[](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=Azure-Samples/snippy&machine=basicLinux32gb&devcontainer_path=.devcontainer%2Fdevcontainer.json) [](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/Azure-Samples/snippy) Snippy is an **Azure Functions**-based reference application that demonstrates how to build **MCP (Model Context Protocol) tools** that integrate with AI assistants like GitHub Copilot. It showcases a modern serverless AI application architecture where Azure Functions serve as both traditional APIs and MCP-compatible tools that AI assistants can discover and use.  The system uses **Durable Task Scheduler** to orchestrate multi-agent workflows, **Cosmos DB with vector indexing** for semantic code search, and **Azure OpenAI** for embeddings and LLM capabilities. The project ships with reproducible **azd** infrastructure—`azd up` deploys the entire stack and works with both local Docker-based emulators and Azure cloud services. > **Important Security Notice** > This repository is intended for learning and demonstration purposes. **Do not** deploy it to production without a thorough security review. At a minimum you should: > > * Swap connection strings for **Managed Identity** + **Azure Key Vault** > * Restrict network access to Azure services via Private Endpoints or service‑tags > * Enable GitHub secret‑scanning and CI security tools [Features](#features) • [Lab Tutorial](#lab-tutorial) • [Getting Started](#getting-started) • [Guidance](#guidance) --- ## Features * **MCP Tool Integration** – expose Azure Functions as discoverable MCP tools for AI assistants * **Durable Agents with Microsoft Agent Framework** – build stateful AI agents using `ChatAgent` with automatic conversation history management * **Multi-Agent Orchestration** – coordinate specialized agents (DeepWiki, CodeStyle) using Durable Task Scheduler with fan-out/fan-in patterns * **Vector Search on Cosmos DB DiskANN** – semantic code retrieval using Azure OpenAI embeddings and low-latency vector indexing * **Monitoring & Observability** – track orchestrations in real-time using DTS dashboard (localhost:8082 local, Azure portal for cloud) * **One-click Deploy** – `azd up` provisions and deploys complete infrastructure including Functions, Cosmos DB, Azure OpenAI, and DTS * **Codespaces & Dev Containers** – fully configured development environment in your browser or local VS Code ### Tool Matrix | Tool Name | Purpose | | -------------------------------- | ------------------------------------------------------------------- | | `save_snippet` | Save code snippets with vector embeddings for semantic search | | `get_snippet` | Retrieve previously saved code snippets by their unique name | | `code_style` | Generate language-specific code style guides from saved snippets | | `deep_wiki` | Create comprehensive wiki documentation by analyzing code snippets | | `generate_comprehensive_documentation` | Orchestrate multi-agent workflow to produce deep wiki and style guide | --- ## Lab Tutorial New to Snippy? Start with our comprehensive **hands-on lab tutorial** that guides you through building the entire application from scratch: 📚 **[Snippy Tutorial: Building an AI-Enhanced Code Snippet Manager](https://azure-samples.github.io/snippy/)** The tutorial covers: * **Step-by-step implementation** – Build the complete application with detailed explanations * **Code review exercises** – Understand MCP tools, durable agents, and vector search patterns * **Local development** – Set up and test with Docker-based emulators * **Azure deployment** – Provision and deploy to production with azd * **Multi-agent orchestration** – Monitor complex workflows with DTS dashboard * **End-to-end testing** – Test with REST Client, GitHub Copilot, and MCP tools **Additional Resources:** * [Quick Reference](https://azure-samples.github.io/snippy/QUICK_REFERENCE/) – Essential commands and common tasks * [Troubleshooting Guide](https://azure-samples.github.io/snippy/TROUBLESHOOTING/) – Solutions to common issues Whether you're new to Azure Functions, MCP tools, or AI agent development, the lab provides a structured learning path with hands-on experience. --- ## Getting Started ### Prerequisites * Azure subscription with permissions to create resources * [azd CLI](https://aka.ms/install-azd) installed * Choose your development environment: - **GitHub Codespaces** (fastest, no local setup required) - **VS Code Dev Containers** (requires [Docker Desktop](https://www.docker.com/products/docker-desktop)) - **Local development** (requires Python 3.11, Node 18+, Azure Functions Core Tools v4) ### Deploy to Azure **Using GitHub Codespaces:** Click the "Open in GitHub Codespaces" badge above, then run: ```bash azd auth login --use-device-code azd up ``` **Using Dev Containers or Local Environment:** ```bash azd init --template Azure-Samples/snippy azd auth login azd up ``` The `azd up` command will: * Prompt for an Azure region (recommend **eastus** or **swedencentral**) * Provision all resources (Functions, Cosmos DB, Azure OpenAI, DTS) * Create an Azure AD app registration for authentication * Deploy the application code When complete, the Function App URL and MCP endpoint will be displayed. See the [tutorial](https://azure-samples.github.io/snippy/) for detailed instructions on local development, testing with emulators, and monitoring orchestrations. --- ## Guidance ### Region Availability Azure OpenAI model support varies by region. Verify availability [here](https://learn.microsoft.com/azure/ai-services/openai/concepts/models#standard-deployment-model-availability) and choose the same region for all Azure resources. **eastus** and **swedencentral** are good default choices. ### Security Snippy uses User-Assigned Managed Identity for secure service-to-service authentication. The infrastructure is configured with: * **User-Assigned Managed Identity** on the Function App with appropriate RBAC roles: * Cosmos DB Data Contributor * Storage Blob Data Owner and Queue Data Contributor * Application Insights Monitoring Metrics Publisher * Azure AI Project Developer For production deployments, we recommend: * Restrict inbound traffic with Private Endpoints + VNet integration * Enable network security features like service endpoints and firewall rules --- ## Contributing Standard **fork → branch → PR** workflow. Use *Conventional Commits* (`feat:`, `fix:`) in commit messages. --- ## License MIT © Microsoft Corporation