Use simple prompts to design and launch orchestration tools.
What is the main purpose of MCP servers?
Do I need to install MCP servers locally?
Can MCP servers work with any AI assistant?
What makes MCP servers different from other dev tools? MCP servers connect AI models directly to tools like GitHub and SQL, helping you skip repetitive glue code and speed up development with smarter, context-aware automation.
Top developers aren’t stitching tools together manually—they’re working smarter.
Many teams waste time switching between editors, version control, databases, and documentation. It interrupts flow and drains focus.
So how are others cutting through the noise?
MCP servers offer a better path. Built on the Model Context Protocol, they enable AI assistants to connect directly to tools such as GitHub, Azure, and SQL databases. With mcp servers, you can remove repetitive steps and automate cross-tool interactions without writing glue code.
This blog explains how they work, why they matter, and how you can get started using them. You'll find practical setup tips, real examples, and leading server implementations to improve your daily workflow.
At their core, MCP servers implement the Model Context Protocol (MCP), an open standard created by Anthropic. This protocol enables AI models, especially large language models, to securely interact with data sources, tools, and platforms within a developer’s environment.
This interaction occurs through a client-server architecture, where tools like Claude Desktop or Visual Studio Code serve as MCP clients, and external systems—such as GitHub, SQL databases, or file systems—are connected via installed MCP servers. These MCP servers expose capabilities such as file operations, SQL queries, or messaging capabilities, all accessible through natural language commands.
Key Benefit: You no longer need to write glue code or manually shift context. Your AI assistant can, for instance, manipulate Git repositories, extract logs, summarize PRs, or interact with enterprise data, all in one flow.
Through the model context protocol, tools are registered with metadata, allowing AI agents to discover and invoke them at runtime. This eliminates the need for hardcoding integrations, speeding up development.
Built-in support for OAuth 2.1, secure file operations, and read-only database access ensures that access to data sources is controlled and auditable. This is essential when handling enterprise data or working in regulated environments.
Each MCP server can handle specific tasks—such as repository management, web content fetching, or database interactions—and can be combined for more complex operations, thanks to a unified interface.
By providing AI with real-time, secure access to tools, you enable features like semantic search, browser automation, or location services without writing bespoke code for each tool.
Here’s a simplified view of the interaction between an AI assistant, MCP clients, and servers:
The AI assistant communicates with an MCP client such as Claude Desktop, which in turn connects to one or more installed MCP servers. Each server provides a specific capability like managing GitHub issues, accessing files, or querying databases.
Microsoft has invested heavily in building MCP tools. These support seamless integration with its ecosystem and simplify tasks such as documentation lookup, cloud resource deployment, and SQL Server management.
| Server Name | Key Capabilities | Relevant Use Cases |
|---|---|---|
| Azure MCP Server | Manage Azure pipelines & functions | Cloud automation, service management |
| GitHub MCP Server | Pull requests, issues, and branches | Repository management, manipulating git repositories |
| SQL Server MCP Server | Interact with Microsoft SQL Server | Database interactions, extract data |
| Microsoft 365 Agents Toolkit | Work with Office apps | Channel management, messaging capabilities |
| Playwright MCP Server | Browser testing | Browser automation, web content fetching |
Other servers extend MCP to specialized domains:
Slack MCP Server: Enables channel management, message summarization
Brave's Search API Server: Adds local search, semantic search, and web content fetching
Apify and Puppeteer Servers: Support robust browser automation
PostgreSQL & SQLite Servers: Provide read-only database access or full control for SQL queries
Filesystem Server: Offers secure file operations, file access, and document edits
Pro Tip: Pairing these with Claude Desktop enables an AI powered development environment that feels truly agentic.
Enable Agent Mode
Add server URLs to settings.json
Select and test tools from the Extensions tab
Use .mcp.json to define tools and access
Assign configurable access controls for security
Use tool approval prompts to ensure secure access
| Deployment Mode | Best For | Benefits |
|---|---|---|
| Local | Solo developers using Claude Desktop or VS Code | Lower latency, fast prototyping |
| Cloud (e.g., via Cloudflare) | Teams and remote setups | Scalable, centralized permissions |
“Let’s talk about Model Context Protocol or MCP. It’s an open standard that allows AI to securely interact with tools and data, creating a consistent, powerful development experience.”
— Mitchell Ashley, LinkedIn
MCP is compatible with many popular programming languages:
Official SDKs: Python, JavaScript, Java, Swift, Kotlin, C#
Community SDKs: Rust, Go, PHP, Ruby
This broad support makes it easy to build custom integrations and embed MCP into AI workflows.
To fully unlock the power of MCP servers, consider combining them with:
AI assistants that operate in agent mode
Enabling AI access to your knowledge base and reference servers
Using AI image generation tools via external MCP connectors
Logging interactions with database tables, ensuring safe and compliant access
Leveraging repository management via github api integration for automation
MCP servers directly tackle the biggest challenges developers face—manual context switching, fragmented toolchains, and limited AI integration. By enabling secure file operations, dynamic tool access, and seamless connections to key data sources, MCP servers streamline your entire development workflow.
As AI continues to reshape how we build software, the Model Context Protocol offers a powerful way to bring intelligence, security, and structure into your tool stack. From repository management to SQL queries, this approach is more than just an upgrade—it is quickly becoming essential.
Now is the time to implement the MCP server configuration, empower your AI assistants, and transform your development process. Explore available tools, set up your installed MCP servers, and unlock the full potential of your workflow.
