acrolinx

cephalin · cephalin · commit 57658777b873 · 2025-07-17T12:03:31.000-05:00
diff --git a/articles/app-service/tutorial-ai-agent-web-app-semantic-kernel-foundry-dotnet.md b/articles/app-service/tutorial-ai-agent-web-app-semantic-kernel-foundry-dotnet.md
@@ -58,9 +58,9 @@ The easiest way to get started is by using GitHub Codespaces, which provides a c
 
 1. Navigate to the GitHub repository at [https://github.com/Azure-Samples/app-service-agentic-semantic-kernel-ai-foundry-agent](https://github.com/Azure-Samples/app-service-agentic-semantic-kernel-ai-foundry-agent).
 
-2. Select the **Code** button, select the **Codespaces** tab, and click **Create codespace on main**.
+2. Select the **Code** button, select the **Codespaces** tab, and select **Create codespace on main**.
 
-3. Wait a few moments for your Codespace to initialize. When ready, you'll see a fully configured VS Code environment in your browser.
+3. Wait a few moments for your Codespace to initialize. When ready, you'll see a fully configured development environment in your browser.
 
 4. Run the application locally:
 
@@ -104,6 +104,8 @@ The OpenAPI code is defined in *Program.cs*. For example, the "get tasks" API de
 
 ## Deploy the sample application
 
+The sample respository contains an Azure Developer CLI (AZD) template, which creates an App Service app with managed identity and deploys your sample application.
+
 1. In the terminal, log into Azure using Azure Developer CLI:
 
    ```bash
@@ -243,7 +245,7 @@ When you're done with the application, you can delete the App Service resources
 azd down --purge
 ```
 
-Sind the AZD template doesn't include the Azure AI Foundry resources, you need to delete them manually if you want.
+Since the AZD template doesn't include the Azure AI Foundry resources, you need to delete them manually if you want.
 
 ## More resources
 
diff --git a/articles/app-service/tutorial-ai-agent-web-app-semantic-kernel-java.md b/articles/app-service/tutorial-ai-agent-web-app-semantic-kernel-java.md
@@ -43,9 +43,9 @@ The easiest way to get started is by using GitHub Codespaces, which provides a c
 
 1. Navigate to the GitHub repository at [https://github.com/Azure-Samples/app-service-agentic-semantic-kernel-java](https://github.com/Azure-Samples/app-service-agentic-semantic-kernel-java).
 
-2. Select the **Code** button, select the **Codespaces** tab, and click **Create codespace on main**.
+2. Select the **Code** button, select the **Codespaces** tab, and select **Create codespace on main**.
 
-3. Wait a few moments for your Codespace to initialize. When ready, you'll see a fully configured VS Code environment in your browser.
+3. Wait a few moments for your Codespace to initialize. When ready, you'll see a fully configured development environment in your browser.
 
 4. Run the application locally:
 
@@ -59,7 +59,7 @@ The easiest way to get started is by using GitHub Codespaces, which provides a c
 
 The Semantic Kernel agent is initialized in [src/main/java/com/example/crudtaskswithagent/controller/AgentController.java](https://github.com/Azure-Samples/app-service-agentic-semantic-kernel-java/blob/main/src/main/java/com/example/crudtaskswithagent/controller/AgentController.java), when the user enters the first prompt in a new browser session. 
 
-You can find the initialization code in the `SemanticKernelAgentService` contructor (in [src/main/java/com/example/crudtaskswithagent/service/SemanticKernelAgentService.java](https://github.com/Azure-Samples/app-service-agentic-semantic-kernel-java/blob/main/src/main/java/com/example/crudtaskswithagent/service/SemanticKernelAgentService.java)). The initialization code does the following: 
+You can find the initialization code in the `SemanticKernelAgentService` constructor (in [src/main/java/com/example/crudtaskswithagent/service/SemanticKernelAgentService.java](https://github.com/Azure-Samples/app-service-agentic-semantic-kernel-java/blob/main/src/main/java/com/example/crudtaskswithagent/service/SemanticKernelAgentService.java)). The initialization code does the following: 
 
 - Creates a kernel with chat completion.
 - Adds a kernel plugin that encapsulates the functionality of the CRUD application (in *src/main/java/com/example/crudtaskswithagent/plugin/TaskCrudPlugin.java*). The interesting parts of the plugin are the `DefineKernelFunction` annotations on the method declarations and the `description` and `returnType` parameters that help the kernel call the plugin intelligently.
@@ -68,12 +68,14 @@ You can find the initialization code in the `SemanticKernelAgentService` contruc
 
 :::code language="csharp" source="~/app-service-agentic-semantic-kernel-java/src/main/java/com/example/crudtaskswithagent/service/SemanticKernelAgentService.java" range="38-90" highlight="11-48,58" :::
 
-Each time the prompt is received, the server code uses `ChatCompletionAgent.invokeAsync()` to invoke the agent with the user prompt and the agent thread. the agent thread keeps track of the chat history.
+Each time the prompt is received, the server code uses `ChatCompletionAgent.invokeAsync()` to invoke the agent with the user prompt and the agent thread. The agent thread keeps track of the chat history.
 
 :::code language="csharp" source="~/app-service-agentic-semantic-kernel-java/src/main/java/com/example/crudtaskswithagent/service/SemanticKernelAgentService.java" range="109-158" highlight="8" :::
 
 ## Deploy the sample application
 
+The sample respository contains an Azure Developer CLI (AZD) template, which creates an App Service app with managed identity and deploys your sample application.
+
 1. In the terminal, log into Azure using Azure Developer CLI:
 
    ```bash
@@ -113,7 +115,7 @@ Each time the prompt is received, the server code uses `ChatCompletionAgent.invo
 
 ## Create and configure the Azure AI Foundry resource
 
-1. In the [Azure AI Foundry portal](https://ai.azure.com), deploy a model of your choice (see [Quickstart: Get started with Azure AI Foundry](/azure/ai-foundry/quickstarts/get-started-code?tabs=azure-ai-foundry&pivots=fdp-project)). A project and a model deployment is created for you in the process.
+1. In the [Azure AI Foundry portal](https://ai.azure.com), deploy a model of your choice (see [Quickstart: Get started with Azure AI Foundry](/azure/ai-foundry/quickstarts/get-started-code?tabs=azure-ai-foundry&pivots=fdp-project)). A project and a model deployment are created for you in the process.
 
 1. From the left menu, select **Overview**.
 
@@ -187,7 +189,7 @@ When you're done with the application, you can delete the App Service resources
 azd down --purge
 ```
 
-Sind the AZD template doesn't include the Azure AI Foundry resources, you need to delete them manually if you want.
+Since the AZD template doesn't include the Azure AI Foundry resources, you need to delete them manually if you want.
 
 ## More resources
 
diff --git a/articles/app-service/tutorial-ai-integrate-azure-ai-agent-dotnet.md b/articles/app-service/tutorial-ai-integrate-azure-ai-agent-dotnet.md
@@ -208,7 +208,7 @@ When exposing APIs via OpenAPI in Azure App Service, follow these security best
 - **Monitor and log activity:** Enable logging and monitor access to detect suspicious activity.
 - **Use managed identities:** When calling other Azure services, use managed identities instead of hardcoded credentials.
 
-For more guidance, see [Secure your App Service app](overview-security.md) and [Best practices for REST API security](/azure/architecture/best-practices/api-design#security).
+For more information, see [Secure your App Service app](overview-security.md) and [Best practices for REST API security](/azure/architecture/best-practices/api-design#security).
 
 ## Next step
 
diff --git a/articles/app-service/tutorial-ai-integrate-azure-ai-agent-java.md b/articles/app-service/tutorial-ai-integrate-azure-ai-agent-java.md
@@ -104,7 +104,7 @@ At a minimum, open the [sample application](https://github.com/Azure-Samples/msd
     public String deleteTodoItem(@PathVariable("id") String id) {
     ```
     
-    This minimimal configuration will give you the following settings, as documented in [springdoc-openapi](https://springdoc.org/):
+    This minimal configuration gives you the following settings, as documented in [springdoc-openapi](https://springdoc.org/):
 
     - Swagger UI at `/swagger-ui.html`.
     - OpenAPI specification at `/v3/api-docs`.
@@ -160,7 +160,7 @@ When exposing APIs via OpenAPI in Azure App Service, follow these security best
 - **Monitor and log activity:** Enable logging and monitor access to detect suspicious activity.
 - **Use managed identities:** When calling other Azure services, use managed identities instead of hardcoded credentials.
 
-For more guidance, see [Secure your App Service app](overview-security.md) and [Best practices for REST API security](/azure/architecture/best-practices/api-design#security).
+For more information, see [Secure your App Service app](overview-security.md) and [Best practices for REST API security](/azure/architecture/best-practices/api-design#security).
 
 ## Next step
 
diff --git a/articles/app-service/tutorial-ai-model-context-protocol-server-dotnet.md b/articles/app-service/tutorial-ai-model-context-protocol-server-dotnet.md
@@ -223,7 +223,7 @@ When your MCP server is called by an agent powered by large language models (LLM
 - **Authentication and Authorization**: Protect your MCP endpoints in App Service behind [Azure API Management with Microsoft Entra ID](/azure/api-management/api-management-howto-protect-backend-with-aad) and ensure only authorized users or agents can access the tools.
 - **Input Validation and Sanitization**: The example code in this tutorial omits input validation and sanitization for simplicity and clarity. In production scenarios, always implement proper validation and sanitization to protect your application. For ASP.NET Core, see [Model validation in ASP.NET Core](/aspnet/core/mvc/models/validation).
 - **HTTPS:** The sample relies on Azure App Service, which enforces HTTPS by default and provides free TLS/SSL certificates to encrypt data in transit.
-- **Least Privilege Principle**: Expose only the necessary tools and data required for your use case. Avoid exposing sensitive operations unless absolutely necessary.
+- **Least Privilege Principle**: Expose only the necessary tools and data required for your use case. Avoid exposing sensitive operations unless necessary.
 - **Rate Limiting and Throttling**: Use [API Management](/azure/api-management/api-management-sample-flexible-throttling) or custom middleware to prevent abuse and denial-of-service attacks.
 - **Logging and Monitoring**: Log access and usage of MCP endpoints for auditing and anomaly detection. Monitor for suspicious activity.
 - **CORS Configuration**: Restrict cross-origin requests to trusted domains if your MCP server is accessed from browsers. For more information, see [Enable CORS](app-service-web-tutorial-rest-api.md#enable-cors).
diff --git a/articles/app-service/tutorial-ai-model-context-protocol-server-java.md b/articles/app-service/tutorial-ai-model-context-protocol-server-java.md
@@ -36,7 +36,7 @@ At a minimum, open the [sample application](https://github.com/Azure-Samples/msd
 
 ## Add MCP server to your web app
 
-1. In the codespace, open *pom.xml* add the `spring-ai-starter-mcp-server-webmvc` package to your project:
+1. In the codespace, open *pom.xml*  and add the `spring-ai-starter-mcp-server-webmvc` package to your project:
 
     ```xml
     <dependency>
@@ -46,7 +46,7 @@ At a minimum, open the [sample application](https://github.com/Azure-Samples/msd
     </dependency>
     ```
 
-1. Open *src/main/java/com/microsoft/azure/appservice/examples/springbootmongodb/TodoApplication.java*. For simplicity of the scenario, you will add all of your MCP server code here.
+1. Open *src/main/java/com/microsoft/azure/appservice/examples/springbootmongodb/TodoApplication.java*. For simplicity of the scenario, you'll add all of your MCP server code here.
 
 1. At the end of *TodoApplication.java*, add the following class. 
 
@@ -115,7 +115,7 @@ At a minimum, open the [sample application](https://github.com/Azure-Samples/msd
     }
     ```
 
-    This method provides the tools in `TodoListToolService` as callbacks for Spring AI. By default, the MCP Server auto-configuration in the `spring-ai-starter-mcp-server-webmvc` package automatically wires up these tool callbacks. Also, by default, the MCP Server endpoint is `<base-url>/sse`.
+    This method provides the tools in `TodoListToolService` as callbacks for Spring AI. By default, the MCP Server autoconfiguration in the `spring-ai-starter-mcp-server-webmvc` package automatically wires up these tool callbacks. Also, by default, the MCP Server endpoint is `<base-url>/sse`.
 
 1. At the top of *TodoApplication.java*, add the following imports.
 
@@ -196,7 +196,7 @@ When your MCP server is called by an agent powered by large language models (LLM
 - **Authentication and Authorization**: Protect your MCP endpoints in App Service behind [Azure API Management with Microsoft Entra ID](/azure/api-management/api-management-howto-protect-backend-with-aad) and ensure only authorized users or agents can access the tools.
 - **Input Validation and Sanitization**: The example code in this tutorial omits input validation and sanitization for simplicity and clarity. In production scenarios, always implement proper validation and sanitization to protect your application. For Spring, see [Spring: Validating Form Input](https://spring.io/guides/gs/validating-form-input).
 - **HTTPS:** The sample relies on Azure App Service, which enforces HTTPS by default and provides free TLS/SSL certificates to encrypt data in transit.
-- **Least Privilege Principle**: Expose only the necessary tools and data required for your use case. Avoid exposing sensitive operations unless absolutely necessary.
+- **Least Privilege Principle**: Expose only the necessary tools and data required for your use case. Avoid exposing sensitive operations unless necessary.
 - **Rate Limiting and Throttling**: Use [API Management](/azure/api-management/api-management-sample-flexible-throttling) or custom middleware to prevent abuse and denial-of-service attacks.
 - **Logging and Monitoring**: Log access and usage of MCP endpoints for auditing and anomaly detection. Monitor for suspicious activity.
 - **CORS Configuration**: Restrict cross-origin requests to trusted domains if your MCP server is accessed from browsers. For more information, see [Enable CORS](app-service-web-tutorial-rest-api.md#enable-cors).
