While the MCP ecosystem is vast and growing, a handful of servers form the essential foundation for any serious AI-assisted development workflow. By starting with these core integrations, you can immediately unlock significant productivity gains across the most common development tasks.
This guide highlights the five “must-have” categories of MCP servers and explains why each is a critical component of your toolkit.
Think of these servers as the pillars of your AI-powered development environment.
1. Version Control (Git & GitHub)
Why it’s essential: This is the most critical integration. It allows your AI assistant to read the history of your codebase, create branches, write commit messages, and open pull requests. It’s the bridge between the AI’s code generation and your team’s collaborative workflow.
2. Filesystem
Why it’s essential: Gives your AI the basic ability to read, write, create, and delete files and directories. Without it, the AI can only suggest changes; with it, the AI can apply them, creating new components, writing test files, and modifying configurations directly.
3. Up-to-Date Documentation (Context7)
Why it’s essential: Language models have a knowledge cutoff date. A documentation server like Context7 gives your AI real-time access to the latest official docs for thousands of libraries. This prevents it from generating code using deprecated APIs and is crucial for working with new technologies.
4. Browser Automation (Puppeteer/Playwright)
Why it’s essential: For web developers, this is a game-changer. It gives your AI control over a headless browser, allowing it to perform end-to-end tests, take screenshots to verify UI changes, and interact with your running application just like a user would.
Once you have the core four in place, add a project management integration to create a seamless workflow from ticket to deployment.
Why it’s essential: This server connects your AI directly to your team’s workflow. It can fetch the acceptance criteria from a Jira or Linear ticket, understand the task’s context, and even update the ticket’s status or add comments when the work is complete. This integration closes the loop, turning your AI into a true team member that participates in the project management process.
By starting with these essential servers, you create a powerful, integrated environment where your AI assistant can participate in every stage of the development lifecycle. Once you’ve mastered this core stack, you can explore the wider MCP ecosystem to find more specialized tools that fit your specific needs.
Microsoft has built an extensive collection of MCP servers that integrate deeply with their ecosystem:
Microsoft Learn Docs MCP
Real-time Microsoft documentation access
Azure MCP Suite
15+ specialized Azure connectors
GitHub MCP Server
Complete GitHub ecosystem integration
Azure DevOps MCP
Enterprise project management
# Microsoft Learn Docs (VS Code)# Click: Install in VS Code button on server page
# Azure MCP Suitenpx -y @azure/mcp@latest
# GitHub MCP (hosted)# Configure in VS Code: https://api.githubcopilot.com/mcp/
# Azure DevOpsnpx -y @azure-devops/mcp@latestThe MCP ecosystem extends far beyond Microsoft’s offerings:
Cloudflare MCP
Edge computing & CDN management
Stripe MCP
Payment processing integration
MongoDB MCP
NoSQL database operations
Linear MCP
Modern issue tracking
MarkItDown MCP
Universal document conversion
Playwright MCP
Advanced browser automation
SQL Server MCP
Microsoft SQL integration
Notion MCP
Knowledge base integration