Understanding MCP and MCP Servers in DevOps: A Beginner's Guide

Table of Contents

• What is MCP?

• What Are MCP Servers?

• Why MCP and MCP Servers Matter in DevOps

• How MCP Works in DevOps: A Simple Example

• Getting Started with MCP Servers in DevOps

  • Choose a Tool with MCP Support

  • Set Up an MCP Server

  • Test with Simple Commands

  • Explore Community MCP Servers

• Challenges to Watch Out For

• The Future of MCP in DevOps

• Conclusion

Welcome to the exciting world of DevOps, where automation and collaboration make software development faster and more reliable! If you're new to DevOps, you might have heard about the Model Context Protocol (MCP) and MCP servers buzzing around in tech discussions. These tools are transforming how DevOps teams use artificial intelligence (AI) to streamline workflows. In this article, we'll explore what MCP and MCP servers are, why they matter in DevOps, and how they can make your life as a DevOps beginner easier. Let’s dive in!

Prerequisites

Before diving into using MCP and MCP servers in your DevOps workflow, ensure you have the following:

1. Basic DevOps Knowledge: Familiarity with DevOps concepts like CI/CD pipelines, version control (e.g., Git), or cloud platforms (e.g., Azure, AWS) is helpful but not mandatory.

2. Tool with MCP Support: Install an IDE or platform with MCP client support, such as Visual Studio Code with GitHub Copilot in agent mode or Claude Desktop.

3. Dependencies for MCP Servers: Depending on the MCP server, you may need:

   • Node.js or Python installed for running server scripts.

   • A package manager like npm for Node.js-based servers.

4. Credentials for DevOps Tools: Have access to credentials (e.g., Azure DevOps personal access token, GitHub token) for the tools you want to connect to.

5. Local or Cloud Environment: A local machine or cloud platform (e.g., Azure Functions) to run the MCP server.

6. Internet Access: Required to download MCP server packages and access documentation or community repositories like GitHub or mcp.so.

What is MCP?

Imagine you’re trying to teach a super-smart assistant (like an AI model) to help you with your DevOps tasks, such as checking server status or managing code repositories. The problem? Most AI models are great at generating text but can’t directly interact with your tools, like Azure DevOps or GitHub. This is where the Model Context Protocol (MCP) comes in.

MCP is an open standard introduced by Anthropic in late 2024. It acts like a universal translator, allowing AI models to connect with external tools, databases, and services in a standardized way. Think of MCP as a “USB-C cable” for AI—it provides a common interface so AI can “plug into” various systems without needing custom code for each one.

In DevOps, MCP lets AI assistants understand and act on your instructions in natural language (like “list my open pull requests”). Instead of you manually navigating tools or writing scripts, MCP enables the AI to fetch data or perform actions by communicating with specialized programs called MCP servers.

What Are MCP Servers?

An MCP server is a lightweight program that acts as a bridge between an AI model and a specific tool or data source. It’s like a friendly librarian who knows exactly where to find the information you need and can deliver it in a way the AI understands. MCP servers follow the MCP standard to provide AI with access to resources (like files or databases) or perform actions (like creating a GitHub issue).

Here’s a simple analogy: imagine you’re asking your AI assistant to check your Azure DevOps project for open tasks. The AI (the “client”) sends your request to an MCP server designed for Azure DevOps. The server talks to Azure DevOps, fetches the task list, and sends it back to the AI, which then presents it to you in plain English. This all happens seamlessly, thanks to the standardized MCP “language” they speak.

MCP servers can connect to all sorts of systems, such as:

• Cloud platforms like Azure or AWS for managing resources.

• Version control systems like GitHub or GitLab for code-related tasks.

• Databases like PostgreSQL or Cosmos DB for querying data.

• Collaboration tools like Slack or Jira for team communication and project management.

Why MCP and MCP Servers Matter in DevOps

As a DevOps beginner, you’re probably learning about tools like CI/CD pipelines, containerization (e.g., Docker), and infrastructure as code (e.g., Terraform). These tools generate a lot of data and require constant monitoring and management. MCP and MCP servers make this easier by letting AI handle repetitive or complex tasks through natural language commands. Here’s why they’re a game-changer:

1. Simplify Complex Tasks: Instead of writing scripts or navigating multiple dashboards, you can ask an AI to “find which S3 buckets are publicly accessible” or “deploy this code to Azure.” The MCP server does the heavy lifting.

2. Save Time: MCP servers let AI fetch real-time data or perform actions instantly, reducing the time you spend on manual tasks.

3. Standardized Integration: Without MCP, connecting AI to every tool requires custom code, which is time-consuming and error-prone. MCP’s universal standard means one integration works across many AI models and tools.

4. Boost Productivity: By automating routine DevOps tasks (like checking logs or creating branches), MCP servers free you to focus on higher-value work, like designing better pipelines or improving system reliability.

How MCP Works in DevOps: A Simple Example

Let’s walk through a real-world scenario to see MCP and MCP servers in action. Suppose you’re a DevOps engineer managing an Azure DevOps project. You want to know which pull requests are waiting for review in your repository. Here’s how MCP makes it happen:

1. You Ask the AI: Using a tool like Visual Studio Code with GitHub Copilot in agent mode, you type, “Show me all open pull requests in my Azure DevOps repo.”

2. AI Talks to the MCP Client: The AI (acting as the MCP host) sends your request to an MCP client, a component built into the tool that handles communication with MCP servers.

3. MCP Client Contacts the MCP Server: The client connects to an Azure DevOps MCP server, which knows how to interact with Azure DevOps APIs. The server interprets your request and queries the Azure DevOps system for open pull requests.

4. MCP Server Responds: The server retrieves the list of pull requests and sends it back to the AI via the MCP client.

5. AI Delivers the Answer: The AI formats the response in natural language, saying something like, “You have three open pull requests in the ‘main’ branch: PR #123, PR #124, and PR #125.”

This process happens in seconds, and you didn’t need to write a single line of code or log into Azure DevOps manually!

Getting Started with MCP Servers in DevOps

Ready to try MCP servers in your DevOps journey? Here’s a beginner-friendly guide to get started:

Choose a Tool with MCP Support

Start with an IDE or platform that supports MCP, like Visual Studio Code with GitHub Copilot in agent mode or Claude Desktop. These tools have built-in MCP clients that can connect to MCP servers.

Set Up an MCP Server

Many pre-built MCP servers are available for popular DevOps tools. For example:

• Azure DevOps MCP Server: Lets AI query projects, work items, or repositories. You can install it from GitHub and configure it with your Azure DevOps credentials.

• GitHub MCP Server: Enables AI to manage repositories, create issues, or list pull requests.

• PostgreSQL MCP Server: Allows AI to run SQL queries on your database.

To set up an MCP server, you’ll typically need to:

• Install dependencies (e.g., Python or Node.js).

• Configure the server with credentials (like an Azure DevOps personal access token).

• Run the server locally or deploy it to a cloud platform like Azure Functions.

For example, to set up the Azure DevOps MCP server, you might use a configuration file like this:

{
  "mcpServers": {
    "azureDevOps": {
      "command": "npx",
      "args": ["-y", "@tiberriver256/mcp-server-azure-devops"],
      "env": {
        "AZURE_DEVOPS_ORG_URL": "https://dev.azure.com/your-organization",
        "AZURE_DEVOPS_AUTH_METHOD": "pat",
        "AZURE_DEVOPS_PAT": "<YOUR_PAT>",
        "AZURE_DEVOPS_DEFAULT_PROJECT": "your-project-name"
      }
    }
  }
}

Save this as mcp.json in your VS Code workspace, and the server will be ready to handle your AI requests.

Test with Simple Commands

Once your MCP server is running, try simple commands in your AI tool, like:

• “List all my Azure DevOps projects.”

• “Create a new branch in my GitHub repo.”

• “Query my database for the latest user activity.”

Explore Community MCP Servers

The MCP ecosystem is growing fast, with over 1,000 community-built servers by early 2025. Check out repositories like mcp.so or GitHub for servers that connect to tools like Jira, Docker, or AWS. These can inspire you to automate more tasks or even build your own MCP server!

Challenges to Watch Out For

While MCP is powerful, it’s not without challenges, especially for beginners:

• Setup Complexity: Some MCP servers require configuring environments (e.g., installing Python or Docker), which can be tricky if you’re new to DevOps. Start with well-documented servers and follow their setup guides.

• Security Risks: MCP servers can access sensitive data, so always use trusted servers and apply least-privilege principles (e.g., limit the server’s access to only what it needs).

• Learning Curve: Understanding how AI, MCP clients, and servers work together takes practice. Be patient and experiment with small tasks first.

The Future of MCP in DevOps

MCP is still a young technology, but it’s already making waves in DevOps. As more tools adopt MCP, you’ll see AI assistants become even smarter, handling complex workflows like:

• Automating CI/CD pipeline monitoring and error fixing.

• Querying infrastructure logs to diagnose issues in real-time.

• Managing cloud resources across multiple platforms with a single prompt.

For DevOps beginners, MCP offers a way to leverage AI without needing to be an expert in coding or AI. It’s like having a super-smart teammate who can translate your natural language requests into technical actions.

Conclusion

The Model Context Protocol (MCP) and MCP servers are opening new doors for DevOps teams by making AI a practical tool for everyday tasks. By connecting AI models to your DevOps tools through a standardized interface, MCP servers simplify workflows, save time, and boost productivity. As a beginner, start small by experimenting with pre-built MCP servers for tools you already use, like Azure DevOps or GitHub. With practice, you’ll find MCP becoming a powerful ally in your DevOps journey.

Ready to explore? Check out the official MCP documentation at modelcontextprotocol.io or explore MCP server repositories on GitHub to find tools that fit your workflow. Happy automating!