minor tweaks to style and formatting

Author: petender

learn-pr/wwl-azure/manage-ado-mcp-server/includes/10-inspect-build-logs.md

@@ -49,13 +49,12 @@ Over repeated incidents, this approach creates a strong troubleshooting discipli
 
 To map with the earlier-described example scenario, here are a few sample prompt to illustrate how to use MCP-assisted prompting for those scenarios:
 
-```
 - "Get all errors and warnings from the Test stage in build #4527."
 - "Summarize failures in the MyProject.Tests project from build #4527."
 - "Show me test instability patterns: which tests failed in build #4527 but passed in build #4526?"
 - "Extract dependency restore and NuGet feed errors from build #4527 Test stage."
 - "List all warnings in the Test stage that might indicate environment drift in build #4527."
-```
+
 
 These prompts narrow scope to the failing stage and test project, helping teams isolate whether the issue is environment-related, a true functional regression, or test instability.
 

learn-pr/wwl-azure/manage-ado-mcp-server/includes/12-guided-exercise-manage-azure-devops-mcp-server.md

@@ -1,14 +1,12 @@
 > [!IMPORTANT]
-> To complete this exercise, you need 
-- An active GitHub account, 
-- An active GitHub Copilot subscription, 
-- Access to an Azure DevOps project 
-- A Visual Studio Code environment.  
+> To complete this exercise, you need:
+> - An active GitHub account 
+> - An active GitHub Copilot subscription
+> - Access to an Azure DevOps project 
+> - A Visual Studio Code environment 
 
 This exercise provides a comprehensive experience of using Azure DevOps MCP Server in Visual Studio Code. You use GitHub Copilot AI-assistance. Throughout the exercise, you handle several common DevOps team tasks, such as interact with Azure Boards work items, retrieve and trigger pipeline runs. Next, you learn how to use MCP to inspect ADO logs and apply Azure DevOps Security guidelines when using MCP. 
 
-By the end of this lab, you are able to:
-
 > [!IMPORTANT]
 > The focus of this exercise is on the integration of both DevOps platforms, without using actual application code.
 

learn-pr/wwl-azure/manage-ado-mcp-server/includes/14-summary.md

@@ -2,15 +2,15 @@ In this module, you learned how to use Azure DevOps MCP Server with GitHub Copil
 
 The module followed an end-to-end progression from setup through execution:
 
-1. understanding the MCP interaction model
-2. installing and validating tooling in Visual Studio Code
-3. authenticating and reviewing payloads safely
-4. gathering context before changing artifacts
-5. creating, linking, and updating work items
-6. managing pull request collaboration workflows
-7. triggering and monitoring pipelines
-8. retrieving and interpreting logs for troubleshooting
-9. applying governance guardrails for sustainable adoption
+1. Understanding the MCP interaction model
+2. Installing and validating tooling in Visual Studio Code
+3. Authenticating and reviewing payloads safely
+4. Gathering context before changing artifacts
+5. Creating, linking, and updating work items
+6. Managing pull request collaboration workflows
+7. Triggering and monitoring pipelines
+8. Retrieving and interpreting logs for troubleshooting
+9. Applying governance guardrails for sustainable adoption
 
 ## Key takeaways
 

learn-pr/wwl-azure/manage-ado-mcp-server/includes/2-understand-azure-devops-mcp-server.md

@@ -2,7 +2,7 @@ Azure DevOps MCP Server provides a practical translation layer between conversat
 
 Before diving into the details of the Azure DevOps MCP Server capabilities, let's start with describing what MCP actually is?
 
-## What is MCP
+## What is MCP?
 
 The Model Context Protocol (MCP) is an open standard developed by Anthropic for enabling AI systems to safely and transparently interact with external tools, data sources, and services. At its core, MCP defines a structured communication layer between AI models (like GitHub Copilot, Anthropic's Claude and other) and the systems they need to access; whether those are databases, APIs, file systems, or specialized platforms like Azure DevOps.
 
@@ -27,13 +27,10 @@ In a business context, MCP addresses a fundamental challenge: teams increasingly
 
 MCP enables several practical outcomes:
 
-**Accelerated Workflow Automation**: DevOps (and wider business) Teams can describe tasks using natural language and have AI safely execute them across multiple systems. A DevOps engineer might ask an AI assistant to "create a bug, assign it to the on-call engineer, and link it to the current incident," and MCP ensures each step is transparent and auditable.
-
-**Reduced Integration Overhead**: Instead of building connectors for every AI platform and every business system, organizations implement MCP once and gain compatibility across tools. This lowers the total cost of custom integration work.
-
-**Improved Governance**: MCP's transparent execution model makes it easier to enforce least-privilege access, log operations for compliance, and audit AI-driven changes to critical systems. This is essential for regulated industries and enterprises managing sensitive data.
-
-**Vendor Neutrality**: Because MCP is open, teams are not locked into a single AI provider or tool vendor. They can migrate assistants or add new tools without rewriting integrations.
+- **Accelerated Workflow Automation**: DevOps (and wider business) Teams can describe tasks using natural language and have AI safely execute them across multiple systems. A DevOps engineer might ask an AI assistant to "create a bug, assign it to the on-call engineer, and link it to the current incident," and MCP ensures each step is transparent and auditable.
+- **Reduced Integration Overhead**: Instead of building connectors for every AI platform and every business system, organizations implement MCP once and gain compatibility across tools. This lowers the total cost of custom integration work.
+- **Improved Governance**: MCP's transparent execution model makes it easier to enforce least-privilege access, log operations for compliance, and audit AI-driven changes to critical systems. This is essential for regulated industries and enterprises managing sensitive data.
+- **Vendor Neutrality**: Because MCP is open, teams are not locked into a single AI provider or tool vendor. They can migrate assistants or add new tools without rewriting integrations.
 
 In practice, MCP enables enterprises to deploy AI as a workflow accelerant while maintaining the security, auditability, and control that business-critical operations require. It shifts the conversation from "How do we safely let AI interact with our systems?" to "How do we standardize that interaction so it scales?"
 
@@ -146,10 +143,10 @@ Understanding this categorization helps you craft better prompts. If your reques
 
 Natural language is flexible, but operational systems still expect specific input. This is where you can craft 'better' prompts, which usually include:
 
-- project or repository scope
-- object type (bug, PR, build, pipeline)
-- clear action verb (list, create, update, link, run)
-- optional constraints (state, iteration, assignee, branch)
+- Project or repository scope
+- Object type (bug, PR, build, pipeline)
+- Clear action verb (list, create, update, link, run)
+- Optional constraints (state, iteration, assignee, branch)
 
 A more generic prompt (question) can still work, but often results in more follow-up questions or broad result sets. A scoped prompt lowers risk and improves speed. Since Generative AI communication between the prompt input and response using Large Language Models (LLMs) relies on tokens, it might also impact cost to have more focused prompts, resulting in a more focused prompt response. 
 

learn-pr/wwl-azure/manage-ado-mcp-server/includes/6-create-link-work-items.md

@@ -25,7 +25,7 @@ Even strong first prompts may require enrichment. Use follow-up actions to impro
 
 - Add reproduction steps in discussion comments
 - Assign ownership explicitly
-- Tag by area or platform (for example, `Android` or 'WindowsOS')
+- Tag by area or platform (for example, `Android` or `WindowsOS`)
 - Place the item in the correct sprint
 
 These details support triage, capacity planning, and faster root-cause analysis later.