Microsoft Foundry — MongoDB + Azure AI Samples

A collection of samples and reference architectures for building AI-powered applications with MongoDB Atlas and Microsoft Azure AI Foundry.

What's Inside

Sample	Description	Technologies
simple-rag-movies	Fully automated, single-script deployment of a Microsoft Foundry agent performing RAG over MongoDB Atlas movie data via MCP tools	Microsoft Foundry, Azure Native MongoDB Atlas, Atlas Vector Search, MongoDB MCP Server, Azure Functions (Flex Consumption), Bicep, PowerShell

simple-rag-movies

Build a Microsoft Foundry agent that performs semantic search over MongoDB Atlas sample movie data — deployed end to end with one script (./scripts/setup-and-deploy.ps1). The sample includes:

• Foundry prompt agent (gpt-5-mini) wired to MCP tools:
  • an embedding Function (Flex Consumption) that serves the semantic_search MCP endpoint and relays all other calls untouched
  • the MongoDB MCP Server on Azure Container Apps — the only component holding the connection string, performing all database operations
• Full automation — one command sets up Atlas (cluster, sample data, vector index), provisions Azure (Foundry account, models, Function, Container App), and creates the agent
• GitHub Codespaces dev container for a zero-local-install deployment path
• Infrastructure as Code — Bicep templates for the MCP Server and embedding Function
• Sample queries and agent instructions to test semantic search, direct filters, and aggregations

→ Get started here: simple-rag-movies/README.md

Repository Structure

Microsoft_Foundry/
├── README.md                        # This file — hub overview
├── MSFT Foundry_Architecture.png    # Reference architecture diagram
└── simple-rag-movies/               # Sample: Foundry agent + Atlas Vector Search RAG
    ├── README.md                    # Sample setup guide
    ├── LICENSE                      # Sample license
    ├── sample-queries.md            # Example queries to test the agent
    ├── deploy/                      # Bicep templates + config.example.json
    ├── docs/                        # Architecture doc and agent instructions
    ├── imgs/                        # Sample architecture diagram
    ├── scripts/                     # setup-and-deploy.ps1 + atlas/ and azure/ automation
    ├── src/embedding-function/      # Azure Function (Python) for embeddings
    └── .devcontainer/               # GitHub Codespaces dev container

Security

This repository follows security best practices:

• No secrets in source control — .gitignore excludes .env files, local.settings.json, certificates, and other credential files
• Secrets are passed as parameters — all deployment templates use @secure() parameters (Bicep) / securestring (ARM), and deployment scripts prompt for credentials at runtime
• Configuration via environment variables — sample code reads credentials from environment variables, never hardcoded
• Template files (e.g., local.settings.json.template) contain only placeholders — copy them and fill in your own values locally

If you ever accidentally commit a secret, rotate it immediately and remove it from git history.

Prerequisites

Most samples in this hub require:

• An Azure subscription
• An Azure AI Foundry project
• A MongoDB Atlas cluster (M0 free tier works)
• Azure CLI

Each sample's README lists its specific prerequisites and setup steps.

Contributing

New samples should follow the existing structure: a dedicated folder with its own README.md, IaC under deploy/, documentation under docs/, and source under src/. Never commit credentials — see the Security section above.