Github Copilot Mcp Server Workshop

Open in GitHub Codespaces Deploy to Azure

🚀 Overview

Welcome to this comprehensive workshop where you'll learn to extend GitHub Copilot's capabilities by building and deploying a custom Model Context Protocol (MCP) server on Azure Functions. This workshop demonstrates the complete journey from local development to production AI integration.

What You'll Build

• Custom MCP Server: A serverless MCP server with three types of tools
• Educational Tools: Markdown review and dependency checking with local algorithms
• Production AI Tool: Azure AI-powered code review demonstrating true MCP architecture
• Azure Functions Deployment: Scalable, serverless hosting for your MCP server
• GitHub Copilot Integration: Seamless connection between Copilot and your custom tools

Learning Outcomes

By the end of this workshop, you'll understand:

• ✅ The difference between educational and production MCP tools
• ✅ How to build and deploy serverless MCP servers on Azure Functions
• ✅ True MCP architecture: tools provide context, AI provides intelligence
• ✅ GitHub Copilot integration patterns and best practices
• ✅ Azure AI Foundry integration with graceful fallback patterns

⚡ How to start! Quick Start Options:

🌟 Option 1: GitHub Codespaces (Recommended - Zero Setup)

Click the "Open in GitHub Codespaces" badge above for instant setup!

• ✅ No local installation required
• ✅ Pre-configured Linux environment with all tools
• ✅ Works on any device with a browser
• ✅ Ready in 2-3 minutes
• 📖 Follow the Linux/Bash Documentation Path

💻 Option 2: Local Development

Choose your platform for local development:

🐧 Linux/macOS (Bash)

• 📖 Linux Workshop Documentation - Complete Linux setup with Bash commands
• 🛠️ Requirements: Node.js, Azure CLI, Azure Functions Core Tools, VS Code
• 💡 Best for: Linux/macOS developers, Bash users, script automation

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📚 Choose Your Workshop Path

Platform	Quick Start	Documentation	Best For
🌟 Codespaces	Open in GitHub Codespaces	📖 Linux/Bash Docs	Zero setup, any device
🐧 Linux/macOS	Setup Guide	📖 Linux/Bash Docs	Linux/macOS developers

🛠️ Prerequisites

Required Software

• Node.js (v18 or later)
• Azure CLI
• Azure Functions Core Tools
• GitHub Copilot subscription
• VS Code with Copilot extension

Azure Account

• Azure subscription (free tier works!)
• Contributor access to create resources

Knowledge Level

• Intermediate JavaScript/TypeScript
• Basic Azure Functions knowledge
• Familiarity with GitHub Copilot

📋 Workshop Flow (3 Hours Total)

Choose your platform path above, then follow these sequential steps:

[Part 1: Setup and Understanding] (30 minutes)

1. Understanding MCP and Architecture Patterns
2. Environment Setup and Prerequisites
3. Project Structure and Dependencies

[Part 2: Local Development] (45 minutes)

1. Building the MCP Server Core
2. Creating Educational Tools (markdown review, dependency check)
3. Implementing Production AI Tool Architecture
4. Testing Locally with Azure Functions

[Part 3: Azure Deployment] (30 minutes)

1. Infrastructure as Code with Bicep
2. Deploying to Azure Functions
3. Monitoring and Troubleshooting

[Part 4: GitHub Copilot Integration] (30 minutes)

1. Configuring MCP in VS Code
2. Testing Tool Discovery and Usage
3. Advanced Integration Patterns

[Part 5: AI Integration](45 minutes) 🤖

1. Setting Up Azure AI Foundry
2. Implementing Real AI Analysis
3. Comparing Educational vs Production Tools
4. Understanding True MCP Architecture

🏗️ Architecture Overview

graph LR
    A[GitHub Copilot] -->|MCP Protocol| B[Azure Functions]
    B --> C[Markdown Tool - Educational]
    B --> D[Dependency Tool - Educational]
    B --> E[AI Code Review - Production]
    
    E -->|API Calls| F[Azure AI Foundry]
    B --> G[Azure Monitor]
    B --> H[Azure Key Vault]
    B --> I[Azure Storage]

Tool Categories

This workshop demonstrates three distinct tool patterns:

🎓 Educational Tools

• markdown_review: Local analysis algorithms, quality scoring
• dependency_check: Static package analysis, security checks
• Purpose: Learn MCP concepts, no external dependencies

🚀 Production Tools

• ai_code_review: Azure AI integration with intelligent analysis
• Purpose: Demonstrate true MCP architecture (tools provide context, AI provides intelligence)
• Features: Real LLM analysis, graceful fallback to mock analysis

🔄 Hybrid Approach

All tools work without Azure costs (minor GPT3.5 cost($.10) if desired) through intelligent fallback patterns, making the workshop accessible to everyone while demonstrating production capabilities.

🎯 Key Learning Outcomes

Technical Skills

• ✅ MCP protocol implementation with JSON-RPC 2.0
• ✅ Azure Functions serverless development
• ✅ TypeScript development with Azure tooling
• ✅ Infrastructure as Code with Bicep
• ✅ Azure AI service integration

Architectural Understanding

• ✅ Educational vs Production MCP tool patterns
• ✅ Graceful degradation and fallback strategies
• ✅ True MCP architecture: tools provide context, AI provides intelligence
• ✅ Serverless cost optimization strategies
• ✅ Security considerations for production MCP servers

GitHub Copilot Integration

• ✅ MCP server configuration in VS Code
• ✅ Tool discovery and usage patterns
• ✅ Advanced integration scenarios
• ✅ Troubleshooting and monitoring

🏁 Workshop Navigation

🌟 Codespaces Users (Recommended)

Start Here: Part 1: Setup and Understanding

🐧 Linux/macOS Users

Start Here: Part 1: Setup and Understanding

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🌟 What Makes This Workshop Special

1. Progressive Complexity: From simple local tools to production AI integration
2. Cost Conscious: Carefully curated services for lowest possible cost.
3. Real-World Ready: Production patterns with security considerations
4. Complete Coverage: Local development → Azure deployment → Copilot integration → AI enhancement
5. Hands-On Testing: Comprehensive test scripts for every stage

🤝 Support and Contributing

• Issues: Found a bug? Open an issue
• Discussions: Questions? Start a discussion
• Contributing: See CONTRIBUTING.md for guidelines

📄 License

MIT License - see LICENSE file for details.

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