Refine documentation for Docker MCP Toolkit: update titles for clarity, enhance section descriptions, and improve formatting across multiple files.

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/1-overview.md

@@ -1,5 +1,5 @@
 ---
-title: Simplify Arm migration with the Docker MCP ToolKit and Arm MCP Server
+title: Simplify Arm migration with the Docker MCP Toolkit and Arm MCP Server
 weight: 2
 
 ### FIXED, DO NOT MODIFY
@@ -38,18 +38,26 @@ These steps are well understood, but they can require careful review across code
 
 The Docker MCP Toolkit is a management interface in Docker Desktop that lets you discover, configure, and run containerized MCP (Model Context Protocol) servers. It connects these servers to AI coding assistants through a unified gateway.
 
-Each MCP server provides specialized capabilities that work together to support the migration workflow. For Arm migration, three MCP servers work together:
+## MCP servers for Arm migration
+
+Three MCP servers work together to support the migration workflow:
+
+**Arm MCP Server**
+
+Provides migration-focused tools:
+- `migrate_ease_scan` detects x86-specific code and compiler flags
+- `check_image` and `skopeo` verify container architecture support
+- `knowledge_base_search` accesses learning resources, Arm intrinsics, and software version compatibility
+- `mca` performs microarchitectural performance analysis
+
+**GitHub MCP Server**
+
+Enables Git repository operations including creating pull requests, managing branches, and committing changes.
+
+**Sequential Thinking MCP Server**
+
+Helps the AI assistant break down complex migration decisions into logical steps.
 
-- **Arm MCP Server**:
-  Provides migration-focused tools, including:
-  - `migrate_ease_scan` for detecting x86-specific code and compiler flags
-  - `check_image` and `skopeo` for verifying container architecture support
-  - `knowledge_base_search` for a knowledge base of learning resources, Arm intrinsics, and software version compatibility
-  - `mca` for microarchitectural performance analysis
-  
-- **GitHub MCP Server**: Enables Git repository operations including creating pull requests, managing branches, and committing changes.
-- 
-- **Sequential Thinking MCP Server**: Helps the AI assistant break down complex migration decisions into logical steps.
 
 ## How AI-assisted migration works
 
@@ -62,21 +70,15 @@ When connected to the Docker MCP Toolkit, an AI coding assistant like GitHub Cop
 - Update Dockerfiles and build configurations for Arm compatibility
 - Create a pull request with the proposed changes using the GitHub MCP Server
 
-## What you will build in this Learning Path
+## The demo application
 
-In this Learning Path, you will migrate a real-world legacy application - a matrix multiplication benchmark written with AVX2 intrinsics for x86 - to Arm64 using GitHub Copilot and Docker MCP Toolkit.
+This Learning Path uses a real-world example: a matrix multiplication benchmark written in C++ with AVX2 intrinsics for x86. You'll migrate it to Arm64 using the AI-assisted workflow described above.
 
 The demo repository is available at [github.com/JoeStech/docker-blog-arm-migration](https://github.com/JoeStech/docker-blog-arm-migration).
 
-You will:
-
-- Set up Docker MCP Toolkit with the Arm, GitHub, and Sequential Thinking MCP servers
-- Connect VS Code with GitHub Copilot to the MCP Gateway
-- Analyze the x86-specific portions of the codebase
-- Use AI-assisted MCP workflows to update intrinsics and container configuration
-- Review and validate the pull request generated by the AI agent
+By the end of this Learning Path, you'll have a working Arm64 container with NEON-optimized code and an automated pull request containing all migration changes.
 
-## What you've accomplished and what's next
+## What you've learned and what's next
 
 You now understand:
 - Why Arm migration requires more than rebuilding containers when architecture-specific code is present

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/2-setup.md

@@ -10,23 +10,23 @@ layout: learningpathall
 
 Make sure the following tools are installed:
 
-- Docker Desktop 4.59 or later.
-- VS Code with the GitHub Copilot extension.
-- A GitHub account with a Personal Access Token (PAT) that allows repository access.
-- A machine with at least 8 GB RAM (16 GB recommended).
+- Docker Desktop 4.59 or later
+- VS Code with the GitHub Copilot extension
+- A GitHub account with a Personal Access Token (PAT) that allows repository access
+- A machine with at least 8 GB RAM (16 GB recommended)
 
-You will use Docker Desktop to host MCP servers locally, and VS Code with GitHub Copilot to invoke those servers through the MCP Gateway.
+You'll use Docker Desktop to host MCP servers locally, and VS Code with GitHub Copilot to invoke those servers through the MCP Gateway.
 
 ## Enable the Docker MCP Toolkit
 
 The MCP Toolkit allows Docker Desktop to run and manage MCP (Model Context Protocol) servers, which expose structured tools that AI assistants can call.
 
-1. Open Docker Desktop.
-2. Go to **Settings** > **Beta features**.
-3. Toggle **Enable Docker MCP Toolkit** on.
-4. Click **Apply**.
+- Open Docker Desktop
+- Go to **Settings** > **Beta features**
+- Toggle **Enable Docker MCP Toolkit** on
+- Select **Apply**
 
-After a few seconds, the **MCP Toolkit** tab appears in the left sidebar.
+The **MCP Toolkit** tab appears in the left sidebar.
 
 ## Add the required MCP servers
 
@@ -55,9 +55,9 @@ Search for **GitHub Official** in the catalog and add the [GitHub MCP Server](ht
 
 Configure authentication:
 
-1. Select the GitHub Official server.
-2. Choose **Personal Access Token** as the authentication method.
-3. Enter your GitHub token from **GitHub Settings** > **Developer Settings** > **Personal access tokens**.
+- Select the GitHub Official server
+- Choose **Personal Access Token** as the authentication method
+- Enter your GitHub token from **GitHub Settings** > **Developer Settings** > **Personal access tokens**
 
 This server lets GitHub Copilot create pull requests, manage issues, and commit changes directly to your repositories.
 
@@ -69,10 +69,10 @@ No configuration is needed. This server helps GitHub Copilot break down complex
 
 ## Connect VS Code to the MCP Gateway
 
-1. In Docker Desktop, go to **MCP Toolkit** > **Clients** tab.
-2. Scroll to **Visual Studio Code** and click **Connect**.
-3. Open VS Code and click the **Extensions** icon in the left toolbar.
-4. Find **MCP_DOCKER**, click the gear icon, and click **Start Server**.
+- In Docker Desktop, go to **MCP Toolkit** > **Clients** tab
+- Scroll to **Visual Studio Code** and select **Connect**
+- Open VS Code and select the **Extensions** icon in the left toolbar
+- Find **MCP_DOCKER**, select the gear icon, and select **Start Server**
 
 ## Verify the connection
 
@@ -82,20 +82,20 @@ Open GitHub Copilot Chat in VS Code and ask:
 What Arm migration tools do you have access to?
 ```
 
-If the setup is correct, Copilot will list tools from:
+If the setup is correct, Copilot lists tools from:
 
 - Arm MCP Server
 - GitHub MCP Server
 - Sequential Thinking MCP Server
 
 This confirms that tool invocation through the MCP Gateway is working.
 
-## What you've accomplished and what's next
+## What you've learned and what's next
 
 You have:
 - Enabled the Docker MCP Toolkit in Docker Desktop
 - Configured three MCP servers: Arm MCP Server, GitHub MCP Server, and Sequential Thinking MCP Server
 - Connected VS Code with GitHub Copilot to the MCP Gateway
 - Verified that Copilot can access migration tools
 
-Next, you'll examine the demo application to identify x86-specific code that needs adaptation for Arm64.
+Next, you'll examine the demo application to identify x86-specific code that needs adaptation for Arm64.

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/3-understand-the-demo.md

@@ -1,5 +1,5 @@
 ---
-title: Understand the legacy x86 demo application
+title: Examine x86 AVX2 intrinsics in the demo application
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -99,7 +99,7 @@ is specific to x86 register structure. On Arm, reduction is implemented using NE
 On newer Arm platforms supporting SVE or SVE2 (for example Neoverse V1/V2 based platforms), wider vector lengths may be available. SVE uses a vector-length-agnostic (VLA) model, which differs from fixed-width AVX2 and NEON programming. The Arm MCP Server knowledge base can help determine the appropriate approach for your target platform.
 {{% /notice %}}
 
-## What you've accomplished and what's next
+## What you've learned and what's next
 
 You have:
 - Examined a legacy x86 application with AVX2 intrinsics

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/4-run-migration.md

@@ -1,5 +1,5 @@
 ---
-title: Run the AI-driven Arm migration
+title: Automate x86 to Arm migration with GitHub Copilot
 weight: 5
 
 ### FIXED, DO NOT MODIFY
@@ -146,7 +146,7 @@ After migration, you should see:
 - Adjusted loop strides from 4 (AVX2) to 2 (NEON)
 - Rewrote horizontal reduction using NEON pair-wise addition
 
-## What you've accomplished and what's next
+## What you've learned and what's next
 
 You have:
 - Provided migration instructions to GitHub Copilot

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/5-validate-and-next-steps.md

@@ -1,5 +1,5 @@
 ---
-title: Validate the migration and explore further
+title: Validate the Arm64 migration and test containers
 weight: 6
 
 ### FIXED, DO NOT MODIFY
@@ -117,7 +117,7 @@ The Docker MCP Toolkit and Arm MCP Server support more than the example migratio
 - **Knowledge base**: The `knowledge_base_search` tool covers all content from [learn.arm.com](https://learn.arm.com) Learning Paths, intrinsics documentation, and software compatibility information.
 - **Dynamic MCP**: AI agents can discover and add new MCP servers from the Docker MCP Catalog during a conversation without manual configuration.
 
-## What you've accomplished
+## What you've learned
 
 In this Learning Path, you: