Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into waf-appgw

Author: halkazwini

.openpublishing.redirection.json

@@ -6875,9 +6875,14 @@
       "redirect_document_id": false
     },
     {
-      "source_path": "articles/storage/files/storage-files-enable-soft-delete.md",
+         "source_path": "articles/storage/files/storage-files-enable-soft-delete.md",
       "redirect_url": "/azure/storage/files/storage-files-prevent-file-share-deletion",
       "redirect_document_id": false
+     },
+     {
+      "source_path": "articles/defender-for-iot/organizations/release-notes-archive.md",
+      "redirect_url": "/azure/defender-for-iot/organizations/release-notes-ot-monitoring-sensor-archive",
+       "redirect_document_id": false
     }
   ]
 }

articles/active-directory-b2c/media/analytics-with-application-insights/app-insights.png

@@ -61,7 +61,7 @@ To create an instance of Application Insights in your subscription, follow these
 1. Complete the form, select **Review + create**, and then select **Create**.
 1. Once the deployment completes, select **Go to resource**.
 1. Under **Configure** in Application Insights menu, select **Properties**.
-1. Record the **CONNECTION STRING** for use in a later step.
+1. Record the **Connection String** for use in a later step.
 
 ## Configure the custom policy
 
@@ -77,7 +77,7 @@ To create an instance of Application Insights in your subscription, follow these
 1. Add the following node as a child of the `<UserJourneyBehaviors>` element. Make sure to replace `{Your Application Insights Key}` with the Application Insights **Connection String** that you recorded earlier.
 
     ```xml
-    <JourneyInsights TelemetryEngine="ApplicationInsights" ConnectionString="{Your Application Insights Connection String}" DeveloperMode="true" ClientEnabled="false" ServerEnabled="true" TelemetryVersion="1.0.0" />
+    <JourneyInsights TelemetryEngine="ApplicationInsights" ConnectionString="{Your Application Insights ConnectionString}" DeveloperMode="true" ClientEnabled="false" ServerEnabled="true" TelemetryVersion="1.0.0" />
     ```
 
     * `DeveloperMode="true"` tells ApplicationInsights to expedite the telemetry through the processing pipeline. Good for development, but constrained at high volumes. In production, set the `DeveloperMode` to `false`.
@@ -102,7 +102,7 @@ To create an instance of Application Insights in your subscription, follow these
          <Endpoint Id="Token" UserJourneyReferenceId="RedeemRefreshToken" />
       </Endpoints>
       <UserJourneyBehaviors>
-        <JourneyInsights TelemetryEngine="ApplicationInsights" ConnectionString="{Your Application Insights Connection String}" DeveloperMode="true" ClientEnabled="false" ServerEnabled="true" TelemetryVersion="1.0.0" />
+        <JourneyInsights TelemetryEngine="ApplicationInsights" ConnectionString="{Your Application Insights ConnectionString}" DeveloperMode="true" ClientEnabled="false" ServerEnabled="true" TelemetryVersion="1.0.0" />
       </UserJourneyBehaviors>
       ...
     </TrustFrameworkPolicy>
@@ -144,7 +144,7 @@ We recommend you to install the [Azure AD B2C extension](https://marketplace.vis
 After you set up the Application Insights, and configure the custom policy, you need to get your Application Insights **API ID**, and create **API Key**. Both the API ID and API key are used by Azure AD B2C extension to read the Application Insights events (telemetries). Your API keys should be managed like passwords. Keep it secret.
 
 > [!NOTE]
-> Application Insights connection string that your create earlier is used by Azure AD B2C to send telemetries to Application Insights. You use the connection string only in your Azure AD B2C policy, not in the VS Code extension.
+> Application Insights Connection String that your create earlier is used by Azure AD B2C to send telemetries to Application Insights. You use the Connection String only in your Azure AD B2C policy, not in the VS Code extension.
 
 To get Application Insights ID and key:
 
@@ -195,7 +195,7 @@ To improve your production environment performance and better user experience, i
 
    ```xml
    <UserJourneyBehaviors>
-     <JourneyInsights TelemetryEngine="ApplicationInsights" ConnectionString="{Your Application Insights Connection String}" DeveloperMode="false" ClientEnabled="false" ServerEnabled="true" TelemetryVersion="1.0.0" />
+     <JourneyInsights TelemetryEngine="ApplicationInsights" ConnectionString="{Your Application Insights ConnectionString}" DeveloperMode="false" ClientEnabled="false" ServerEnabled="true" TelemetryVersion="1.0.0" />
    </UserJourneyBehaviors>
    ```
 

articles/active-directory-b2c/troubleshoot-with-application-insights.md

@@ -50,6 +50,10 @@
         href: design-api-github-copilot-azure.md
       - name: Register APIs - GitHub Actions
         href: register-apis-github-actions.md
+      - name: Register and discover MCP servers
+        href: register-discover-mcp-server.md
+      - name: Export API from API Center to Copilot Studio
+        href: export-to-copilot-studio.yml
 - name: API governance
   items:
       - name: Use metadata for governance

articles/api-center/TOC.yml

@@ -0,0 +1,79 @@
+### YamlMime:HowTo
+
+metadata:
+  title: Export API from API Center as Copilot Studio connector
+  description: Learn how to export an API definition from your Azure API center as a custom connector in Microsoft Copilot Studio.
+  author: dlepow
+  ms.author: danlep
+  ms.service: azure-api-management
+  ms.topic: how-to 
+  ms.date: 04/28/2025
+  ms.custom: template-how-to
+
+  #customer intent: As an API program manager, I want to export an API from my API center inventory as a connector in Microsoft Copilot Studio for use in agents.
+title: |
+  Export API definition from API Center to Copilot Studio as a connector
+
+introduction: |
+  You can export an API definition from your Azure API center as a [connector in Microsoft Copilot Studio](/connectors/overview) (preview). Copilot Studio is a graphical, low-code tool for building agents and agent flows and is part of the Microsoft Power Platform family.
+  
+  In Microsoft Copilot Studio, citizen developers can use the exported connector as a custom connector to [extend the capabilities of AI agents](/microsoft-copilot-studio/copilot-connectors-in-copilot-studio).
+
+  In this article, you create a custom connector from an OpenAPI definition associated with an API in an API center.  
+
+prerequisites:
+  summary: |
+
+  dependencies:
+    - An API center instance. You can [create one](set-up-api-center.md) in the Azure portal.
+    - An API registered in the API center inventory. The API must have a version associated with a [deployment](configure-environments-deployments.md), and it must have an API definition in OpenAPI 2.0 format.
+    - A Power Platform [environment](/power-apps/powerapps-overview#power-apps-for-admins).
+    - A Microsoft Copilot Studio [license](/microsoft-copilot-studio/requirements-licensing-subscriptions).
+
+procedureSection:
+  - title: | 
+        Export API definition from API Center
+    summary: |
+      Follow these steps to export an API definition from your Azure API center as a custom connector in Microsoft Copilot Studio. 
+       
+    steps:
+      - |
+        Navigate to your API center in the Azure portal.
+      - |
+        In the left menu, select **Copilot Studio (preview)**.
+      - |
+        Select **Create a connector**. The **Create a connector** window opens.
+      - |
+        Select a **Power Platform environment** to export the API to. 
+      - |
+        Enter a **Connector display name**.
+      - |
+        Select the **API**, **API version**, and **Deployment** for the connector. The **Host** URL is populated with the deployment's runtime URL.
+      - |
+        Select the **API definition** to use for the connector.
+      - |
+        Optionally, if you configured [API access settings](https://aka.ms/apicenter/docs/access) for the API version in your API center, select an available **Security requirement** type to use for the connector.
+      - |
+        Select **Create**. 
+
+        :::image type="content" source="media/export-to-copilot-studio/copilot-studio-menu-item.png" alt-text="Screenshot of creating a Copilot Studio connector in the portal.":::
+
+        
+        The connector is now available for use in Microsoft Copilot Studio.
+    
+        After creating the connector:
+        
+        - You can [test the connector](/connectors/custom-connectors/define-openapi-definition#test-the-connector) in Power Automate or Power Apps. 
+        
+        - After testing, use the connector in [Microsoft Copilot Studio](/microsoft-copilot-studio/) to create applications and workflows.
+
+relatedContent:
+  
+    - text: Create a custom connector from an OpenAPI definition
+      url: /connectors/custom-connectors/define-openapi-definition
+
+    - text: Learn more about building AI-driven agents with Microsoft Copilot Studio
+      url: /microsoft-copilot-studio/nlu-gpt-overview
+
+    - text: Get started with Microsoft Copilot Studio 
+      url: /training/modules/power-virtual-agents-bots

articles/api-center/export-to-copilot-studio.yml

@@ -0,0 +1,102 @@
+---
+title: Inventory and Discover MCP Servers in Your API Center
+description: Learn about how Azure API Center can be a centralized registry for MCP servers in your organization. Developers and other stakeholders can use the API Center portal to discover MCP servers.
+author: dlepow
+ms.service: azure-api-center
+ms.custom: 
+ms.topic: concept-article
+ms.date: 04/28/2025
+ms.author: danlep 
+# Customer intent: As an API program manager, I want to register and discover  MCP servers as APIs in my API Center inventory.
+---
+
+# Register and discover remote MCP servers in your API inventory
+
+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.
+
+## MCP servers in your API inventory
+
+The following sections describe how to inventory and discover a remote MCP server in your API Center. 
+
+### MCP API type
+
+To register an MCP server in your API center inventory, specify the API type as **MCP**. To register an API using the Azure portal, see [Tutorial: Register APIs in your API inventory](register-apis.md).
+
+As described in the following sections, when you register an MCP server, you can specify an environment, deployment, and definition.
+
+
+### Environment and deployment for MCP server
+
+In API Center, specify an *environment* and a *deployment* for your MCP server. The environment is the location of the MCP server, such as an API management platform or a compute service, and the deployment is a runtime URL for the MCP service. 
+
+For information about creating an environment and a deployment, see [Tutorial: Add environments and deployments for APIs](configure-environments-deployments.md).
+
+### Definition for remote MCP server
+
+Optionally, add an API definition for a remote MCP server in OpenAPI 3.0 format. The API definition must include a URL endpoint for the MCP server. For an example of adding an OpenAPI definition, see [Tutorial: Register APIs in your API inventory](register-apis.md#add-a-definition-to-your-version).
+
+
+You can use the following lightweight OpenAPI 3.0 API definition for your MCP server, which includes a `url` endpoint for the MCP server:
+
+
+```json
+{
+  "openapi": "3.0.0",
+  "info": {
+    "title": "Demo MCP server",
+    "description": "Very basic MCP server that exposes mock tools and prompts.",
+    "version": "1.0"
+  },
+  "servers": [
+    {
+      "url": "https://my-mcp-server.contoso.com"
+    }
+  ]
+}
+```
+
+###  Discover MCP servers using API Center portal
+
+Set up the [API Center portal](set-up-api-center-portal.md) so that developers and other stakeholders in your organization can discover MCP servers in your API inventory. Users can browse and filter MCP servers in the inventory and view details such as the URL endpoint of the MCP server, if available in the MCP server's API definition. 
+
+:::image type="content" source="media/register-discover-mcp-server/mcp-server-portal-small.png" lightbox="media/register-discover-mcp-server/mcp-server-portal.png" alt-text="Screenshot of MCP server in API Center portal.":::
+
+> [!NOTE]
+> The URL endpoint for the MCP server is only visible in the API Center portal if you add an MCP deployment and an API definition for the MCP server.
+
+## Related content
+
+* [Import APIs to your API center from API Management](import-api-management-apis.md)
+* [Use the Visual Studio extension for API Center](build-register-apis-vscode-extension.md) to build and register APIs from Visual Studio Code.
+