How to Register and Prepare for the GitHub Copilot Exam Step-by-Step Guide
If you’re planning to validate your AI-assisted coding skills, the GitHub Copilot Certification Exam is a great way to prove your expertise. This guide walks you through everything — from registration to preparation — so you can approach exam day with confidence. https://dellenny.com/how-to-register-and-prepare-for-the-github-copilot-exam-step-by-step-guide/Writing Cleaner Code with GitHub Copilot Suggestions
In today’s fast-moving software development world, maintaining readable, maintainable, and reliable code is more crucial than ever. Tools like GitHub Copilot offer developers a new way to assist in that journey—by suggesting code, refactorings, tests, and improvements in real time. In this blog, we’ll explore how to use Copilot (and its suggestions) not just for speed—but for cleaner code. https://dellenny.com/writing-cleaner-code-with-github-copilot-suggestions/Implementing MCP Remote Servers with Azure Function App and GitHub Copilot Integration
Introduction In the evolving landscape of AI-driven applications, the ability to seamlessly connect large language models (LLMs) with external tools and data sources is becoming a cornerstone of intelligent system design. Model Context Protocol (MCP) — a specification that enables AI agents to discover and invoke tools dynamically, based on context. While MCP is powerful, implementing it from scratch can be daunting !!! That’s where Azure Functions comes in handy. With its event-driven, serverless architecture, Azure Functions now supports a preview extension for MCP, allowing developers to build remote MCP servers that are scalable, secure, and cloud-native. Further, In VS Code, GitHub Copilot Chat in Agent Mode can connect to your deployed Azure Function App acting as an MCP server. This connection allows Copilot to leverage the tools and services exposed by your function app. Why Use Azure Functions for MCP? Serverless Simplicity: Deploy MCP endpoints without managing infrastructure. Secure by Design: Leverage HTTPS, system keys, and OAuth via EasyAuth or API Management. Language Flexibility: Build in .NET, Python, or Node.js using QuickStart templates. AI Integration: Enable GitHub Copilot, VS Code, or other AI agents to invoke your tools via SSE endpoints. Prerequisites Python version 3.11 or higher Azure Functions Core Tools >= 4.0.7030 Azure Developer CLI To use Visual Studio Code to run and debug locally: Visual Studio Code Azure Functions extension An storage emulator is needed when developing azure function app in VScode. you can deploy Azurite extension in VScode to meet this requirement. Press enter or click to view image in full size You can run the Azurite in VS Code as shown below. C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\IDE\Extensions\Microsoft\Azure Storage Emulator> .\azurite.exe Press enter or click to view image in full size alternatively, you can also run Azurite in docker container as shown below. docker run -p 10000:10000 -p 10001:10001 -p 10002:10002 \ mcr.microsoft.com/azure-storage/azurite For more information about setting up Azurite, visit Use Azurite emulator for local Azure Storage development | Microsoft Learn Github Repositories Following Github repos are needed to setup this PoC. Repository for MCP server using Azure Function App https://github.com/mafzal786/mcp-azure-functions-python.git Repository for AI Foundry agent as MCP Client https://github.com/mafzal786/ai-foundry-agent-with-remote-mcp-using-azure-functionapp.git Clone the repository Run the following command to clone the repository to start building your MCP server using Azure function app. git clone https://github.com/mafzal786/mcp-azure-functions-python.git Run the MCP server in VS Code Once cloned. Open the folder in VS Code. Create a virtual environment in VS Code. Change directory to “src” in a new terminal window, install the python dependencies and start the function host locally as shown below. cd src pip install -r requirements.txt func start Note: by default this will use the webhooks route: /runtime/webhooks/mcp/sse. Later we will use this in Azure to set the key on client/host calls: /runtime/webhooks/mcp/sse?code=<system_key> Press enter or click to view image in full size MCP Inspector In a new terminal window, install and run MCP Inspector. npx @modelcontextprotocol/inspector Click to load the MCP inspector. Also provide the generated proxy session token. http://127.0.0.1:6274/#resources In the URL type and click “Connect”: http://localhost:7071/runtime/webhooks/mcp/sse Once connected, click List Tools under Tools and select “hello_mcp” tool and click “Run Tool” for testing as shown below. Press enter or click to view image in full size Select another tool such as get_stockprice and run it as shown below. Press enter or click to view image in full size Deploy Function App to Azure from VS Code For deploying function app to azure from vs code, make sure you have Azure Tools extension enabled in VS Code. To learn more about Azure Tools extension, visit the following Azure Extensions if your VS code environment is not setup for Azure development, follow Configure Visual Studio Code for Azure development with .NET — .NET | Microsoft Learn Once Azure Tools are setup, sign in to Azure account with Azure Tools Press enter or click to view image in full size Once Sign-in is completed, you should be able to see all of your existing resources in the Resources view. These resources can be managed directly in VS Code. Look for Function App in Resource, right click and click “Deploy to Function App”. Press enter or click to view image in full size If you already have it deployed, you will get the following pop-up. Click “Deploy” Press enter or click to view image in full size This will start deploying your function app to Azure. In VS Code, Azure tab will display the following. Press enter or click to view image in full size Once the deployment is completed, you can view the function app and all the tools in Azure portal under function app as shown below. Press enter or click to view image in full size Get the mcp_extension key from Functions → App Keys in Function App. Press enter or click to view image in full size This mcp_extension key would be needed in mcp.json file in VS code, if you would like to test the MCP server using Github Copilot in VS Code. Your entries in mcp.json file will look like as below for example. { "inputs": [ { "type": "promptString", "id": "functions-mcp-extension-system-key", "description": "Azure Functions MCP Extension System Key", "password": true }, { "type": "promptString", "id": "functionapp-name", "description": "Azure Functions App Name" } ], "servers": { "remote-mcp-function": { "type": "sse", "url": "https://${input:functionapp-name}.azurewebsites.net/runtime/webhooks/mcp/sse", "headers": { "x-functions-key": "${input:functions-mcp-extension-system-key}" } }, "local-mcp-function": { "type": "sse", "url": "http://0.0.0.0:7071/runtime/webhooks/mcp/sse" } } } Test Azure Function MCP Server in MCP Inspector Launch MCP Inspector and provide the Azure Function in MCP inspector URL. Provide authentication as shown below. Bearer token is mcp_extension key. Testing an MCP server with GitHub Copilot Testing an MCP server with GitHub Copilot involves configuring and utilizing the server within your development environment to provide enhanced context and capabilities to Copilot Chat. Steps to Test an MCP Server with GitHub Copilot: Ensure Agent Mode is Enabled: Open Copilot Chat in Visual Studio Code and select “Agent” mode. This mode allows Copilot to interact with external tools and services, including MCP servers. Add the MCP Server: Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P) and run the command MCP: Add Server. Press enter or click to view image in full size Follow the prompts to configure the server. You can choose to add it to your workspace settings (creating a .vscode/mcp.json file) . Select HTTP or Server-Sent events Press enter or click to view image in full size Specify the URL and click Enter Press enter or click to view image in full size Provide a name of your choice Press enter or click to view image in full size Select scope as Global or workspace. I selected Workspace Press enter or click to view image in full size This will generate mcp.json file in .vscode or create a new entry if mcp.json already exists as shown below. Click Start to “start” the server. Also make sure your Azure function app is locally running with func start command. Press enter or click to view image in full size Now Type the prompt as shown below. Press enter or click to view image in full size Try another tool as below. Press enter or click to view image in full size VS code terminal output for reference. Press enter or click to view image in full size Testing an MCP server with Claude Desktop Claude Desktop is a standalone AI application that allows users to interact with Claude AI models directly from their desktop, providing a seamless and efficient experience. you can download Claude desktop at Download Claude In this article, I have added another tool to utilize to test your MCP server running in Azure Function app. Modify claude_desktop_config.json with the following. you can find this file in window environment at C:\Users\<username>\AppData\Roaming\Claude { "mcpServers": { "my mcp": { "command": "npx", "args": [ "mcp-remote", "http://localhost:7071/runtime/webhooks/mcp/sse" ] } } } Note: If claude_desktop_config.json does not exists, click on setting in Claude desktop under user and visit developer tab. You will see you MCP server in Claude Desktop as shown below. Press enter or click to view image in full size Type the prompt such as “What is the stock price of Tesla” . After submitting, you will notice that it is invoking the tool “get_stockprice” from the MCP server running locally and configured in the .json earlier. Click Allow once or Allow always as shown below. Following output will be displayed. Press enter or click to view image in full size Now lets try weather related prompt. As you can see, it has invoked “get_weatheralerts” tool from MCP server. Press enter or click to view image in full size Azure AI Foundry agent as MCP Client Use the following Github repo to set up Azure AI Foundry agent as MCP client. git clone https://github.com/mafzal786/ai-foundry-agent-with-remote-mcp-using-azure-functionapp.git Open the code in VS code and follow the instructions mentioned in README.md file at Github repo. Once you execute the code, following output will show up in VS code. Press enter or click to view image in full size In this code, message is hard coded. Change the content to “what is weather advisory for Florida” and rerun the program. It will call get_weatheralerts tool and output will look like as below. Press enter or click to view image in full size Conclusion The integration of Model Context Protocol (MCP) with Azure Functions marks a pivotal step in democratizing AI agent development. By leveraging Azure’s serverless architecture, developers can now build remote MCP servers that scale automatically, integrate seamlessly with other Azure services, and expose modular tools to intelligent agents like GitHub Copilot. This setup not only simplifies the deployment and management of MCP servers but also enhances the developer experience — allowing tools to be invoked contextually by AI agents in environments like VS Code, GitHub Codespaces, or Copilot Studio[2]. Whether you’re building a tool to query logs, calculate metrics, or manage data, Azure Functions provides the flexibility, security, and scalability needed to bring your AI-powered workflows to life. As the MCP spec continues to evolve, and GitHub Copilot expands its agentic capabilities, this architecture positions you to stay ahead — offering a robust foundation for cloud-native AI tooling that’s both powerful and future-proof.What Is the GitHub Copilot Certification and Why It Matters for Developers
In the ever-evolving world of software development, AI-powered tools are taking centre stage. Among them, GitHub Copilot has become a significant player. But what exactly is the GitHub Copilot Certification, and why should developers care? Let’s dive in. https://dellenny.com/what-is-the-github-copilot-certification-and-why-it-matters-for-developers/Study Buddy: Learning Data Science and Machine Learning with an AI Sidekick
If you've ever wished for a friendly companion to guide you through the world of data science and machine learning, you're not alone. As part of the "For Beginners" curriculum, I recently built a Study Buddy Agent, an AI-powered assistant designed to help learners explore data science interactively, intuitively, and joyfully. Why a Study Buddy? Learning something new can be overwhelming, especially when you're navigating complex topics like machine learning, statistics, or Python programming. The Study Buddy Agent is here to change that. It brings the curriculum to life by answering questions, offering explanations, and nudging learners toward deeper understanding, all in a conversational format. Think of it as your AI-powered lab partner: always available, never judgmental, and endlessly curious. Built with chatmodes, Powered by Purpose The agent lives inside a .chatmodes file in the https://github.com/microsoft/Data-Science-For-Beginners/blob/main/.github/chatmodes/study-mode.chatmode.md. This file defines how the agent behaves, what tone it uses, and how it interacts with learners. I designed it to be friendly, encouraging, and beginner-first—just like the curriculum itself. It’s not just about answering questions. The Study Buddy is trained to: Reinforce key concepts from the curriculum Offer hints and nudges when learners get stuck Encourage exploration and experimentation Celebrate progress and milestones What’s Under the Hood? The agent uses GitHub Copilot's chatmode, which allows developers to define custom behaviors for AI agents. By aligning the agent’s responses with the curriculum’s learning objectives, we ensure that learners stay on track while enjoying the flexibility of conversational learning. How You Can Use It YouTube Video here: Study Buddy - Data Science AI Sidekick Clone the repo: Head to the https://github.com/microsoft/Data-Science-For-Beginners and clone it locally or use Codespaces. Open the GitHub Copilot Chat, and select Study Buddy: This will activate the Study Buddy. Start chatting: Ask questions, explore topics, and let the agent guide you. What’s Next? This is just the beginning. I’m exploring ways to: Expand the agent to other beginner curriculums (Web Dev, AI, IoT) Integrate feedback loops so learners can shape the agent’s evolution Final Thoughts In my role, I believe learning should be inclusive, empowering, and fun. The Study Buddy Agent is a small step toward that vision, a way to make data science feel less like a mountain and more like a hike with a good friend. Try it out, share your feedback, and let’s keep building tools that make learning magical. Join us on Discord to share your feedback.The Future of AI: Reduce AI Provisioning Effort - Jumpstart your solutions with AI App Templates
In the previous post, we introduced Contoso Chat – an open-source RAG-based retail chat sample for Azure AI Foundry, that serves as both an AI App template (for builders) and the basis for a hands-on workshop (for learners). And we briefly talked about five stages in the developer workflow (provision, setup, ideate, evaluate, deploy) that take them from the initial prompt to a deployed product. But how can that sample help you build your app? The answer lies in developer tools and AI App templates that jumpstart productivity by giving you a fast start and a solid foundation to build on. In this post, we answer that question with a closer look at Azure AI App templates - what they are, and how we can jumpstart our productivity with a reuse-and-extend approach that builds on open-source samples for core application architectures.The Future of AI: Developing Lacuna - an agent for Revealing Quiet Assumptions in Product Design
A conversational agent named Lacuna is helping product teams uncover hidden assumptions embedded in design decisions. Built with Copilot Studio and powered by Azure AI Foundry, Lacuna analyzes product documents to identify speculative beliefs and assess their risk using design analysis lenses: impact, confidence, and reversibility. By surfacing cognitive biases and prompting reflection, Lacuna encourages teams to validate assumptions through lightweight evidence-gathering methods. This experiment in human-AI collaboration explores how agents can foster epistemic humility and transform static documents into dynamic conversations.The Future of AI: Creating a Web Application with Vibe Coding
Discover how vibe coding with GPT-5 in Azure AI Foundry transforms web development. This post walks through building a Translator API-powered web app using natural language instructions in Visual Studio Code. Learn how adaptive translation, tone and gender customization, and Copilot agent collaboration redefine the developer experience.The Future of AI: Vibe Code with Adaptive Custom Translation
This blog explores how vibe coding—a conversational, flow-based development approach—was used to build the AdaptCT playground in Azure AI Foundry. It walks through setting up a productive coding environment with GitHub Copilot in Visual Studio Code, configuring the Copilot agent, and building a translation playground using Adaptive Custom Translation (AdaptCT). The post includes real-world code examples, architectural insights, and advanced UI patterns. It also highlights how AdaptCT fine-tunes LLM outputs using domain-specific reference sentence pairs, enabling more accurate and context-aware translations. The blog concludes with best practices for vibe coding teams and a forward-looking view of AI-augmented development paradigms.Using an AI Agent to Automate Jira Updates, PR Reviews, and Code Deployment
In modern software development, teams juggle multiple tools: Jira for project management, GitHub/GitLab for code collaboration, and CI/CD pipelines for deployment. Developers often spend significant time switching contexts—updating Jira tickets, reviewing pull requests, and triggering deployments. An AI agent can automate much of this workflow, acting as a “digital teammate” that fetches Jira data, helps review PRs, and pushes deployments. In this blog, we’ll explore how to set it up step by step. https://dellenny.com/supercharging-your-workflow-using-an-ai-agent-to-automate-jira-updates-pr-reviews-and-code-deployment/
