|
||||
@server.tool()
|
||||
async def get_weather(location: str) -> str:
|
||||
"""Get weather for a location.
|
||||
|
||||
Args:
|
||||
location: Location to get weather for, e.g., city name, state, or coordinates
|
||||
|
||||
"""
|
||||
if not location:
|
||||
return "Location is required."
|
||||
|
||||
# mock weather data
|
||||
conditions = [ "Sunny", "Rainy", "Cloudy", "Snowy" ]
|
||||
weather = {
|
||||
"location": location,
|
||||
"temperature": f"{random.randint(10, 90)}°F",
|
||||
"condition": random.choice(conditions),
|
||||
}
|
||||
return json.dumps(weather, ensure_ascii=False)
|
||||
|
||||
@server.tool()
|
||||
async def git_clone_repo(repo_url: str, target_folder: str) -> str:
|
||||
"""Clone a git repository to a specified folder.
|
||||
|
||||
Args:
|
||||
repo_url: URL of the git repository to clone
|
||||
target_folder: Path to the folder where the repository should be cloned
|
||||
"""
|
||||
# Check if target folder exists
|
||||
target_path = Path(target_folder).expanduser().absolute()
|
||||
if target_path.exists():
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": f"Target folder already exists: {str(target_path)}"
|
||||
})
|
||||
|
||||
# Check if git is installed
|
||||
try:
|
||||
subprocess.run(["git", "--version"], check=True, capture_output=True, text=True)
|
||||
except FileNotFoundError:
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": "Git is not installed. Please install Git first."
|
||||
})
|
||||
except subprocess.CalledProcessError:
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": "Error checking Git installation."
|
||||
})
|
||||
|
||||
# Create parent directory if it doesn't exist
|
||||
target_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Clone the repository
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["git", "clone", repo_url, str(target_path)],
|
||||
check=True,
|
||||
capture_output=True,
|
||||
text=True
|
||||
)
|
||||
return json.dumps({
|
||||
"success": True,
|
||||
"target_folder": str(target_path)
|
||||
})
|
||||
except subprocess.CalledProcessError as e:
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": f"Git clone failed: {e.stderr}"
|
||||
})
|
||||
|
||||
@server.tool()
|
||||
async def open_in_vscode(folder_path: str, use_insiders: bool = False) -> str:
|
||||
"""Open a folder in VS Code or VS Code Insiders application.
|
||||
|
||||
Args:
|
||||
folder_path: Path to the folder to open
|
||||
use_insiders: Whether to use VS Code Insiders instead of regular VS Code
|
||||
"""
|
||||
folder_path = Path(folder_path).expanduser().absolute()
|
||||
|
||||
if not folder_path.exists():
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": f"Folder does not exist: {str(folder_path)}"
|
||||
})
|
||||
|
||||
# Determine the command based on OS and whether to use Insiders
|
||||
system = platform.system()
|
||||
|
||||
try:
|
||||
if system == "Darwin": # macOS
|
||||
app_name = "Visual Studio Code - Insiders" if use_insiders else "Visual Studio Code"
|
||||
subprocess.run(["open", "-a", app_name, str(folder_path)], check=True)
|
||||
elif system == "Windows":
|
||||
# On Windows, use the start command to open the application
|
||||
vscode_path = os.path.join(os.environ.get("LOCALAPPDATA", ""),
|
||||
"Programs",
|
||||
"Microsoft VS Code Insiders" if use_insiders else "Microsoft VS Code",
|
||||
"Code.exe")
|
||||
|
||||
# Check if VS Code exists at the expected path
|
||||
if not os.path.exists(vscode_path):
|
||||
# Try alternative installation paths
|
||||
program_files = os.environ.get("ProgramFiles", "C:\\Program Files")
|
||||
program_files_x86 = os.environ.get("ProgramFiles(x86)", "C:\\Program Files (x86)")
|
||||
|
||||
alt_paths = [
|
||||
os.path.join(program_files, "Microsoft VS Code" + (" Insiders" if use_insiders else ""), "Code.exe"),
|
||||
os.path.join(program_files_x86, "Microsoft VS Code" + (" Insiders" if use_insiders else ""), "Code.exe")
|
||||
]
|
||||
|
||||
for path in alt_paths:
|
||||
if os.path.exists(path):
|
||||
vscode_path = path
|
||||
break
|
||||
|
||||
if os.path.exists(vscode_path):
|
||||
# Launch the resolved executable directly (no shell) to avoid
|
||||
# command injection via shell metacharacters in folder_path.
|
||||
subprocess.run([vscode_path, str(folder_path)], check=True)
|
||||
else:
|
||||
# Fallback to command line approach
|
||||
cmd = "code-insiders" if use_insiders else "code"
|
||||
subprocess.run([cmd, str(folder_path)], check=True)
|
||||
|
||||
elif system == "Linux":
|
||||
# On Linux, try to use the desktop application
|
||||
app_name = "code-insiders" if use_insiders else "code"
|
||||
|
||||
# Try using xdg-open which should use the desktop application
|
||||
try:
|
||||
subprocess.run(["xdg-open", str(folder_path)], check=True)
|
||||
except (FileNotFoundError, subprocess.CalledProcessError):
|
||||
# Fallback to direct command
|
||||
subprocess.run([app_name, str(folder_path)], check=True)
|
||||
else:
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": f"Unsupported operating system: {system}"
|
||||
})
|
||||
|
||||
return json.dumps({
|
||||
"success": True,
|
||||
"message": f"Opened {str(folder_path)} in {'VS Code Insiders' if use_insiders else 'VS Code'}"
|
||||
})
|
||||
except subprocess.CalledProcessError as e:
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": f"Failed to open VS Code: {str(e)}"
|
||||
})
|
||||
except FileNotFoundError:
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": f"{'VS Code Insiders' if use_insiders else 'VS Code'} is not installed or not in PATH"
|
||||
})
|
||||
Generated
+759
@@ -0,0 +1,759 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.10"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "exceptiongroup", marker = "python_full_version < '3.11'" },
|
||||
{ name = "idna" },
|
||||
{ name = "sniffio" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949, upload-time = "2025-03-17T00:02:54.77Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "25.4.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/6b/5c/685e6633917e101e5dcb62b9dd76946cbb57c26e133bae9e0cd36033c0a9/attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11", size = 934251, upload-time = "2025-10-06T13:54:44.725Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.4.26"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6", size = 160705, upload-time = "2025-04-26T02:12:29.51Z" }
|
||||
wheels = [
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Reference in New Issue
Block a user