Merging changes synced from https://github.com/MicrosoftDocs/azure-docs-pr (branch live)

Author: Learn Build Service GitHub App

.openpublishing.redirection.json

@@ -2394,6 +2394,11 @@
       "redirect_url": "/azure/architecture/web-apps/guides/multi-region-app-service/multi-region-app-service",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/app-service/app-service-configure-premium-tier.md",
+      "redirect_url": "/azure/app-service/app-service-configure-premium-v4-tier",
+      "redirect_document_id": false
+    },           
     {
       "source_path_from_root": "/articles/public-multi-access-edge-compute-mec/tutorial-create-vm-using-python-sdk.md",
       "redirect_url": "/previous-versions/azure/public-multi-access-edge-compute-mec/tutorial-create-vm-using-python-sdk",
@@ -6851,7 +6856,27 @@
     },
     {
       "source_path_from_root": "/articles/azure-functions/migration.md",
-      "redirect_url": "/azure/azure-functions/migration/migrate-lambda-workloads-overview",
+      "redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/azure-functions/migration/migrate-lambda-workloads-overview.md",
+      "redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/azure-functions/migration/discover-lambda-workloads.md",
+      "redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/azure-functions/migration/assess-lambda-workloads.md",
+      "redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/azure-functions/migration/migrate-lambda-workloads.md",
+      "redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
       "redirect_document_id": false
     },
     {

articles/api-center/includes/about-mcp-servers.md

@@ -0,0 +1,43 @@
+---
+title: Include file
+description: Include file
+services: api-center
+author: dlepow
+
+ms.service: azure-api-center
+ms.topic: include
+ms.date: 05/12/2025
+ms.author: danlep
+ms.custom: Include file
+---
+
+## About MCP servers
+
+AI agents are becoming widely adopted because of enhanced large language model (LLM) capabilities. However, even the most advanced models face limitations because of their isolation from external data. Each new data source potentially requires custom implementations to extract, prepare, and make data accessible for the models.
+
+The [model context protocol](https://www.anthropic.com/news/model-context-protocol) (MCP) helps solve this problem. MCP is an open standard for connecting AI models and agents with external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet, such as remote databases or APIs).
+
+### MCP architecture
+
+The following diagram illustrates the MCP architecture:
+ 
+:::image type="content" source="media/about-mcp-servers/mcp-architecture.png" alt-text="Diagram of model context protocol (MCP) architecture.":::
+
+The architecture consists of the following components:
+
+| Component      | Description                                                                                     |
+|----------------|-------------------------------------------------------------------------------------------------|
+| **MCP hosts**  | LLM applications such as chat apps or AI assistants in your IDEs (like GitHub Copilot in Visual Studio Code) that need to access external capabilities |
+| **MCP clients**| Protocol clients, inside the host application, that maintain 1:1 connections with servers        |
+| **MCP servers**| Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients |
+| **MCP protocol**| Transport layer in the middle        |
+
+MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it connects to the MCP server. The MCP server then connects to, for example, a database or an API. MCP hosts and servers connect with each other through the MCP protocol.
+
+### Remote versus local MCP servers
+
+MCP utilizes a client-host-server architecture built on [JSON-RPC 2.0 for messaging](https://modelcontextprotocol.io/docs/concepts/architecture). Communication between clients and servers occurs over defined transport layers, and supports primarily two modes of operation:
+
+* **Remote MCP servers** - MCP clients connect to MCP servers over the internet, establishing a connection using HTTP and server-sent events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth.
+
+* **Local MCP servers** MCP clients connect to MCP servers on the same machine, using standard input/output as a local transport method.

articles/api-center/media/register-discover-mcp-server/mcp-architecture.png renamed to articles/api-center/includes/media/about-mcp-servers/mcp-architecture.png

@@ -14,36 +14,7 @@ ms.author: danlep
 
 This article describes how to use Azure API Center to maintain an inventory (or *registry*) of remote model context protocol (MCP) servers and help stakeholders discover them using the API Center portal. MCP servers expose backend APIs or data sources in a standard way to AI agents and models that consume them.
 
-## About MCP servers
-
-AI agents are becoming widely adopted because of enhanced large language model (LLM) capabilities. However, even the most advanced models face limitations because of their isolation from external data. Each new data source potentially requires custom implementations to extract, prepare, and make data accessible for the models.
-
-The [model context protocol](https://www.anthropic.com/news/model-context-protocol) (MCP) helps solve this problem. MCP is an open standard for connecting AI models and agents with external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet, such as remote databases or APIs).
-
-### MCP architecture
-
-The following diagram illustrates the MCP architecture:
- 
-:::image type="content" source="media/register-discover-mcp-server/mcp-architecture.png" alt-text="Diagram of model context protocol (MCP) architecture.":::
-
-The architecture consists of the following components:
-
-| Component      | Description                                                                                     |
-|----------------|-------------------------------------------------------------------------------------------------|
-| **MCP hosts**  | LLM applications such as chat apps or AI assistants in your IDEs (like GitHub Copilot in Visual Studio Code) that need to access external capabilities |
-| **MCP clients**| Protocol clients, inside the host application, that maintain 1:1 connections with servers        |
-| **MCP servers**| Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients |
-| **MCP protocol**| Transport layer in the middle        |
-
-MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it connects to the MCP server. The MCP server then connects to, for example, a database or an API. MCP hosts and servers connect with each other through the MCP protocol.
-
-### Remote versus local MCP servers
-
-MCP utilizes a client-host-server architecture built on [JSON-RPC 2.0 for messaging](https://modelcontextprotocol.io/docs/concepts/architecture). Communication between clients and servers occurs over defined transport layers, and supports primarily two modes of operation:
-
-* **Remote MCP servers** - MCP clients connect to MCP servers over the internet, establishing a connection using HTTP and server-sent events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth.
-
-* **Local MCP servers** MCP clients connect to MCP servers on the same machine, using standard input/output as a local transport method.
+[!INCLUDE [about-mcp-servers](includes/about-mcp-servers.md)]
 
 ## MCP servers in your API inventory
 

articles/api-center/register-discover-mcp-server.md

@@ -212,6 +212,8 @@
         href: azure-openai-api-from-specification.md
       - name: Authenticate and authorize to Azure OpenAI
         href: api-management-authenticate-authorize-azure-openai.md
+      - name: Expose REST API as MCP server
+        href: export-rest-mcp-server.md
       - name: Semantic caching for Azure OpenAI API requests
         href: azure-openai-enable-semantic-caching.md
       - name: Protect Azure OpenAI keys

articles/api-management/TOC.yml

@@ -1,12 +1,12 @@
 ---
-title: Capacity metrics - Azure API Management | Microsoft Docs
+title: Capacity Metrics - Azure API Management
 description: This article explains the capacity metrics in Azure API Management and how to make informed decisions about whether to scale an instance.
 services: api-management
 author: dlepow
 
 ms.service: azure-api-management
 ms.topic: how-to
-ms.date: 10/01/2024
+ms.date: 04/17/2025
 ms.author: danlep
 ms.custom: fasttrack-edit
 ---
@@ -19,18 +19,18 @@ API Management provides [Azure Monitor metrics](api-management-howto-use-azure-m
 
 This article explains the capacity metrics and how they behave, shows how to access capacity metrics in the Azure portal, and suggests when to consider scaling or upgrading your API Management instance.
 
-[!INCLUDE [api-management-workspace-availability](../../includes/api-management-workspace-availability.md)]
-
 > [!IMPORTANT]
-> This article introduces how to monitor and scale your Azure API Management instance based on capacity metrics. However, when an instance *reaches* its capacity, it won't throttle to prevent overload. Instead, it will act like an overloaded web server: increased latency, dropped connections, and timeout errors. API clients should be ready to handle these issues as they do with other external services, for example by using retry policies.
+> This article introduces how to monitor and scale your Azure API Management instance based on capacity metrics. However, when an instance *reaches* its capacity, it won't throttle to prevent overload. Instead, it will act like an overloaded web server: increased latency, dropped connections, and time-out errors. API clients should be ready to handle these issues as they do with other external services, for example by using retry policies.
+
+[!INCLUDE [api-management-workspace-try-it](../../includes/api-management-workspace-try-it.md)]
 
 ## Prerequisites
 
 To follow the steps in this article, you must have an API Management instance in one of the tiers that supports capacity metrics. For more information, see [Create an Azure API Management instance](get-started-create-service-instance.md).
 
 ## Available capacity metrics
 
-Different capacity metrics are available in the [v2 service tiers](v2-service-tiers-overview.md) and classic tiers. 
+Different capacity metrics are available in the [v2 service tiers](v2-service-tiers-overview.md), classic tiers, and [workspace gateways](workspaces-overview.md#workspace-gateway). 
 
 #### [v2 tiers](#tab/v2-tiers)
 
@@ -66,6 +66,21 @@ Available aggregations for this metric are as follows.
 
 [!INCLUDE [api-management-cpu-memory-capacity](../../includes/api-management-cpu-memory-capacity.md)]
 
+#### [Workspace gateways](#tab/workspace-gateway)
+
+In a workspace gateway, the following metrics are available:
+
+* **CPU Utilization (%)** - The percentage of CPU capacity used by the gateway units.
+
+* **Memory Utilization (%)** - The percentage of memory capacity used by the gateway units.
+
+Available aggregations for these metrics are as follows.
+
+* **Avg** - Average percentage of capacity used across gateway processes in every [unit](upgrade-and-scale.md) of a workspace gateway. 
+* **Max** - Percentage of capacity in gateway process with the greatest consumption.  
+
+[!INCLUDE [api-management-cpu-memory-capacity](../../includes/api-management-cpu-memory-capacity.md)]
+
 ---
 
 ## Capacity metric behavior
@@ -85,7 +100,7 @@ The more complex operations on the requests are, the higher the capacity consump
 
 Capacity metrics can also spike intermittently or be greater than zero even if no requests are being processed. It happens because of system- or platform-specific actions and should not be taken into consideration when deciding whether to scale an instance.
 
-Although capacity metrics are designed to surface problems with your API Management instance, there are cases when problems won't be reflected in changes in these metrics. Additionally, low capacity metrics don't necessarily mean that your API Management instance isn't experiencing any problems.
+Although capacity metrics are designed to surface problems with your API Management instance (or workspace gateway), there are cases when problems won't be reflected in changes in these metrics. Additionally, low capacity metrics don't necessarily mean that your API Management instance isn't experiencing any problems.
 
 
 ## Use the Azure portal to examine capacity metrics
@@ -128,19 +143,32 @@ Access metrics in the portal to understand how much capacity is used over time.
 > * You can use Azure Monitor [autoscaling](api-management-howto-autoscale.md) to automatically add an Azure API Management unit. Scaling operation can take around 30 minutes, so you should plan your rules accordingly.  
 > * In multi-region deployments, only scaling the primary location is allowed.
 
+#### [Workspace gateways](#tab/workspace-gateway) 
+
+1. Navigate to your API Management instance in the [Azure portal](https://portal.azure.com/).
+1. In the left menu, under **APIs**, select **Workspaces** > the workspace associated with the gateway that you want to monitor.
+1. In the left menu, under **Deployment + infrastructure**, select **Gateways** > the workspace gateway that you want to monitor.
+1. In the left menu, under **Monitoring**, select **Metrics**.
+1. Select the **CPU Utilization (%)** or **Memory Utilization (%)** metric from the available metrics. Choose the default **Avg** aggregation or select the **Max** aggregation to see the peak usage.
+1. Pick a desired timeframe from the top bar of the section.
+
+> [!NOTE]
+> You can set a [metric alert](api-management-howto-use-azure-monitor.md#set-up-an-alert-rule) to let you know when something unexpected is happening. For example, get notifications when your workspace gateway has exceeded its expected peak CPU or Memory usage for more than 20 minutes.
+
+
 ---
 
 ## Use capacity for scaling decisions
 
-Use capacity metrics for making decisions whether to scale an API Management instance to accommodate more load. The following are general considerations:
+Use capacity metrics for making decisions whether to scale an API Management instance (or workspace gateway) to accommodate more load. The following are general considerations:
 
 + Look at a long-term trend and average.
 + Ignore sudden spikes that are most likely not related to an increase in load (see [Capacity metric behavior](#capacity-metric-behavior) section for explanation).
 + As a general rule, upgrade or scale your instance when a capacity metric value exceeds **60% - 70%** for a long period of time (for example, 30 minutes). Different values may work better for your service or scenario.
-+ If your instance is configured with only 1 unit, upgrade or scale your instance when a capacity metric value exceeds **40%** for a long period. This recommendation is based on the need to reserve capacity for guest OS updates in the underlying service platform.
++ If your instance or workspace gateway is configured with only 1 unit, upgrade or scale it when a capacity metric value exceeds **40%** for a long period. This recommendation is based on the need to reserve capacity for guest OS updates in the underlying service platform.
 
->[!TIP]  
-> If you are able to estimate your traffic beforehand, test your API Management instance on workloads you expect. You can increase the request load on your tenant gradually and monitor the value of the capacity metric that corresponds to your peak load. Follow the steps from the previous section to use Azure portal to understand how much capacity is used at any given time.
+> [!TIP]  
+> If you are able to estimate your traffic beforehand, test your API Management instance or workspace gateway on workloads you expect. You can increase the request load gradually and monitor the value of the capacity metric that corresponds to your peak load. Follow the steps from the previous section to use Azure portal to understand how much capacity is used at any given time.
 
 > [!IMPORTANT]
 > When defining autoscaling rules, make sure to avoid autoscaling flapping that will cause a scale in/scale out loop. To learn more, we recommend reading [Azure Monitor Autoscale's guidance around flapping](/azure/azure-monitor/autoscale/autoscale-flapping).

articles/api-management/api-management-capacity.md

@@ -62,7 +62,7 @@ sections:
           * The [API Management REST API](/rest/api/apimanagement/)
           * Azure client SDKs for [.NET](/dotnet/api/overview/azure/api-management), [JavaScript](/javascript/api/overview/azure/api-management), [Java](/java/api/overview/azure/api-management), and other languages
           * [Azure PowerShell](/powershell/module/az.apimanagement/) and [Azure CLI](/cli/azure/apim)
-          * [Azure Resource Manager](/azure/templates/microsoft.apimanagement/allversions) and Bicep templates. See example [quickstart templates](https://azure.microsoft.com/resources/templates/?Page=2&term=api+management).
+          * [Azure Resource Manager](/azure/templates/microsoft.apimanagement/allversions) and Bicep files. See example [quickstart templates](https://azure.microsoft.com/resources/templates/?Page=2&term=api+management).
           * [Terraform](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/api_management)
 
       - question: |

articles/api-management/api-management-faq.yml

@@ -6,7 +6,7 @@ author: dlepow
 
 ms.service: azure-api-management
 ms.topic: concept-article
-ms.date: 10/15/2024
+ms.date: 05/12/2025
 ms.author: danlep
 ---
 
@@ -48,6 +48,9 @@ Each API Management [pricing tier](api-management-key-concepts.md#api-management
 | Azure Monitor and Log Analytics request logs                                                              | No          | Yes       | Yes   | Yes      | Yes      | Yes      |Yes     | Yes |
 | Application Insights request logs                                                               | Yes          | Yes       | Yes   | Yes      | Yes      | Yes      |Yes     | Yes |
 | Static IP                                                                                    | No          | Yes       | Yes   | No          |Yes      | No          | Yes     | No |
+| Export API to Power Platform                                                         | Yes          | Yes       | Yes    | Yes       | Yes       | Yes       | Yes     | Yes |
+| Export API to Postman                                                         | Yes          | Yes       | Yes    | Yes       | Yes       | Yes       | Yes     | Yes |
+| Export API to MCP server (preview)                                                        | No          | No       | Yes    | No       | Yes       | No       | Yes     | No |
 
 <sup>1</sup> Enables the use of Microsoft Entra ID (and Azure AD B2C) as an identity provider for user sign in on the developer portal.<br/>
 <sup>2</sup> Including related functionality such as users, groups, issues, applications, and email templates and notifications.<br/>