CloudOps Series

Turbocharge Your CloudOps Workflow: MCP Servers which I use daily

A Deep Dive into Practical Model Context Protocol Servers for Day-to-Day Acceleration

4 min readJul 30, 2025

The Model Context Protocol (MCP) is quickly emerging as a foundational standard for integrating AI systems. Designed as an open protocol, MCP allows AI models to interface effortlessly with external tools, data sources, and applications. Its growing adoption is largely due to its lightweight design and adaptability with minimal setup, developers can plug AI-powered apps into a broad and expanding ecosystem, streamlining integration without added complexity.

I am using AI features in my day to day workflows for quite sometime now. I can definitely say that context switching is our silent productivity killer. You’re writing Terraform, CDK or debugging Lambda policies, or looking for a documentation, each step involves rummaging for docs, code samples, how-tos, and best practices. The result? Dozens of browser tabs, fragmented attention, and slow progress.

What if there were a way to bring the collective intelligence of documentation, code patterns, and organizational standards right into your IDE, contextually surfacing exactly what you need at the right moment? Enter Model Context Protocol (MCP) servers, the secret sauce that has reshaped my daily workflow. Let’s dive into how I use MCP servers with tools like Cursor, GitHub Copilot, Kiro and others to automate, accelerate, and standardize my DevOps/CloudOps work.

What is MCP (Model Context Protocol), Briefly?

MCP is an open protocol for integrating AI models with external tools, APIs, and rich data sources in a context-aware way. In practice, MCP servers act as smart plugins for your IDE, surfacing documentation, templates, architecture diagrams, and much more based on what’s in your editor.

Why MCP Servers?

Before diving into specific servers, let’s clarify the value proposition of MCP servers in the context of DevOps and CloudOps:

· Contextual Awareness: MCP servers provide your IDE or development environment with real-time, contextual access to documentation, code snippets, infrastructure templates, and more.

· Boosted Productivity: By offering on-demand, context-aware resources without switching tabs or tools, MCP servers help developers and operators remain in their flow state.

· Consistency & Automation: Using MCP servers means less copy-pasting from random sources and more using validated, organizationally approved best practices in code and operations.

My daily toolkit looks like this:

IDE/AI assistant: GitHub with Copilot, Cursor, Kiro, Amazon Q
MCP Servers: Carefully curated set, each addressing specific DevOps/CloudOps challenges

Below, I’ll detail each MCP server I rely on, and how it transforms my workflow.

AWS Docs

I use the AWS Docs MCP to fetch official API specs, resource limits, and best practices in-line.

Real scenario: Debugging a tricky S3 permissions issue, I highlighted my resource block, AWS Docs MCP instantly surfaced the right IAM policy pattern, with links to the official doc and a code snippet to drop in. Zero tab-switching.

"aws-docs": {
      "command": "uvx",
      "args": [
        "awslabs.aws-documentation-mcp-server@latest"
      ],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "disabled": false,
      "autoApprove": []
}

AWS CDK MCP

I primarily work with AWS, so my go to tool for writing Infra is AWS CDK. The AWS CDK MCP lets me search for reusable constructs and view code samples as I build directly in my IDE.

Example: CDK construct for API Gateway

"awslabs-cdk-mcp-server": {
  "command": "uvx",
  "args": [
    "awslabs.cdk-mcp-server@latest"
  ],
  "env": {
    "FASTMCP_LOG_LEVEL": "ERROR"
  },
  "disabled": false,
  "autoApprove": []
}

Terraform via AWS Labs

The Terraform MCP (via AWS Labs) delivers pre-validated modules, resource documentation, and vetted usage patterns.

Example: Can you write terraform module for API gateway

"awslabs.terraform-mcp-server": {
  "command": "uvx",
  "args": [
    "awslabs.terraform-mcp-server@latest"
  ],
  "env": {
    "FASTMCP_LOG_LEVEL": "ERROR"
  },
  "disabled": false,
  "autoApprove": []
}

DuckDuckGo

Some answers live outside official docs. The DuckDuckGo MCP lets me securely query Stack Overflow, blogs, and more without leaking search data or breaking focus.

Example: I want to build an MCP server using FastAPI

"ddg-search": {
      "command": "uvx",
      "args": [
        "duckduckgo-mcp-server"
      ],
      "disabled": false,
      "autoApprove": [
        "search",
        "fetch_content"
      ]
    },

Context7

Context7 MCP is a tool that supercharges AI prompts with real-time, version-specific documentation and code examples.

Example: Create a basic NodeJs project using next js and include a app router. use context7

"Context7": {
  "command": "npx",
  "args": [
    "-y",
    "@upstash/context7-mcp"
  ],
  "env": {},
  "disabled": false,
  "autoApprove": []
}

Microsoft Docs

This mcp brings up-to-date information directly from Microsoft’s official documentation.

Example: What models are available in EU regions

"microsoft.docs.mcp": {
   "type": "http",
   "url": "https://learn.microsoft.com/api/mcp"
},

Why I Don’t Use General GitHub or Other MCPs

While GitHub and other generic services offer their own MCP servers, I intentionally prefer direct, natural interaction with those platforms. My philosophy is to leverage MCP servers where they provide unique, context-enriching value such as surfacing documentation, code patterns, or visualizations within the IDE while using GitHub and similar tools for source control, code review, and collaboration in their native interfaces. This division of labor keeps my workflow both streamlined and focused.

Best Practices for Integrating MCP Servers into Your Workflow

Curate Your MCP Stack: Avoid cognitive overload by integrating only those MCP servers that align with your daily needs.

Stay Updated: Routinely check for updates to your MCP servers to benefit from improved features, security patches, and new templates.

Foster Team Adoption: Advocate for your team to embrace contextual, IDE-driven practices. Sharing custom MCP configurations can help establish a shared foundation for productivity.