Exposing Legacy APIs hosted on Azure Container Apps to AI Agents using MCP Servers
Overview This article explains the process of exposing legacy APIs, which are hosted on Azure Container Apps, to AI Agents, as Model Context Protocol (MCP) servers by utilizing Azure API Management. Use Case Imagine your organization has a legacy REST API hosted on Azure Container Apps that core business systems depend on (order management, pricing, inventory, etc.). You now want AI agents (e.g., GitHub Copilot Agent Mode, Claude, internal copilots) to safely discover and invoke that functionality as “tools,” without rewriting the backend or loosening governance. To showcase the use case, we deploy an e-commerce application that includes these legacy API endpoints onto Azure Container Apps. For demonstration purposes, we use the Store API a widely used REST API designed for e-commerce prototyping. The exposed endpoints are then published as MCP Server endpoints through Azure API Management, providing agents a standards-based entry point with centralized security, throttling, and observability. Following this, Visual Studio Code is used to consume these endpoints, facilitating modern and efficient API access for client applications. Deploy Sample Store App to Azure Container Apps Deploy the Store App to Azure Container Apps. The App offers realistic endpoints for managing products, users, shopping carts, and authentication. Fork the Store API and build the .Dockerfile and provide the below details to create the App. More details on deployment could be found here . az group create --name "store-app-rg" --location "East US" az containerapp env create --name "store-app-env" --resource-group "store-app-rg" --location "East US" az containerapp create --name "store-app" --resource-group "store-app-rg" --environment "store-app-env" --image "voronalk/fakestoreapi:latest" --target-port 3000 --ingress external --min-replicas 1 --max-replicas 5 --cpu 0.5 --memory 1Gi --env-vars PORT=3000 az containerapp show --name "store-app" --resource-group "store-app-rg" --query "properties.configuration.ingress.fqdn" --output table Create an Azure API Management Instance To securely create, expose and govern MCP servers and their backends, API management instance is created. It also provides centralized control over MCP server authentication, authorization, and monitoring. az group create --name "store-app-rg" --location "East US" az apim create --name "store-app-apim" --resource-group "store-app-rg" --location "East US" --publisher-email "admin@example.com" --publisher-name "Store App Publisher" --sku-name Developer Import the Storefront Open API Specification in Azure API Management The APIs which are hosted on Azure Container Apps needs to be imported to Azure API Management such that the existing APIs can be exposed through MCP Server without rewriting their backends. The Open API Specification provides options to quick import and creates Proxies within API Management # First get the Container App URL CONTAINER_APP_URL=$(az containerapp show --name "store-app" --resource-group "store-app-rg" --query "properties.configuration.ingress.fqdn" --output tsv) # Import the API az apim api import --resource-group "store-app-rg" --service-name "store-app-apim" --api-id "store-api" --path "/storeapp" --specification-format "OpenApi" --specification-url "https://fakestoreapi.com/docs-data" --display-name "Store API" --protocols https --service-url "https://$CONTAINER_APP_URL" Here is the Open API representation of the Legacy Store Endpoints Expose the Legacy APIs as a MCP Server Navigate to APIs, select MCP Servers > + Create MCP server. Provide the endpoints that needs to be available as MCP server. Select the operations required to be exposed to the AI Agents. Follow the steps for more details Below, the MCP server is created, and the API operations are exposed as tools. The MCP server is listed in the MCP Servers blade. The Server URL column shows the endpoint of the MCP server to call for testing or within a client application. Configure the MCP Server from VS Code Follow the steps mentioned here to configure the MCP Server in VS Code. The MCP endpoint created in the above step is protected by an API Key, ensure that the key details are provided in the configuration. Update the settings.json with the following configuration Update the mcp.json with the following configuration providing the MCP endpoint for the AI Agents Query the MCP Server endpoint from VS Code Chat Following is the response from the query on requesting the MCP endpoint. Following are some of the other prompts you could try Show me all the products available in the Store List the top 10 products from Store Give me all men’s clothing and sort them by price Find all carts created between 2020-10-01 and 2020-12-31Agentic Applications on Azure Container Apps with Microsoft Foundry
Agents have exploded in popularity over the last year, reshaping not only the kinds of applications developers build but also the underlying architectures required to run them. As agentic applications grow more complex by invoking tools, collaborating with other services, and orchestrating multi-step workflows, architectures are naturally shifting toward microservice patterns. Azure Container Apps is purpose-built for this world: a fully managed, serverless platform designed to run independent, composable services with autoscaling, pay-per-second pricing, and seamless app-to-app communication. By combining Azure Container Apps with the Microsoft Agent Framework (MAF) and Microsoft Foundry, developers can run containerized agents on ACA while using Foundry to visualize and monitor how those agents behave. Azure Container Apps handles scalable, high-performance execution of agent logic, and Microsoft Foundry lights up rich observability for reasoning, planning, tool calls, and errors through its integrated monitoring experience. Together, they form a powerful foundation for building and operating modern, production-grade agentic applications. In this blog, we’ll walk through how to build an agent running on Azure Container Apps using Microsoft Agent Framework and OpenTelemetry, and then connect its telemetry to Microsoft Foundry so you can see your ACA-hosted agent directly in the Foundry monitoring experience. Prerequisites An Azure account with an active subscription. If you don't have one, you can create one for free. Ensure you have a Microsoft Foundry project setup. If you don’t already have one, you can create a project from the Azure AI Foundry portal. Azure Developer CLI (azd) installed Git installed The Sample Agent The complete sample code is available in this repo and can be deployed end-to-end with a single command. It's a basic currency agent. This sample deploys: An Azure Container Apps environment An agent built with Microsoft Agent Framework (MAF) OpenTelemetry instrumentation using the Azure Monitor exporter Application Insights to collect agent telemetry A Microsoft Foundry resource Environment wiring to integrate the agent with Microsoft Foundry Deployment Steps Clone the repository: git clone https://github.com/cachai2/foundry-3p-agents-samples.git cd foundry-3p-agents-samples/azure Authenticate with Azure: azd auth login Set the following azd environment variable azd env set AZURE_AI_MODEL_DEPLOYMENT_NAME gpt-4.1-mini Deploy to Azure azd up This provisions your Azure Container App, Application Insights, logs pipeline and required environment variables. While deployment runs, let’s break down how the code becomes compatible with Microsoft Foundry. 1. Setting up the agent in Azure Container Apps To integrate with Microsoft Foundry, the agent needs two essential capabilities: Microsoft Agent Framework (MAF): Handles agent logic, tools, schema-driven execution, and emits standardized gen_ai.* spans. OpenTelemetry: Sends the required agent/model/tool spans to Application Insights, which Microsoft Foundry consumes for visualization and monitoring. Although this sample uses MAF, the same pattern works with any agent framework. MAF and LangChain currently provide the richest telemetry support out-of-the-box. 1.1 Configure Microsoft Agent Framework (MAF) The agent includes: A tool (get_exchange_rate) An agent created by ChatAgent A runtime manager (AgentRuntime) A FastAPI app exposing /invoke Telemetry is enabled using two components already present in the repo: configure_azure_monitor: Configures OpenTelemetry + Azure Monitor exporter + auto-instrumentation. setup_observability(): Enables MAF’s additional spans (gen_ai.*, tool spans, agent lifecycle spans). From the repo (_configure_observability()): from azure.monitor.opentelemetry import configure_azure_monitor from agent_framework.observability import setup_observability from opentelemetry.sdk.resources import Resource def _configure_observability() -> None: configure_azure_monitor( resource=Resource.create({"service.name": SERVICE_NAME}), connection_string=APPLICATION_INSIGHTS_CONNECTION_STRING, ) setup_observability(enable_sensitive_data=False) This gives you: gen_ai.model.* spans (model usage + token counts) tool call spans agent lifecycle & execution spans HTTP + FastAPI instrumentation Standardized telemetry required by Microsoft Foundry No manual TracerProvider wiring or OTLP exporter setup is needed. 1.2 OpenTelemetry Setup (Azure Monitor Exporter) In this sample, OpenTelemetry is fully configured by Azure Monitor’s helper: import os from azure.monitor.opentelemetry import configure_azure_monitor from opentelemetry.sdk.resources import Resource from agent_framework.observability import setup_observability SERVICE_NAME = os.getenv("ACA_SERVICE_NAME", "aca-currency-exchange-agent") configure_azure_monitor( resource=Resource.create({"service.name": SERVICE_NAME}), connection_string=os.getenv("APPLICATION_INSIGHTS_CONNECTION_STRING"), ) # Enable Microsoft Agent Framework gen_ai/tool spans on top of OTEL setup_observability(enable_sensitive_data=False) This automatically: Installs and configures the OTEL tracer provider Enables batching + exporting of spans Adds HTTP/FastAPI/Requests auto-instrumentation Sends telemetry to Application Insights Adds MAF’s agent + tool spans All required environment variables (such as APPLICATION_INSIGHTS_CONNECTION_STRING) are injected automatically by azd up. 2. Deploy the Model and Test Your Agent Once azd up completes, you're ready to deploy a model to the Microsoft Foundry instance and test it. Find the resource name of your deployed Azure AI Services from azd up and navigate to it. From there, open it in Microsoft Foundry, navigate to the Model Catalog and add the gpt-4.1-mini model. Find the resource name of your deployed Azure Container App and navigate to it. Copy the application URL Set your container app URL environment variable in your terminal. (The below commands are for WSL.) export APP_URL="Your container app URL" Now, go back to your terminal and run the following curl command to invoke the agent curl -X POST "$APP_URL/invoke" \ -H "Content-Type: application/json" \ -d '{ "prompt": "How do I convert 100 USD to EUR?" }' 3. Verifying Telemetry to Application Insights Once your Container App starts, you can validate telemetry: Open the Application Insights resource created by azd up Go to Logs Run these queries (make sure you're in KQL mode not simple mode) Check MAF-genAI spans: dependencies | where timestamp > ago(3h) | extend genOp = tostring(customDimensions["gen_ai.operation.name"]), genSys = tostring(customDimensions["gen_ai.system"]), reqModel = tostring(customDimensions["gen_ai.request.model"]), resModel = tostring(customDimensions["gen_ai.response.model"]) | summarize count() by genOp, genSys, reqModel, resModel | order by count_ desc Check agent + tools: dependencies | where timestamp > ago(1h) | extend genOp = tostring(customDimensions["gen_ai.operation.name"]), agent = tostring(customDimensions["gen_ai.agent.name"]), tool = tostring(customDimensions["gen_ai.tool.name"]) | where genOp in ("agent.run", "invoke_agent", "execute_tool") | project timestamp, genOp, agent, tool, name, target, customDimensions | order by timestamp desc If telemetry is flowing, you’re ready to plug your agent into Microsoft Foundry. 4. Connect Application Insights to Microsoft Foundry Microsoft Foundry uses your Application Insights resource to power: Agent monitoring Tool call traces Reasoning graphs Multi-agent orchestration views Error analysis To connect: Navigate to Monitoring in the left navigation pane of the Microsoft Foundry portal. Select the Application analytics tab. Select your application insights resource created from azd up Connect the resource to your AI Foundry project. Note: If you are unable to add your application insights connection this way, you may need to follow the following: Navigate to the Overview of your Foundry project -> Open in management center -> Connected resources -> New Connection -> Application Insights Foundry will automatically start ingesting: gen_ai.* spans tool spans agent lifecycle spans workflow traces No additional configuration is required. 5. Viewing Dashboards & Traces in Microsoft Foundry Once your Application Insights connection is added, you can view your agent’s telemetry directly in Microsoft Foundry’s Monitoring experience. 5.1 Monitoring The Monitoring tab shows high-level operational metrics for your application, including: Total inference calls Average call duration Overall success/error rate Token usage (when available) Traffic trends over time This view is useful for spotting latency spikes, increased load, or changes in usage patterns, and these visualizations are powered by the telemetry emitting from your agents in Azure Container Apps. 5.2 Traces Timeline The Tracing tab shows the full distributed trace of each agent request, including all spans emitted by Microsoft Foundry and your Azure Container App with Microsoft Agent Framework. You can see: Top-level operations such as invoke_agent, chat, and process_thread_run Tool calls like execute_tool_get_exchange_rate Internal MAF steps (create_thread, create_message, run tool) Azure credential calls (GET /msi/token) Input/output tokens and duration for each span This view gives you an end-to-end breakdown of how your agent executed, which tools it invoked, and how long each step took — essential for debugging and performance tuning. Conclusion By combining Azure Container Apps, the Microsoft Agent Framework, and OpenTelemetry, you can build agents that are not only scalable and production-ready, but also fully observable and orchestratable inside Microsoft Foundry. Container Apps provides the execution engine and autoscaling foundation, MAF supplies structured agent logic and telemetry, and Microsoft Foundry ties everything together with powerful planning, monitoring, and workflow visualization. This architecture gives you the best of both worlds: the flexibility of running your own containerized agents with the dependencies you choose, and the intelligence of Microsoft Foundry to coordinate multi-step reasoning, tool call, and cross-agent workflows. As the agent ecosystem continues to evolve, Azure Container Apps and Microsoft Foundry provide a strong, extensible foundation for building the next generation of intelligent, microservice-driven applications.Azure Functions Ignite 2025 Update
Azure Functions is redefining event-driven applications and high-scale APIs in 2025, accelerating innovation for developers building the next generation of intelligent, resilient, and scalable workloads. This year, our focus has been on empowering AI and agentic scenarios: remote MCP server hosting, bulletproofing agents with Durable Functions, and first-class support for critical technologies like OpenTelemetry, .NET 10 and Aspire. With major advances in serverless Flex Consumption, enhanced performance, security, and deployment fundamentals across Elastic Premium and Flex, Azure Functions is the platform of choice for building modern, enterprise-grade solutions. Remote MCP Model Context Protocol (MCP) has taken the world by storm, offering an agent a mechanism to discover and work deeply with the capabilities and context of tools. When you want to expose MCP/tools to your enterprise or the world securely, we recommend you think deeply about building remote MCP servers that are designed to run securely at scale. Azure Functions is uniquely optimized to run your MCP servers at scale, offering serverless and highly scalable features of Flex Consumption plan, plus two flexible programming model options discussed below. All come together using the hardened Functions service plus new authentication modes for Entra and OAuth using Built-in authentication. Remote MCP Triggers and Bindings Extension GA Back in April, we shared a new extension that allows you to author MCP servers using functions with the MCP tool trigger. That MCP extension is now generally available, with support for C#(.NET), Java, JavaScript (Node.js), Python, and Typescript (Node.js). The MCP tool trigger allows you to focus on what matters most: the logic of the tool you want to expose to agents. Functions will take care of all the protocol and server logistics, with the ability to scale out to support as many sessions as you want to throw at it. [Function(nameof(GetSnippet))] public object GetSnippet( [McpToolTrigger(GetSnippetToolName, GetSnippetToolDescription)] ToolInvocationContext context, [BlobInput(BlobPath)] string snippetContent ) { return snippetContent; } New: Self-hosted MCP Server (Preview) If you’ve built servers with official MCP SDKs and want to run them as remote cloud‑scale servers without re‑writing any code, this public preview is for you. You can now self‑host your MCP server on Azure Functions—keep your existing Python, TypeScript, .NET, or Java code and get rapid 0 to N scaling, built-in server authentication and authorization, consumption-based billing, and more from the underlying Azure Functions service. This feature complements the Azure Functions MCP extension for building MCP servers using the Functions programming model (triggers & bindings). Pick the path that fits your scenario—build with the extension or standard MCP SDKs. Either way you benefit from the same scalable, secure, and serverless platform. Use the official MCP SDKs: # MCP.tool() async def get_alerts(state: str) -> str: """Get weather alerts for a US state. Args: state: Two-letter US state code (e.g. CA, NY) """ url = f"{NWS_API_BASE}/alerts/active/area/{state}" data = await make_nws_request(url) if not data or "features" not in data: return "Unable to fetch alerts or no alerts found." if not data["features"]: return "No active alerts for this state." alerts = [format_alert(feature) for feature in data["features"]] return "\n---\n".join(alerts) Use Azure Functions Flex Consumption Plan's serverless compute using Custom Handlers in host.json: { "version": "2.0", "configurationProfile": "mcp-custom-handler", "customHandler": { "description": { "defaultExecutablePath": "python", "arguments": ["weather.py"] }, "http": { "DefaultAuthorizationLevel": "anonymous" }, "port": "8000" } } Learn more about MCPTrigger and self-hosted MCP servers at https://aka.ms/remote-mcp Built-in MCP server authorization (Preview) The built-in authentication and authorization feature can now be used for MCP server authorization, using a new preview option. You can quickly define identity-based access control for your MCP servers with Microsoft Entra ID or other OpenID Connect providers. Learn more at https://aka.ms/functions-mcp-server-authorization. Better together with Foundry agents Microsoft Foundry is the starting point for building intelligent agents, and Azure Functions is the natural next step for extending those agents with remote MCP tools. Running your tools on Functions gives you clean separation of concerns, reuse across multiple agents, and strong security isolation. And with built-in authorization, Functions enables enterprise-ready authentication patterns, from calling downstream services with the agent’s identity to operating on behalf of end users with their delegated permissions. Build your first remote MCP server and connect it to your Foundry agent at https://aka.ms/foundry-functions-mcp-tutorial. Agents Microsoft Agent Framework 2.0 (Public Preview Refresh) We’re excited about the preview refresh 2.0 release of Microsoft Agent Framework that builds on battle hardened work from Semantic Kernel and AutoGen. Agent Framework is an outstanding solution for building multi-agent orchestrations that are both simple and powerful. Azure Functions is a strong fit to host Agent Framework with the service’s extreme scale, serverless billing, and enterprise grade features like VNET networking and built-in auth. Durable Task Extension for Microsoft Agent Framework (Preview) The durable task extension for Microsoft Agent Framework transforms how you build production-ready, resilient and scalable AI agents by bringing the proven durable execution (survives crashes and restarts) and distributed execution (runs across multiple instances) capabilities of Azure Durable Functions directly into the Microsoft Agent Framework. Combined with Azure Functions for hosting and event-driven execution, you can now deploy stateful, resilient AI agents that automatically handle session management, failure recovery, and scaling, freeing you to focus entirely on your agent logic. Key features of the durable task extension include: Serverless Hosting: Deploy agents on Azure Functions with auto-scaling from thousands of instances to zero, while retaining full control in a serverless architecture. Automatic Session Management: Agents maintain persistent sessions with full conversation context that survives process crashes, restarts, and distributed execution across instances Deterministic Multi-Agent Orchestrations: Coordinate specialized durable agents with predictable, repeatable, code-driven execution patterns Human-in-the-Loop with Serverless Cost Savings: Pause for human input without consuming compute resources or incurring costs Built-in Observability with Durable Task Scheduler: Deep visibility into agent operations and orchestrations through the Durable Task Scheduler UI dashboard Create a durable agent: endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4o-mini") # Create an AI agent following the standard Microsoft Agent Framework pattern agent = AzureOpenAIChatClient( endpoint=endpoint, deployment_name=deployment_name, credential=AzureCliCredential() ).create_agent( instructions="""You are a professional content writer who creates engaging, well-structured documents for any given topic. When given a topic, you will: 1. Research the topic using the web search tool 2. Generate an outline for the document 3. Write a compelling document with proper formatting 4. Include relevant examples and citations""", name="DocumentPublisher", tools=[ AIFunctionFactory.Create(search_web), AIFunctionFactory.Create(generate_outline) ] ) # Configure the function app to host the agent with durable session management app = AgentFunctionApp(agents=[agent]) app.run() Durable Task Scheduler dashboard for agent and agent workflow observability and debugging For more information on the durable task extension for Agent Framework, see the announcement: https://aka.ms/durable-extension-for-af-blog. Flex Consumption Updates As you know, Flex Consumption means serverless without compromise. It combines elastic scale and pay‑for‑what‑you‑use pricing with the controls you expect: per‑instance concurrency, longer executions, VNet/private networking, and Always Ready instances to minimize cold starts. Since launching GA at Ignite 2024 last year, Flex Consumption has had tremendous growth with over 1.5 billion function executions per day and nearly 40 thousand apps. Here’s what’s new for Ignite 2025: 512 MB instance size (GA). Right‑size lighter workloads, scale farther within default quota. Availability Zones (GA). Distribute instances across zones. Rolling updates (Public Preview). Unlock zero-downtime deployments of code or config by setting a single configuration. See below for more information. Even more improvements including: new diagnostic settingsto route logs/metrics, use Key Vault App Config references, new regions, and Custom Handler support. To get started, review Flex Consumption samples, or dive into the documentation to see how Flex can support your workloads. Migrating to Azure Functions Flex Consumption Migrating to Flex Consumption is simple with our step-by-step guides and agentic tools. Move your Azure Functions apps or AWS Lambda workloads, update your code and configuration, and take advantage of new automation tools. With Linux Consumption retiring, now is the time to switch. For more information, see: Migrate Consumption plan apps to the Flex Consumption plan Migrate AWS Lambda workloads to Azure Functions Durable Functions Durable Functions introduces powerful new features to help you build resilient, production-ready workflows: Distributed Tracing: lets you track requests across components and systems, giving you deep visibility into orchestration and activities with support for App Insights and OpenTelemetry. Extended Sessions support in .NET isolated: improves performance by caching orchestrations in memory, ideal for fast sequential activities and large fan-out/fan-in patterns. Orchestration versioning (public preview): enables zero-downtime deployments and backward compatibility, so you can safely roll out changes without disrupting in-flight workflows Durable Task Scheduler Updates Durable Task Scheduler Dedicated SKU (GA): Now generally available, the Dedicated SKU offers advanced orchestration for complex workflows and intelligent apps. It provides predictable pricing for steady workloads, automatic checkpointing, state protection, and advanced monitoring for resilient, reliable execution. Durable Task Scheduler Consumption SKU (Public Preview): The new Consumption SKU brings serverless, pay-as-you-go orchestration to dynamic and variable workloads. It delivers the same orchestration capabilities with flexible billing, making it easy to scale intelligent applications as needed. For more information see: https://aka.ms/dts-ga-blog OpenTelemetry support in GA Azure Functions OpenTelemetry is now generally available, bringing unified, production-ready observability to serverless applications. Developers can now export logs, traces, and metrics using open standards—enabling consistent monitoring and troubleshooting across every workload. Key capabilities include: Unified observability: Standardize logs, traces, and metrics across all your serverless workloads for consistent monitoring and troubleshooting. Vendor-neutral telemetry: Integrate seamlessly with Azure Monitor or any OpenTelemetry-compliant backend, ensuring flexibility and choice. Broad language support: Works with .NET (isolated), Java, JavaScript, Python, PowerShell, and TypeScript. Start using OpenTelemetry in Azure Functions today to unlock standards-based observability for your apps. For step-by-step guidance on enabling OpenTelemetry and configuring exporters for your preferred backend, see the documentation. Deployment with Rolling Updates (Preview) Achieving zero-downtime deployments has never been easier. The Flex Consumption plan now offers rolling updates as a site update strategy. Set a single property, and all future code deployments and configuration changes will be released with zero-downtime. Instead of restarting all instances at once, the platform now drains existing instances in batches while scaling out the latest version to match real-time demand. This ensures uninterrupted in-flight executions and resilient throughput across your HTTP, non-HTTP, and Durable workloads – even during intensive scale-out scenarios. Rolling updates are now in public preview. Learn more at https://aka.ms/functions/rolling-updates. Secure Identity and Networking Everywhere By Design Security and trust are paramount. Azure Functions incorporates proven best practices by design, with full support for managed identity—eliminating secrets and simplifying secure authentication and authorization. Flex Consumption and other plans offer enterprise-grade networking features like VNETs, private endpoints, and NAT gateways for deep protection. The Azure Portal streamlines secure function creation, and updated scenarios and samples showcase these identity and networking capabilities in action. Built-in authentication (discussed above) enables inbound client traffic to use identity as well. Check out our updated Functions Scenarios page with quickstarts or our secure samples gallery to see these identity and networking best practices in action. .NET 10 Azure Functions now supports .NET 10, bringing in a great suite of new features and performance benefits for your code. .NET 10 is supported on the isolated worker model, and it’s available for all plan types except Linux Consumption. As a reminder, support ends for the legacy in-process model on November 10, 2026, and the in-process model is not being updated with .NET 10. To stay supported and take advantage of the latest features, migrate to the isolated worker model. Aspire Aspire is an opinionated stack that simplifies development of distributed applications in the cloud. The Azure Functions integration for Aspire enables you to develop, debug, and orchestrate an Azure Functions .NET project as part of an Aspire solution. Aspire publish directly deploys to your functions to Azure Functions on Azure Container Apps. Aspire 13 includes an updated preview version of the Functions integration that acts as a release candidate with go-live support. The package will be moved to GA quality with Aspire 13.1. Java 25, Node.js 24 Azure Functions now supports Java 25 and Node.js 24 in preview. You can now develop functions using these versions locally and deploy them to Azure Functions plans. Learn how to upgrade your apps to these versions here In Summary Ready to build what’s next? Update your Azure Functions Core Tools today and explore the latest samples and quickstarts to unlock new capabilities for your scenarios. The guided quickstarts run and deploy in under 5 minutes, and incorporate best practices—from architecture to security to deployment. We’ve made it easier than ever to scaffold, deploy, and scale real-world solutions with confidence. The future of intelligent, scalable, and secure applications starts now—jump in and see what you can create!Compose for Agents on Azure Container Apps and Serverless GPU (public preview)
Empowering intelligent applications The next wave of AI is agentic – systems that can reason, plan, and act on our behalf. Whether you’re building a virtual assistant that books travel or a multi‑model workflow that triages support tickets, these applications rely on multiple models, tools, and services working together. Unfortunately, building them has not been easy: Tooling sprawl. Developers must wire together LLMs, vector databases, MCP (Model Context Protocol) tools and orchestration logic, often across disparate SDKs and running processes. Keeping those dependencies in sync for local development and production is tedious and error‑prone. Specialized hardware. Large language models and agent orchestration frameworks often require GPUs to run effectively. Procuring and managing GPU instances can be costly, particularly for prototypes and small teams. Operational complexity. Agentic applications are typically composed of many services. Scaling them, managing health and secure connectivity, and reproducing the same environment from a developer laptop into production quickly becomes a full‑time job. Why Azure Container Apps is the right home With Azure Container Apps (ACA), you can now tackle these challenges without sacrificing the familiar Docker Compose workflow that so many developers love. We’re excited to announce that Compose for Agents is in public preview on Azure Container Apps. This integration brings the power of Docker’s new agentic tooling to a platform that was built for serverless containers. Here’s why ACA is the natural home for agentic workloads: Serverless GPUs with per‑second billing. Container Apps offers serverless GPU compute. Your agentic workloads can run on GPUs only when they need to, and you only pay for the seconds your container is actually running. This makes it economical to prototype and scale complex models without upfront infrastructure commitments. Media reports on the preview note that Docker’s Offload service uses remote GPUs via cloud providers such as Microsoft to overcome local hardware limits, and ACA brings that capability directly into the Azure native experience. Sandboxed dynamic sessions for tools. Many agentic frameworks execute user‑provided code as part of their workflows. ACA’s dynamic sessions provide secure, short‑lived sandboxes for running these tasks. This means untrusted or transient code (for example, evaluation scripts or third‑party plugins) runs in an isolated environment, keeping your production services safe. Fully managed scaling and operations. Container Apps automatically scales each service based on traffic and queue length, and it can scale down to zero when idle. You get built‑in service discovery, ingress, rolling updates and revision management without having to operate your own orchestrator. Developers can focus on building agents rather than patching servers. First‑class Docker Compose support. Compose remains a favourite tool for developers’ inner loop and for orchestrating multi‑container systems. Compose for Agents extends the format to declare open‑source models, agents and tools alongside your microservices. By pointing docker compose up at ACA, the same YAML file you use locally now deploys automatically to a fully managed container environment. Model Runner and MCP Gateway built in. Docker’s Model Runner lets you pull open‑weight language models from Docker Hub and exposes them via OpenAI‑compatible endpoints, and the MCP (Model Context Protocol) Gateway connects your agents to curated tools. ACA integrates these components into your Compose stack, giving you everything you need for retrieval‑augmented generation, vector search or domain‑specific tool invocation. What this means for developers The Compose for Agents public preview on Container Apps brings together the simplicity of Docker Compose and the operational power of Azure’s serverless compute platform. Developers can now: Define agent stacks declaratively. Instead of cobbling together scripts, you describe your entire agentic application in a single compose.yaml file. Compose already supports popular frameworks like LangGraph, Embabel, Vercel AI SDK, Spring AI, Crew AI, Google ADK and Agno. You can mix and match these frameworks with your own microservices, databases and queues. Run anywhere with the same configuration. Docker emphasizes that you can “define your open models, agents and MCP‑compatible tools, then spin up your full agentic stack with a simple docker compose up”. By bringing this workflow to ACA, Microsoft ensures that the same compose file runs unchanged on your laptop and in the cloud. Scale seamlessly. Large language models and multi‑agent orchestration can be compute‑intensive. News coverage notes that Docker’s Offload service provides remote GPUs for these workloads ACA extends that capability with serverless GPUs and automated scaling, letting you test locally and then burst to the cloud with no changes to your YAML. Collaboration with Docker This preview is the result of close collaboration between Microsoft and Docker. A Docker has always been focused on simplifying complex developer workflows. “With Compose for Agents, we’re extending that same experience that developers know and love from containers to agents, bringing the power of Compose to the emerging world of AI-native, agentic applications. It delivers the same simplicity and predictability to prototyping, testing, and deploying across local and cloud environments” said Elyi Aleyner, VP of Strategy and Head of Tech Alliances at Docker. “We’re excited to partner with Microsoft to bring this innovation to Azure Container Apps, enabling developers to go from ‘compose up’ on their laptops to secure, GPU-backed workloads in the cloud with zero friction.” Empowering choice Every team has its own favourite frameworks and tools. We’ve ensured that Compose for Agents on ACA is framework‑agnostic: you can use LangGraph for complex workflows, CrewAI for multi‑agent coordination, or Spring AI to integrate with your existing Java stack. Want to run a vector store from the MCP catalog alongside your own service? Simply add it to your Compose file. Docker’s curated catalog provides over a hundred ready‑to‑use tools and services for retrieval, document summarization, database access and more. ACA’s flexibility means you’re free to choose the stack that best fits your problem. Get started today The public preview of Compose for Agents support in Azure Container Apps is available now. You can: Install the latest Azure Container Apps Extension Define your application in a compose.yaml file, including models, tools and agent code and deploy to ACA via az containerapp compose up. ACA will provision GPU resources, dynamic sessions and auto‑scaling infrastructure automatically. Iterate locally using standard docker compose up commands, then push the same configuration to the cloud when you’re ready. For more detailed instructions please go to https://aka.ms/aca/compose-for-agentsWhat's new in Azure Container Apps at Ignite'25
Azure Container Apps (ACA) is a fully managed serverless container platform that enables developers to design and deploy microservices and modern apps without requiring container expertise or needing infrastructure management. ACA is rapidly emerging as the preferred platform for hosting AI workloads and intelligent agents in the cloud. With features like code interpreter, Serverless GPUs, simplified deployments, and per-second billing, ACA empowers developers to build, deploy, and scale AI-driven applications with exceptional agility. ACA makes it easy to integrate agent frameworks, leverage GPU acceleration, and manage complex, multi-container AI environments - all while benefiting from a serverless, fully managed infrastructure. External customers like Replit, NFL Combine, Coca-Cola, and European Space Agency as well as internal teams like Microsoft Copilot (as well as many others) have bet on ACA as their compute platform for AI workloads. ACA is quickly becoming the leading platform for updating existing applications and moving them to a cloud-native setup. It allows organizations to seamlessly migrate legacy workloads - such as Java and .NET apps - by using AI-powered tools like GitHub Copilot to automate code upgrades, analyze dependencies, and handle cloud transformations. ACA’s fully managed, serverless environment removes the complexity of container orchestration. This helps teams break down monolithic or on-premises applications into robust microservices, making use of features like version control, traffic management, and advanced networking for fast iteration and deployment. By following proven modernization strategies while ensuring strong security, scalability, and developer efficiency, ACA helps organizations continuously innovate and future-proof their applications in the cloud. Customers like EY, London Stock Exchange, Chevron, and Paychex have unlocked significant business value by modernizing their workloads onto ACA. This blog presents the latest features and capabilities of ACA, enhancing its value for customers by enabling the rapid migration of existing workloads and development of new cloud applications, all while following cloud-native best practices. Secure sandboxes for AI compute ACA now supports dynamic shell sessions, currently available in public preview. These shell sessions are platform-managed built-in containers designed to execute common shell commands within an isolated, sandboxed environment. With the addition of empty shell sessions and an integrated MCP server, ACA enables customers to provision secure, isolated sandboxes instantly - ideal for use cases such as code execution, tool testing, and workflow automation. This functionality facilitates seamless integration with agent frameworks, empowering agents to access disposable compute environments as needed. Customers can benefit from rapid provisioning, improved security, and decreased operational overhead when managing agentic workloads. To learn more about how to add secure sandbox shell sessions to Microsoft Foundry agents as a tool, visit the walkthrough at https://aka.ms/aca/dynamic-sessions-mcp-tutorial. Docker Compose for Agents support ACA has added Docker Compose for Agents support in public preview, making it easy for developers to define agentic applications stack-agnostic, with MCP and custom model support. Combined with native serverless GPU support, Docker Compose for Agents allows fast iteration and scaling for AI-driven agents and application using LangGraph, LangChain CrewAI, Spring AI, Vercel AI SDK and Agno. These enhancements provide a developer-focused platform that streamlines the process for modern AI workloads, bringing together both development and production cycles into one unified environment. Additional regional availability for Serverless GPUs Serverless GPU solutions offer capabilities such as automatic scaling with NVIDIA A100 or T4 GPUs, per-second billing, and strict data isolation within container boundaries. ACA Serverless GPUs are now generally available in 11 additional regions, further facilitating developers’ ability to deploy AI inference, model training, and GPU-accelerated workloads efficiently. For further details on supported regions, please visit https://aka.ms/aca/serverless-gpu-regions. New Flexible Workload Profile The Flexible workload profile is a new option that combines the simplicity of serverless Consumption with the performance and control in Dedicated profiles. It offers a familiar pay-per-use model along with enhanced features like scheduled maintenance, dedicated networking, and support for larger replicas to meet demanding application needs. Customers can enjoy the advantages of dedicated resources together with effortless infrastructure management and billing from the Consumption model. Operating on a dedicated compute pool, this profile ensures better predictability and isolation without introducing extra operational complexity. It is designed for users who want the ease of serverless scaling, but also need more control over performance and environmental stability. Confidential Computing Confidential computing support is now available in public preview for ACA, offering hardware-based Trusted Execution Environments (TEEs) to secure data in use. This adds to existing encryption of data at rest and in transit by encrypting memory and verifying the cloud environment before processing. It helps protect sensitive data from unauthorized access, including by cloud operators, and is useful for organizations with high security needs. Confidential computing can be enabled via workload profiles, with the preview limited to certain regions. Extending Network capabilities General Availability of Rule-based Routing Rule-based routing for ACA is now generally available, offering users improved flexibility and easier composition when designing microservice architectures, conducting A/B testing, or implementing blue-green deployments. With this feature, you can route incoming HTTP traffic to specific apps within your environment by specifying host names or paths - including support for custom domains. You no longer need to set up an extra reverse proxy (like NGINX); simply define routing rules for your environment, and traffic will be automatically directed to the appropriate target apps. General Availability of Premium Ingress ACA support for Premium Ingress is now Generally Available. This feature introduces environment-level ingress configuration options, with the primary highlight being customizable ingress scaling. This capability supports the scaling of the ingress proxy, enabling customers to better handle higher demand workloads, such as large performance tests. By configuring your ingress proxy to run on workload profiles, you can scale out more ingress instances to handle more load. Running the ingress proxy on a workload profile will incur associated costs. To further enhance the flexibility of your application, this release includes other ingress-related settings, such as termination grace period, idle request timeout, and header count. Additional Management capabilities Public Preview of Deployment labels ACA now offers deployment labels in public preview, letting you assign names like dev, staging, or prod to container revisions which can be automatically assigned. This makes environment management easier and supports advanced strategies such as A/B testing and blue-green deployments. Labels help route traffic, control revisions, and streamline rollouts or rollbacks with minimal hassle. With deployment labels, you can manage app lifecycles more efficiently and reduce complexity across environments. General Availability of Durable Task Scheduler support Durable Task Scheduler (DTS) support is now generally available on ACA, empowering users with a robust pro-code workflow solution. With DTS, you can define reliable, containerized workflows as code, benefiting from built-in state persistence and fault-tolerant execution. This enhancement streamlines the creation and administration of complex workflows by boosting scalability, reliability, and enabling efficient monitoring capabilities. What’s next ACA is redefining how developers build and deploy intelligent agents. Agents deployed to Azure Container Apps with Microsoft Agent Framework and Open Telemetry can also be plugged directly into Microsoft Foundry, giving teams a single pane of glass for their agents in Azure. With serverless scale, GPU-on-demand, and enterprise-grade isolation, ACA provides the ideal foundation for hosting AI agents securely and cost-effectively. Utilizing open-source frameworks such as n8n on ACA enables the deployment of no-code automation agents that integrate seamlessly with Azure OpenAI models, supporting intelligent routing, summarization, and adaptive decision-making processes. Similarly, running other agent frameworks like Goose AI Agent on ACA enables it to operate concurrently with model inference workloads (including Ollama and GPT-OSS) within a unified, secure environment. The inclusion of serverless GPU support allows for efficient hosting of large language models such as GPT-OSS, optimizing both cost and scalability for inference tasks. Furthermore, ACA facilitates the remote hosting of Model Context Protocol (MCP) servers, granting agents secure access to external tools and APIs via streamable HTTP transport. Collectively, these features enable organizations to develop, scale, and manage complex agentic workloads - from workflow automation to AI-driven assistants - while leveraging ACA’s enterprise-grade security, autoscaling capabilities, and developer-centric user experience. In addition to these, ACA also enables a wide range of cross-compatibility with various frameworks and services, making it an ideal platform for running Azure Functions on ACA, Distributed Application Runtime (Dapr) microservices, as well as polyglot apps across .NET / Java / JavaScript. As always, we invite you to visit our GitHub page for feedback, feature requests, or questions about Azure Container Apps, where you can open a new issue or up-vote existing ones. If you’re curious about what we’re working on next, check out our roadmap. We look forward to hearing from you!Reimagining AI Ops with Azure SRE Agent: New Automation, Integration, and Extensibility features
Azure SRE Agent offers intelligent and context aware automation for IT operations. Enhanced by customer feedback from our preview, the SRE Agent has evolved into an extensible platform to automate and manage tasks across Azure and other environments. Built on an Agentic DevOps approach - drawing from proven practices in internal Azure operations - the Azure SRE Agent has already saved over 20,000 engineering hours across Microsoft product teams operations, delivering strong ROI for teams seeking sustainable AIOps. An Operations Agent that adapts to your playbooks Azure SRE Agent is an AI powered operations automation platform that empowers SREs, DevOps, IT operations, and support teams to automate tasks such as incident response, customer support, and developer operations from a single, extensible agent. Its value proposition and capabilities have evolved beyond diagnosis and mitigation of Azure issues, to automating operational workflows and seamless integration with the standards and processes used in your organization. SRE Agent is designed to automate operational work and reduce toil, enabling developers and operators to focus on high-value tasks. By streamlining repetitive and complex processes, SRE Agent accelerates innovation and improves reliability across cloud and hybrid environments. In this article, we will look at what’s new and what has changed since the last update. What’s New: Automation, Integration, and Extensibility Azure SRE Agent just got a major upgrade. From no-code automation to seamless integrations and expanded data connectivity, here’s what’s new in this release: No-code Sub-Agent Builder: Rapidly create custom automations without writing code. Flexible, event-driven triggers: Instantly respond to incidents and operational changes. Expanded data connectivity: Unify diagnostics and troubleshooting across more data sources. Custom actions: Integrate with your existing tools and orchestrate end-to-end workflows via MCP. Prebuilt operational scenarios: Accelerate deployment and improve reliability out of the box. Unlike generic agent platforms, Azure SRE Agent comes with deep integrations, prebuilt tools, and frameworks specifically for IT, DevOps, and SRE workflows. This means you can automate complex operational tasks faster and more reliably, tailored to your organization’s needs. Sub-Agent Builder: Custom Automation, No Code Required Empower teams to automate repetitive operational tasks without coding expertise, dramatically reducing manual workload and development cycles. This feature helps address the need for targeted automation, letting teams solve specific operational pain points without relying on one-size-fits-all solutions. Modular Sub-Agents: Easily create custom sub-agents tailored to your team’s needs. Each sub-agent can have its own instructions, triggers, and toolsets, letting you automate everything from outage response to customer email triage. Prebuilt System Tools: Eliminate the inefficiency of creating basic automation from scratch, and choose from a rich library of hundreds of built-in tools for Azure operations, code analysis, deployment management, diagnostics, and more. Custom Logic: Align automation to your unique business processes by defining your automation logic and prompts, teaching the agent to act exactly as your workflow requires. Flexible Triggers: Automate on Your Terms Invoke the agent to respond automatically to mission-critical events, not wait for manual commands. This feature helps speed up incident response and eliminate missed opportunities for efficiency. Multi-Source Triggers: Go beyond chat-based interactions, and trigger the agent to automatically respond to Incident Management and Ticketing systems like PagerDuty and ServiceNow, Observability Alerting systems like Azure Monitor Alerts, or even on a cron-based schedule for proactive monitoring and best-practices checks. Additional trigger sources such as GitHub issues, Azure DevOps pipelines, email, etc. will be added over time. This means automation can start exactly when and where you need it. Event-Driven Operations: Integrate with your CI/CD, monitoring, or support systems to launch automations in response to real-world events - like deployments, incidents, or customer requests. Vital for reducing downtime, it ensures that business-critical actions happen automatically and promptly. Expanded Data Connectivity: Unified Observability and Troubleshooting Integrate data, enabling comprehensive diagnostics and troubleshooting and faster, more informed decision-making by eliminating silos and speeding up issue resolution. Multiple Data Sources: The agent can now read data from Azure Monitor, Log Analytics, and Application Insights based on its Azure role-based access control (RBAC). Additional observability data sources such as Dynatrace, New Relic, Datadog, and more can be added via the Remote Model Context Protocol (MCP) servers for these tools. This gives you a unified view for diagnostics and automation. Knowledge Integration: Rather than manually detailing every instruction in your prompt, you can upload your Troubleshooting Guide (TSG) or Runbook directly, allowing the agent to automatically create an execution plan from the file. You may also connect the agent to resources like SharePoint, Jira, or documentation repositories through Remote MCP servers, enabling it to retrieve needed files on its own. This approach utilizes your organization’s existing knowledge base, streamlining onboarding and enhancing consistency in managing incidents. Azure SRE Agent is also building multi-agent collaboration by integrating with PagerDuty and Neubird, enabling advanced, cross-platform incident management and reliability across diverse environments. Custom Actions: Automate Anything, Anywhere Extend automation beyond Azure and integrate with any tool or workflow, solving the problem of limited automation scope and enabling end-to-end process orchestration. Out-of-the-Box Actions: Instantly automate common tasks like running azcli, kubectl, creating GitHub issues, or updating Azure resources, reducing setup time and operational overhead. Communication Notifications: The SRE Agent now features built-in connectors for Outlook, enabling automated email notifications, and for Microsoft Teams, allowing it to post messages directly to Teams channels for streamlined communication. Bring Your Own Actions: Drop in your own Remote MCP servers to extend the agent’s capabilities to any custom tool or workflow. Future-proof your agentic DevOps by automating proprietary or emerging processes with confidence. Prebuilt Operations Scenarios Address common operational challenges out of the box, saving teams time and effort while improving reliability and customer satisfaction. Incident Response: Minimize business impact and reduce operational risk by automating detection, diagnosis, and mitigation of your workload stack. The agent has built-in runbooks for common issues related to many Azure resource types including Azure Kubernetes Service (AKS), Azure Container Apps (ACA), Azure App Service, Azure Logic Apps, Azure Database for PostgreSQL, Azure CosmosDB, Azure VMs, etc. Support for additional resource types is being added continually, please see product documentation for the latest information. Root Cause Analysis & IaC Drift Detection: Instantly pinpoint incident causes with AI-driven root cause analysis including automated source code scanning via GitHub and Azure DevOps integration. Proactively detect and resolve infrastructure drift by comparing live cloud environments against source-controlled IaC, ensuring configuration consistency and compliance. Handle Complex Investigations: Enable the deep investigation mode that uses a hypothesis-driven method to analyze possible root causes. It collects logs and metrics, tests hypotheses with iterative checks, and documents findings. The process delivers a clear summary and actionable steps to help teams accurately resolve critical issues. Incident Analysis: The integrated dashboard offers a comprehensive overview of all incidents managed by the SRE Agent. It presents essential metrics, including the number of incidents reviewed, assisted, and mitigated by the agent, as well as those awaiting human intervention. Users can leverage aggregated visualizations and AI-generated root cause analyses to gain insights into incident processing, identify trends, enhance response strategies, and detect areas for improvement in incident management. Inbuilt Agent Memory: The new SRE Agent Memory System transforms incident response by institutionalizing the expertise of top SREs - capturing, indexing, and reusing critical knowledge from past incidents, investigations, and user guidance. Benefit from faster, more accurate troubleshooting, as the agent learns from both successes and mistakes, surfacing relevant insights, runbooks, and mitigation strategies exactly when needed. This system leverages advanced retrieval techniques and a domain-aware schema to ensure every on-call engagement is smarter than the last, reducing mean time to resolution (MTTR) and minimizing repeated toil. Automatically gain a continuously improving agent that remembers what works, avoids past pitfalls, and delivers actionable guidance tailored to the environment. GitHub Copilot and Azure DevOps Integration: Automatically triage, respond to, and resolve issues raised in GitHub or Azure DevOps. Integration with modern development platforms such as GitHub Copilot coding agent increases efficiency and ensures that issues are resolved faster, reducing bottlenecks in the development lifecycle. Ready to get started? Azure SRE Agent home page Product overview Pricing Page Pricing Calculator Pricing Blog Demo recordings Deployment samples What’s Next? Give us feedback: Your feedback is critical - You can Thumbs Up / Thumbs Down each interaction or thread, or go to the “Give Feedback” button in the agent to give us in-product feedback - or you can create issues or just share your thoughts in our GitHub repo at https://github.com/microsoft/sre-agent. We’re just getting started. In the coming months, expect even more prebuilt integrations, expanded data sources, and new automation scenarios. We anticipate continuous growth and improvement throughout our agentic AI platforms and services to effectively address customer needs and preferences. Let us know what Ops toil you want to automate next!
How-to add an MCP tool to your Azure Foundry agent using dynamic sessions on Azure Container Apps
Now available in preview, Azure Container Apps supports a new shell session pool type in dynamic sessions as well as MCP support for both shell and Python container types. With MCP enablement, you can bring the benefits of dynamics sessions to your AI agents by giving them the ability to execute code, run system commands, access file systems, and perform complex tasks in isolated, secure container environments that appear and disappear on demand. The following is a tutorial on how to use an MCP enabled dynamic session as a tool used in an Azure Foundry agent giving it the capability to run remote shell commands. Setup For this tutorial, we're going to use an ARM template to deploy our Container App Session Pool resource. Begin by signing into Azure and creating a resource group. az login After you're logged in, set the following variables that will be used to deploy the resource. Replace the <> with your own values. SUBSCRIPTION_ID=$(az account show --query id --output tsv) RESOURCE_GROUP=<RESOURCE_GROUP_NAME> SESSION_POOL_NAME=<SESSION_POOL_NAME> LOCATION=<LOCATION> Create a resource group az group create --name $RESOURCE_GROUP --location $LOCATION 1. Create a shell dynamic session with MCP enabled Use an ARM template to create a shell session pool with MCP server enabled. Create a deployment template file named deploy.json and copy the following into the file: { "$schema": "http://schema.management.azure.com/schemas/2015-01-01/deploymentTemplate.json#", "contentVersion": "1.0.0.0", "parameters": { "name": { "type": "String" }, "location": { "type": "String" } }, "resources": [ { "type": "Microsoft.App/sessionPools", "apiVersion": "2025-02-02-preview", "name": "[parameters('name')]", "location": "[parameters('location')]", "properties": { "poolManagementType": "Dynamic", "containerType": "Shell", "scaleConfiguration": { "maxConcurrentSessions": 5 }, "sessionNetworkConfiguration": { "status": "EgressEnabled" }, "dynamicPoolConfiguration": { "lifecycleConfiguration": { "lifecycleType": "Timed", "coolDownPeriodInSeconds": 300 } }, "mcpServerSettings": { "isMCPServerEnabled": true } } } ] } Next, deploy the ARM template you just created. az deployment group create --resource-group $RESOURCE_GROUP --template-file deploy.json --name $SESSION_POOL_NAME --location $LOCATION Once completed, your resource will be provisioned and available in the portal. Get the MCP server endpoint and credentials Next, retrieve the MCP server endpoint from the deployed container app session pool. az rest --method GET --uri "/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.App/sessionPools/$SESSION_POOL_NAME?api-version=2025-02-02-preview" --query "properties.mcpServerSettings.mcpServerEndpoint" -o tsv Then, request the API credentials for the MCP server az rest --method POST --uri "/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.App/sessionPools/$SESSION_POOL_NAME/fetchMCPServerCredentials?api-version=2025-02-02-preview" --query "apiKey" -o tsv Keep the fetched values nearby and copied to be used in the next step to connect your MCP server as a tool in the Azure Foundry agent. 2. Create an Azure Foundry project and agent Go to the Azure Foundry website and follow the steps below to create a project. Make sure you are in the New Foundry portal by turning it on in the top nav. Navigate to the top left nav bar and click an existing project and click Create new project Give your project a Name Click on Advanced options to choose and fill out the following Foundry resource Subscription Region Resource group After filling the previous options out, click Create Once your project is created, follow the steps below to create an agent. Click on the Build tab on the top right navigation bar Navigate to the Agents section in the left nav Click on the Create agent button Give your agent a name and click Create 3. Connect your remote MCP server as a Tool to your agent After your agent is created, you can connect a remote MCP server as a tool for your agent to use. Follow the steps below to connect an MCP server to your agent. Navigate to Tools on the left nav bar Click on the Connect a tool button Go to the Custom tab Click on the Model Context Protocol (MCP) option, click Create Fill out the form on the following screen and click Create. Use the MCP server endpoint and API credentials from the previous section. Name="your-unique-name" Remote MCP server endpoint="your-mcp-server-endpoint" Authentication type= "Key-based" Credential= Key:"x-ms-apikey" Value: "your-api-key" Once the tool is created you will see a Use in an agent button. Click on that and choose the agent you previously created. After choosing the agent you should now see a connected tool in your agent view. 4. Use the connected tool to run shell commands with your agent Now that the tool is connected you can use the playground to prompt the agent with a question that will launch the shell environment. Try the following prompt with your agent: "Create a hello world flask app in a new remote environment. Then send a request to the app to show that it works." Once prompted, the connected MCP server will use it's tools to launch a shell environment and create an environmentId. This is used to track your session by creating a unique GUID so future commands are run in the same session. The Foundry agent will then ask for approval for each step as the connected MCP tool will execute a series of shell commands that will install Python and Flask. Once all requests have been completed and approved, you'll notice it has run the necessary shell commands in the remote environment and sent a curl request to the app with a response of Hello, World! In this tutorial, we demonstrated how to extend Azure AI Foundry agents using Azure Container Apps dynamic session pools. By enabling MCP support on a shell session pool, we created a secure, on-demand execution environment that allows AI agents to run remote shell commands. With this setup, your AI agents can now bridge the gap between conversation and action, providing users with interactive and useful experiences.Azure Functions on Azure Container Apps: The Unified Platform for Event-Driven and Finite Workloads
Overview Azure Functions on Azure Container Apps (ACA) integrates the high-productivity model of Function-as-a-Service (FaaS) with the flexible, cloud-native hosting environment of Azure Container Apps. Functions deployed in this manner run as continuous services ("Apps"), providing event-driven responsiveness while simultaneously possessing the mechanisms to handle tasks that traditionally have a definite start and end time. This platform utilizes a rich set of triggers and bindings and incorporates advanced Azure Container Apps features, enabling it to execute virtually any containerized workload. Use Cases for Azure Functions on Azure Container Apps Azure Functions on Azure Container Apps is ideally suited for all event-driven scenarios and can robustly handle tasks conventionally categorized as discrete, finite, or batch processing: Scenario Type Implementation via Functions on Azure Container Apps Rationale Scheduled Tasks Use Timer triggers that execute code on predefined timed intervals, such as executing data clean-up code or generating reports. Timer triggers reliably ensure code execution at specific, recurring intervals, defining a discrete task timeframe. Batch or Stream Processing Use Event Hubs triggers to capture and transform data from IoT or event source streams or use Blob/Queue triggers in conjunction with durable execution patterns (like Fan-out/Fan-in) to process large datasets. Functions are adept at processing and transforming data immediately upon event arrival. Machine Learning (Inference/Processing) Functions can run AI inference by pulling data from a queue or integrating with services using bindings. GPU support is available on Azure Container Apps for compute-intensive ML workloads. Functions can wrap the complex logic needed for model processing and leverage highly optimized hardware resources available on Azure Container Apps Event-Driven Workloads (Discrete) Use Queue Storage triggers or Service Bus triggers where a message arrival instantly triggers processing. Durable Functions can orchestrate this workload. Functions excel at immediate response to messages and events, managing message queues and processing event streams. On-Demand Processing Use HTTP triggers as webhooks or APIs to initiate processing upon request. For asynchronous work, the HTTP trigger can defer the actual work to a queue-triggered function. HTTP endpoints allow manual or programmatic initiation of any workload, providing on-demand execution. CI/CD Runners (Agent Execution) Although typically containerized tasks, the required trigger logic (e.g., queue events) can be managed by Functions. The containerized function itself executes the necessary code in response to the event. Functions provide the event processing, scaling, and execution environment necessary to run code triggered by external CI/CD platforms. Additional Scenarios Unique to Functions on Azure Container Apps Functions deployed on Azure Container Apps, support several critical capabilities as mentioned below GPU workloads: Supports hosting options for specialized hardware, including GPU-enabled compute resources via serverless GPU or Dedicated workload profiles for highly intensive applications like AI/ML workloads. Flexible Functions Programming model with Event Driven Triggers: Provides a managed Function-as-a-Service (FaaS) environment focused on running code blocks. Supports a comprehensive and expansive set of event-driven triggers, including HTTP, Timer, Azure Storage (Queue, Blob), Event Hubs, Azure Cosmos DB, and Service Bus Complex Stateful Workflows: Azure Functions natively supports Durable Functions which supports stateful workflows using imperative code. Supports asynchronous HTTP APIs for coordinating long-running processes, monitoring, and handling human interaction. For APIs requiring higher processing time (beyond 1 hr. of processing limit), scale by tuning infra based as per the need. Scalable Web API/Ingress Endpoints: Functions can be deployed specifically to build scalable REST endpoints using HTTP triggers. This leverages Azure Container App's native ingress capabilities, with flexibility to define your own ingress settings for advanced, customizable, and scalable handling of external traffic. Advanced Deployment Management: Supports features like multi-revisions and traffic splitting. This enables complex deployment strategies like phased rollouts and blue/green deployments for zero-downtime updates. Integrated Microservices Patterns (Dapr): Functions on Azure Container Apps have built-in Dapr support, allowing immediate utilization of sidecars for functionalities like secure Service Invocation, Pub/Sub messaging, and state management, essential for modern microservices architectures.Hosting Remote MCP Server on Azure Container Apps (ACA) using Streamable HTTP transport mechanism
About Continuing from the earlier article of Hosting Remote MCP Server on Azure Container Apps (ACA) using SSE transport mechanism This blog showcases the Hosting Remote MCP Servers on Azure Container Apps (ACA) as HTTP type transport. Overview The Model Context Protocol (MCP) has revolutionized how AI assistants interact with external tools and data sources. While many examples focus on local implementations using stdio transport, this post demonstrates how to build and deploy a production-ready MCP server using HTTP transport in Azure Container Apps. In this article, we create a live forex converter that fetches real-time exchange rates from external APIs, showcasing how MCP servers can integrate with third-party services to provide dynamic, up-to-date information to AI assistants. What is MCP HTTP Transport? MCP supports multiple transport mechanisms, with HTTP being ideal for cloud deployments: Stdio Transport: {"type": "stdio"} - Direct process communication HTTP Transport: {"type": "http"} - RESTful API communication HTTP transport enables: Cloud deployment and scaling Cross-platform compatibility Multiple client connections Load balancing and high availability Integration with external APIs Real-time data fetching from third-party services Building and testing the MCP Server from the GitHub Repo Follow the steps to clone the code on your local machine and test the server locally # Clone the repository git clone https://github.com/deepganguly/azure-container-apps-mcp-sample.git # Navigate to the project directory cd azure-container-apps-mcp-sample # Install dependencies npm install # Test Locally, Run the MCP server npm start # Test the server (in another terminal) curl -X POST http://localhost:3001/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' The `server.js` (checkout the file from the above repository for more details) fetches live forex exchange rates from a Third-Party API and servers as live price converter for any requests. It performs the following functions 1. Exchange Rate Management Caches live exchange rates from exchangerate-api.com for 10 minutes Fetches fresh rates when cache expires or is empty Falls back to hardcoded rates if the external API fails (USD, EUR, GBP, JPY, INR, CAD, AUD, CHF, CNY) 2. Exchange Rate Management Listens on /mcp endpoint for POST requests with JSON-RPC 2.0 format Handles tools/list method - Returns available tools (convert_currency) Handles tools/call method - Executes currency conversion requests Returns proper MCP responses with jsonrpc, id, and result/error fields // Fetch live exchange rates from exchangerate-api.com (free, no API key needed) async function getLiveRates() { try { // Check cache first if (ratesCache && cacheTimestamp && (Date.now() - cacheTimestamp) < CACHE_DURATION) { return ratesCache; } console.log('Fetching live exchange rates...'); const response = await fetch('https://api.exchangerate-api.com/v4/latest/USD'); const data = await response.json() 3. Currency Conversion Logic USD as base conversion - Direct rate lookup for USD to other currencies Other to USD conversion - Uses inverse rate (1/rate) Cross-currency conversion - Converts through USD (from→USD→to) Calculates exchange rate and final converted amount 4. Response Formatting Success response - Returns formatted text with amount, converted value, live rate, and timestamp Error handling - Returns proper JSON-RPC error responses for failures Deploy the App to Azure Container Apps The code can be deployed with the following commands. Also, check out the main.bicep file provided in the repository for quick one step deployment # Clone the repository git clone https://github.com/deepganguly/azure-container-apps-mcp-sample.git #Login to Azure az login # Create resource group az group create --name mcp-live-rates-rg --location eastus # Create Container App environment az containerapp env create --name mcp-forex-env --resource-group mcp-live-rates-rg --location eastus # Deploy container app az containerapp up --name mcp-live-forex-server --resource-group mcp-live-rates-rg --environment mcp-forex-env --source . --target-port 3001 --ingress external Configure Easy Auth for the Endpoint Once the application is deployed, enable OAuth based authentication for the endpoint. Checkout the git for more details # Create Azure AD app registration az ad app create --display-name "YOUR_APP_DISPLAY_NAME" --sign-in-audience AzureADMyOrg # Enable ID token issuance - required for Easy Auth az ad app update --id "YOUR_APP_ID" --enable-id-token-issuance true # Add the redirect URI for Easy Auth callback az ad app update \ --id "YOUR_APP_ID" \ --web-redirect-uris "https://YOUR_APP_NAME_WITH_AUTH.YOUR_ENVIRONMENT_DOMAIN/.auth/login/aad/callback" # Generate a client secret az ad app credential reset --id "YOUR_APP_ID" --display-name "YOUR_APP_NAME-Secret" # Create service principal - required for proper authentication az ad sp create --id "YOUR_APP_ID" # Configure Microsoft Entra ID authentication with the App az containerapp auth microsoft update \ --name YOUR_APP_NAME_WITH_AUTH \ --resource-group YOUR_RESOURCE_GROUP \ --client-id "YOUR_APP_ID" \ --client-secret "YOUR_CLIENT_SECRET" \ --tenant-id "YOUR_TENANT_ID" \ --yes Connect the MCP Server with VS Code Chat Step 1: Get Your Deployed Server URL After deployment, your server is available at: https://mcp-live-forex-server.****.eastus.azurecontainerapps.io/ Key Endpoints: - MCP Endpoint: https://mcp-live-forex-server.****.eastus.azurecontainerapps.io/mcp - Health Check: https://mcp-live-forex-server.****.eastus.azurecontainerapps.io//health Step 2: Configure VS Code Settings 1. Open VS Code 2. Navigate to .vscode/mcp.json file with the following configurations { "servers": { "my-mcp-server-1a118d61": { "url": "https://mcp-live-forex-server.****.eastus.azurecontainerapps.io/mcp", "type": "http" } }, "inputs": [ { "id": "convert_currency", "type": "promptString", "description": "Convert {amount} {from} to {to} using live exchange rates" } ] } 3. Add the mcp.json as a Server 4. Reload the Window and query the VS code chat client Following picture showcases the Add Server configuration from the mcp.json file and the follow up conversation for the exchange rates Conclusion In this article we see the approach that enables to run serverless functions in a fully managed, scalable container environment, leveraging the flexibility of containers and the power of Azure Container Apps. You can now monitor, scale, and update your app easily using Azure tools and CLI.
