If you will be delivering this session, check the session-delivery-sources folder for slides, scripts, and other resources.
In this talk, discover how the Model Context Protocol (MCP) is transforming the way AI agents connect with tools, data and each other in a standardized way. Dive into building your own MCP server, connecting a client, and validating your setup using the official MCP Inspector.
By the end of this session, learners will be able to:
- Explain MCP fundamentals (host, client, server) and the roles of prompts, tools, and resources.
- Build and run a minimal Python MCP server that writes to and reads from a CSV ledger.
- Invoke the server from GitHub Copilot Chat in VS Code using a prompt that triggers a typed tool and retrieves a resource.
- Model Context Protocol (MCP)
- GitHub Copilot Chat (Agent + Tools) and Visual Studio Code
- Python 3.12, FastMCP, uv (venv + deps), CSV
| Resources | Links | Description |
|---|---|---|
| Additional Resources for this session | https://learn.microsoft.com | Links specific to this session |
| Resources | Links | Description |
|---|---|---|
| MCP for Beginners (GitHub) | https://github.com/microsoft/mcp-for-beginners | Beginner-friendly MCP guide with examples and docs |
| AI Tour 2026 Resource Center | https://aka.ms/AITour26-Resource-Center | Links to all repos for AI Tour 26 Sessions |
| Microsoft Foundry Community Discord | Connect with the Microsoft Foundry Community! | |
| Learn at AI Tour | https://aka.ms/LearnAtAITour | Continue learning on Microsoft Learn |
Additional Languages Coming Soon
 Gwyneth Pena-Siguenza 📢 |
You might need an Azure subscription to follow the steps in this repo. 👉 Start your free journey here: https://aka.ms/devrelft
This Azure Free Trial provides $200 credit for 30 days. Some features may incur costs after the trial. Check the Azure pricing calculator to estimate costs.
Important
Free Tier Limitations: The Azure free subscription has significant constraints that may prevent full implementation of this repo:
- Model access: Some advanced models (e.g., GPT-5, Claude) may not be available or have very limited quotas
- Rate limits: Strict API call limits (e.g., requests per minute, tokens per day)
- Region restrictions: Free tier resources may only be available in limited regions
- Feature restrictions: Some Microsoft Foundry features (agent orchestration, evaluations) may require pay-as-you-go
- Credit exhaustion: $200 credit can be consumed quickly with heavy AI model usage
Recommendation: For full functionality, consider a pay-as-you-go subscription or request access to Azure for Students ($100 credit, no credit card required) or the Microsoft for Startups Founders Hub.
Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.
The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. Within Azure AI Foundry portal, the Content Safety service allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.
Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using Performance and Quality and Risk and Safety evaluators. You also have the ability to create and evaluate with custom evaluators.
You can evaluate your AI application in your development environment using the Azure AI Evaluation SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the azure ai evaluation sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Foundry portal .