Refine documentation for Docker MCP Toolkit: improve clarity and consistency in migration steps and setup instructions - first pass

Author: madeline-underwood

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

@@ -28,8 +28,8 @@ When architecture-specific optimizations are present, migration may involve:
 - Identifying x86-specific intrinsics or assembly
 - Updating compiler flags and build configurations
 - Mapping AVX2 operations to appropriate NEON equivalents
-- Rewriting vectorized code and adjusting loop structures.
-- Updating Dockerfiles, base images, and compiler flags.
+- Rewriting vectorized code and adjusting loop structures
+- Updating Dockerfiles, base images, and compiler flags
 - Validating correctness and performance on Arm systems
 
 These steps are well understood, but they can require careful review across code, build scripts, and container configurations.

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

@@ -12,7 +12,7 @@ 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 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.

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

@@ -77,15 +77,15 @@ To run this code on Arm, several adjustments are required:
 
 2. **Intrinsic mapping**: Each AVX2 intrinsic must be mapped to an Arm equivalent.
    For example:
-   - `_mm256_setzero_pd()` creates a 256-bit zero vector of four doubles(Arm NEON is 128-bit).
+   - `_mm256_setzero_pd()` creates a 256-bit zero vector of four doubles. Arm NEON uses 128-bit registers.
    - `_mm256_loadu_pd()` loads 4 doubles at once (NEON loads 2 with `vld1q_f64`).
    - `_mm256_add_pd()` and `_mm256_mul_pd()` are 256-bit operations (NEON uses 128-bit equivalents).
    - `_mm256_extractf128_pd()` extracts the high 128 bits (not needed on NEON).
 
-4. **Vector width differences**: AVX2 operates on 256-bit registers (four double-precision values). NEON operates on 128-bit registers (two double-precision values). This affects:
-  - Loop stride
-  - Accumulation logic
-  - Horizontal reduction patterns
+3. **Vector width differences**: AVX2 operates on 256-bit registers (four double-precision values). NEON operates on 128-bit registers (two double-precision values). This affects:
+   - Loop stride
+   - Accumulation logic
+   - Horizontal reduction patterns
 
 4. **Horizontal reduction logic**: The AVX2 pattern:
 
@@ -99,5 +99,4 @@ 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 %}}
 
-In the next section, you will use GitHub Copilot together with the Docker MCP Toolkit to analyze these elements automatically and generate Arm-compatible updates.
 In the next section, you will use GitHub Copilot with the Docker MCP Toolkit to automate the entire migration.

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

@@ -15,7 +15,7 @@ cd docker-blog-arm-migration
 code .
 ```
 
-Make sure the MCP_DOCKER server is running in VS Code ( Use **Extensions** > **MCP_DOCKER** > **Start Server** if needed).
+Make sure the MCP_DOCKER server is running in VS Code (use **Extensions** > **MCP_DOCKER** > **Start Server** if needed).
 
 This allows GitHub Copilot to invoke the configured MCP servers through the MCP Gateway.
 

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

@@ -66,10 +66,10 @@ This produces a multi-architecture manifest that allows Docker to automatically
 
 AI-assisted workflows streamline repetitive discovery and mapping tasks, particularly when architecture-specific intrinsics are involved.
 
-| Approach | Time |
-|----------|------|
-| Manual migration (install tools, research intrinsics, rewrite code, debug, document) | Several hours, depending on complexity |
-| Docker MCP Toolkit + GitHub Copilot (prompt, review, merge) | Significantly reduced manual effort |
+| Approach | Effort |
+|----------|--------|
+| Manual migration (install tools, research intrinsics, rewrite code, debug, document) | Several hours to days, depending on complexity |
+| Docker MCP Toolkit + GitHub Copilot (prompt, review, merge) | Reduced to minutes for initial migration, plus review time |
 
 Actual time savings depend on codebase size and complexity, but structured tool invocation reduces the need for manual documentation lookup and repetitive edits.