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# [Microsoft AI Tour 2026](https://aitour.microsoft.com)
## 🔥WRK540: Unlock Your Agents' Potential with Model Context Protocol and PostgreSQL
[](https://aka.ms/MicrosoftFoundryDiscord-AITour26)
[](https://aka.ms/MicrosoftFoundryForum-AITour26)
If you will be delivering this session, check the [session-delivery-sources](./session-delivery-resources/) folder for slides, scripts, and other resources.
### Session Description
Build a conversational AI agent for Zava, a retail DIY company, that analyzes sales data and helps customers find products. Learn to create secure, intelligent agents using Microsoft Foundry Agent Service, Model Context Protocol (MCP) for external data connections, and PostgreSQL with Row Level Security (RLS) and pgvector for role-based data protection and semantic search.
### 🧠 Learning Outcomes
By the end of this session, learners will be able to:
1. **Microsoft Foundry Agent Service**: Build and deploy AI agents with integrated tools and observability.
2. **Model Context Protocol (MCP)**: Connects the Agent Service to external tools and data over industry standard protocols to enhance agent functionality.
3. **PostgreSQL**: Use PostgreSQL as a vector database for semantic search and implement Row Level Security (RLS) to protect sensitive data based on user roles.
4. **Microsoft Foundry**: An enterprise-grade AI development platform providing unified model access, comprehensive monitoring, distributed tracing capabilities, and production-ready governance for AI applications at scale.
### 💻 Technologies Used
1. Microsoft Foundry
1. PostgreSQL including Row Level Security (RLS) and Semantic Search with the pgvector extension
1. Model Context Protocol (MCP)
### 🔗 Session Resources
| Resources | Links | Description |
|:-------------------|:----------------------------------|:-------------------|
| Workshop Repository | [Unlock your Agents Potential with MCP and PostgreSQL](https://github.com/microsoft/Unlock-Your-Agents-Potential-with-MCP-and-PostgreSQL) | Workshop materials and resources |
| Workshop Docs | [Workshop Documentation](https://microsoft.github.io/aitour26-WRK540-unlock-your-agents-potential-with-model-context-protocol/) | Workshop documentation site |
| Documentation | [Microsoft Foundry](https://learn.microsoft.com/azure/ai-foundry/)| Microsoft Foundry documentation |
| Module | [Fundamentals of AI agents on Azure](https://learn.microsoft.com/training/modules/ai-agent-fundamentals/)| Training module on AI agent fundamentals |
| Documentation | [Tracing using Application Insights](https://learn.microsoft.com/azure/ai-services/agents/concepts/tracing)| Guide to tracing with Application Insights |
| Documentation | [Evaluating your AI agents with Azure AI Evaluation SDK](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/agent-evaluate-sdk)| AI agent evaluation documentation |
| Documentation | [Model Context Protocol (MCP)](https://learn.microsoft.com/azure/developer/azure-mcp-server/)| Model Context Protocol documentation |
| Documentation | [🚀 Model Context Protocol (MCP) Curriculum for Beginners](https://github.com/microsoft/mcp-for-beginners/tree/main)| Beginner-friendly MCP curriculum |
| Documentation | [PostgreSQL on Azure](https://learn.microsoft.com/azure/postgresql/)| PostgreSQL on Azure documentation |
| Documentation | [pgvector extension for PostgreSQL](https://learn.microsoft.com/azure/postgresql/flexible-server/how-to-use-pgvector)| Guide to using pgvector extension |
| Documentation | [Row Level Security (RLS) in PostgreSQL](https://learn.microsoft.com/azure/postgresql/flexible-server/security-access-control#row-level-security)| PostgreSQL Row Level Security documentation |
### 📚 Continued Learning Resources
| Resources | Links | Description |
|:-------------------|:----------------------------------|:-------------------|
| AI Tour 2026 Resource Center | <https://aka.ms/AITour26-Resource-center> | Links to all repos for AI Tour 26 Sessions |
| Microsoft Foundry Community Discord | [](https://aka.ms/MicrosoftFoundryDiscord-AITour26)| Connect with the Microsoft Foundry Community! |
| Learn at AI Tour | <https://aka.ms/LearnAtAITour> | Continue learning on Microsoft Learn |
### 🌐 Multi-Language Support
Additional languages are coming soon.
## Content Owners
<table>
<tr>
<td align="center"><a href="http://github.com/gloveboxes">
<img src="https://github.com/gloveboxes.png" width="100px;" alt="Dave Glover"
/><br />
<sub><b>Dave Glover (Python)
</b></sub></a><br />
<a href="https://github.com/gloveboxes" title="talk">📢</a>
</td>
<td align="center"><a href="http://github.com/marlenezw">
<img src="https://github.com/marlenezw.png" width="100px;" alt="Marlene Mhangami (Python)"/><br />
<sub><b>Marlene Mhangami (Python)
</b></sub></a><br />
<a href="https://github.com/marlenezw" title="talk">📢</a>
</td>
</tr>
<tr>
<td align="center"><a href="http://github.com/aaronpowell">
<img src="https://github.com/aaronpowell.png" width="100px;" alt="Aaron Powell
"/><br />
<sub><b>Aaron Powell (C#)
</b></sub></a><br />
<a href="https://github.com/aaronpowell" title="talk">📢</a>
</td>
</tr>
</table>
##
## Responsible AI
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](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](https://learn.microsoft.com/legal/cognitive-services/openai/transparency-note?tabs=text) 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](https://learn.microsoft.com/azure/ai-services/content-safety/overview) 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 Microsoft 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](https://learn.microsoft.com/azure/ai-services/content-safety/quickstart-text?tabs=visual-studio%2Clinux&pivots=programming-language-rest) 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](https://learn.microsoft.com/azure/ai-studio/concepts/evaluation-metrics-built-in). You also have the ability to create and evaluate with [custom evaluators](https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#custom-evaluators).
You can evaluate your AI application in your development environment using the [Azure AI Evaluation SDK](https://microsoft.github.io/promptflow/index.html). 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](https://learn.microsoft.com/azure/ai-studio/how-to/develop/flow-evaluate-sdk). Once you execute an evaluation run, you can [visualize the results in Microsoft Foundry portal](https://learn.microsoft.com/azure/ai-studio/how-to/evaluate-flow-results).
## Compiling the Python Requirements
1. Update the `requirements.in` file with libraries and specify version ranges.
2. Install pip tools `pip install pip-tools`.
3. Compile the requirements by running `pip-compile -r requirements.in -o requirements.lock.txt`.
## Published Docs
This repo is configured to use mkdocs, and the docs are in the `docs` folder. The workshop docs are published [here](https://microsoft.github.io/aitour26-WRK540-unlock-your-agents-potential-with-model-context-protocol/).
Thanks for creating content for AI Tour 26.
One of our opportunities from FY25 was make a more consistent experience for attendees. Following this template, and keeping users in these repos, will help us achieve this opportunity for this year's AI Tour.
Steps:
## Docs using MKDocs
If you are interested in using mkdocs, this repo is configured for it.
1. update the `mkdocs.yml` file to reference your session. Look particularly for the `site_name`, `site_author`, and `repo_name` tags.
1. Subfolders in the docs folder will show up as tabs in the navigation bar.
If you want to disable mkdocs:
1. Delete the mkdocs.yml file.
1. Delete the references to it in [docs/README.md](./docs/README.md)
1. Delete the docs/assets folder and its contents.
## MKDocs and Language Translation
1. From VS Code, select Agent mode, select the desired LLM, eg `claude sonnet 4`.
2. Type `/mkdocs-translations`
3. You'll be prompted to enter the target language, be sure to select the correct name of the language, e.g. `Spanish` or `French`.
4. The Agent will then translate the content in the `docs/docs/en` folder to the target language, creating a new folder under `docs/docs/` with the appropriate ISO 639-1 or locale code.
## Other folders
Note the data, docs, src, lab, session-delivery-resources folders.
1. Remove folders that you dont need.
1. Please keep and use the folders that you do need. e.g. put your data in the data folder, and put your docs in the docs folder.
## Update Readme
1. Fill out the content below in this file, below the banner graphic.
## Update Repo Info
1. Note the data, docs, src, lab, session-delivery-resources folders.
1. Remove folders that you dont need, but please keep and use the folders that you do need. e.g. put your data in the data folder, and put your docs in the docs folder.
1. Fill out the content below in this file, below the banner graphic.
1. Update the Repo Info for this repo
1. Click the gear icon⚙️ in the upper right.
1. Set a good description of this repo.
1. Add the technologies that you're using in this session. E.g. the same items that are in the Technologies Used section below.
1. Please embed links to Learn with your campaign codes!
## Questions?
Send them to Mike Kinsman, Erik Weis, and cc Anthony Bartolo.
## Last step
Delete this line and everything above it!
