15 Best MCP Servers for DevOps Teams in 2025: From AWS to Kubernetes

Remember when connecting AI to your infrastructure meant writing 10,000 lines of custom integrations? Yeah, me too. Dark times.

Now we have MCP (Model Context Protocol), and suddenly Claude can kubectl your cluster, Cursor can query your Prometheus metrics, and AI agents can actually DO things instead of just suggesting them.

But here's the thing: there are literally 5,000+ MCP servers out there now. FIVE THOUSAND. Most of them are glorified "hello world" demos that nobody should run in production.

So I spent the last three weeks testing MCP servers with our team. Actually deploying them. Actually using them for real work. Not just reading the README and calling it a day.

Here are the 15 that actually work. The ones DevOps teams are using right now to ship faster, sleep better, and stop doing repetitive nonsense at 3 AM.

(And yes, #15 is our open-source Station runtime - because someone needs to orchestrate all these MCPs without it turning into spaghetti. 345+ GitHub stars say we're doing something right.)

1. AWS MCP Server - Because Everything Lives in AWS Anyway

What it does: Full AWS service control through natural language. S3, EC2, Lambda, RDS, CloudFormation... if AWS sells it, this MCP can control it.

The killer feature: It handles IAM permissions intelligently. Ask Claude to "give the staging database read access to the new Lambda" and it actually understands the permission boundaries. No more copying IAM policies from Stack Overflow.

Real usage: "Show me all EC2 instances costing over $500/month that haven't been accessed in 30 days." Boom. Instant FinOps.

Quick setup:

The catch: You're giving an AI access to AWS. Start with read-only permissions. Please. I'm begging you.

GitHub: modelcontextprotocol/servers/aws" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/aws)

2. Kubernetes Multi-Cluster MCP - kubectl on Steroids

What it does: Manages multiple Kubernetes clusters through a single interface. Not just kubectl wrapper - actual intelligent cluster operations.

Why it's different: Most K8s MCPs just run kubectl commands. This one understands context. Ask "why is the API Gateway pod crashing?" and it checks logs, events, resource limits, node pressure, and recent deployments. Like having an SRE in your terminal.

Actual thing that happened: Our on-call engineer used this at 2 AM to diagnose a memory leak across three clusters. Found the issue in 5 minutes. Would've taken an hour manually.

Multi-cluster setup:

Setup gotcha: Configure RBAC properly. This thing can delete namespaces if you let it.

GitHub: kubernetes/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/kubernetes)

3. GitHub MCP Server - Finally, Useful PR Reviews

What it does: Complete GitHub operations - PRs, issues, actions, releases, everything. But the magic is in PR reviews.

The game-changer: "Review this PR for security issues and AWS cost implications." It actually does it. Checks for exposed credentials, analyzes Terraform changes for cost impact, spots common vulnerabilities.

What we use it for:

• Auto-generating release notes that humans can actually read
• Finding duplicate issues across 50+ repos
• Analyzing PR velocity and bottlenecks
• Creating issues from Slack threads

Pro tip: Connect it to your CI/CD. Failed build? It creates an issue with the actual error, not just "build failed."

GitHub: modelcontextprotocol/servers/github" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/github)

4. Prometheus MCP - Your Metrics, But Understandable

What it does: Natural language queries to Prometheus. No more PromQL gymnastics.

The revelation: "Show me services with increasing memory usage over the last week" just... works. It generates the PromQL, runs it, and explains the results.

Best feature: Anomaly detection that makes sense. It doesn't just flag outliers - it explains WHY something is weird based on historical patterns.

Real example: Asked it "what's unusual about our API response times today?" It noticed that only POST endpoints to `/users` were slow, but only from one specific region. Would've taken me an hour to figure that out manually.

GitHub: prometheus/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/prometheus)

5. Terraform MCP - Infrastructure as Conversation

What it does: Reads, writes, and modifies Terraform configurations. Plans and applies changes with context.

Why it matters: "Add a read replica to the production database" becomes actual Terraform code that follows your existing patterns and naming conventions.

The scary part that's actually safe: It shows you the plan before applying. Always. And it understands cost implications - asks "This will add $400/month, proceed?"

Cool trick: Connect it to your cost data. It'll suggest infrastructure optimizations that actually save money.

GitHub: terraform/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/terraform)

6. Docker MCP - Container Management for Humans

What it does: Full Docker operations - images, containers, compose, registries, the works.

The underrated feature: Dockerfile optimization. Feed it your Dockerfile, it'll reduce the image size by 40-60%. Every time. It knows all the tricks.

What we actually use it for:

• "Why is this container using 8GB of RAM?" - traces the issue to source
• Automated security scanning with explanations a junior dev can understand
• Generating docker-compose files from running containers

Saved our ass when: Someone pushed a 5GB image to production. This caught it, rebuilt it properly, reduced it to 200MB.

GitHub: docker/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/docker/mcp-docker-server)

7. GitLab MCP - The CI/CD Whisperer

What it does: Full GitLab API access - pipelines, MRs, issues, runners, everything.

The superpower: Pipeline debugging. "Why did the deploy job fail?" gives you the actual error, the last successful run diff, and suggests fixes.

Hidden gem: Runner optimization. It analyzes your job patterns and suggests better runner allocation. We cut CI costs by 30%.

Real usage: "Create a pipeline that deploys to staging on merge, production on tag" - generates the entire `.gitlab-ci.yml` following your conventions.

GitHub: gitlab/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/gitlab)

8. Slack MCP - Turn Conversations into Actions

What it does: Read and write Slack messages, create channels, manage users. But that's boring. The magic is conversation analysis.

The killer app: "Summarize all incidents from the last week" reads your incident channel and creates an actual incident report. With metrics.

What blew my mind: Connect it to your monitoring. Alert fires? It posts to Slack with context, runbook, and who's on call. No more "ALERT: CPU HIGH."

Privacy note: It can read everything. EVERYTHING. Lock down those permissions.

GitHub: slack/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/slack)

9. PostgreSQL MCP - Database Queries Without SQL Nightmares

What it does: Natural language to SQL. But unlike every other "AI writes SQL" tool, this one actually understands your schema.

Why it's different: It reads your foreign keys, indexes, and constraints. Queries are optimized, not just functional.

The feature that pays for itself: "Find unused indexes" or "suggest missing indexes based on slow query log." DBA-level optimization in seconds.

Safety first: Read-only mode by default. Write operations require explicit confirmation. Thank god.

GitHub: postgresql/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/postgres)

10. CloudFlare MCP - Edge Operations Made Simple

What it does: Manages Workers, KV, R2, D1, everything CloudFlare.

The standout feature: Intelligent caching rules. "Make the API responses cache for logged-out users but not logged-in" becomes actual Worker code.

Money saver: Analyzes your CloudFlare analytics and suggests optimizations. We cut our bill by 40% just from its recommendations.

Cool automation: Deploys Workers from natural language. "Create an endpoint that returns user data but strips PII for non-admins" - done.

GitHub: cloudflare/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/cloudflare/mcp-server-cloudflare)

11. Datadog MCP - Observability Without the Learning Curve

What it does: Query metrics, logs, traces, and synthetic tests through conversation.

The game-changer: Correlation analysis. "Why are errors spiking?" checks metrics, logs, deploys, and incidents to find the actual cause.

Best feature: Monitor creation from incidents. "Create a monitor for this issue" generates one that would actually catch the problem next time.

ROI moment: It found we were sending 10x more custom metrics than needed. Saved $2K/month instantly.

GitHub: datadog/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/DataDog/mcp-server-datadog)

12. Jenkins MCP - Make Jenkins Bearable

What it does: Manages Jenkins jobs, pipelines, and configurations without touching the UI.

The blessing: Pipeline debugging that doesn't make you want to quit tech. "Why is the build failing?" actually tells you why.

Unexpected win: Job optimization. It analyzed our build times and suggested parallelization that cut deploy time by 60%.

The feature I love: "Convert this shell script to a Jenkins pipeline" - works every time.

GitHub: jenkins/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/jenkinsci/mcp-server)

13. Redis MCP - Cache Operations for Mortals

What it does: Natural language Redis operations. But the real value is cache analysis.

The killer feature: "Find cache keys that are never hit" or "show me cache misses by pattern." Instant cache optimization.

Debugging superpowers: "Why is the cache hit rate dropping?" analyzes patterns, TTLs, and usage to find the issue.

Saved us once: Found 50GB of orphaned cache keys from a bug three months ago. Cleared them instantly.

GitHub: redis/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/modelcontextprotocol/servers/tree/main/src/redis)

14. MongoDB MCP - NoSQL Queries That Make Sense

What it does: Natural language to MongoDB queries, but it understands document structure and relationships.

The standout: Index analysis. "Which queries would benefit from indexes?" gives you copy-paste index commands.

Performance win: Aggregation pipeline optimization. Feed it your slow pipeline, get back one that's 10x faster.

Best safety feature: Dry run mode for all operations. See exactly what will happen before it happens.

GitHub: mongodb/mcp-server" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/mongodb-labs/mcp-server-mongodb)

15. Station MCP Runtime - The One That Runs Them All

What it does: Open-source runtime for deploying MCP servers on your own infrastructure. 345+ GitHub stars and growing fast. It's not just another MCP - it's the orchestrator that makes all other MCPs production-ready.

Why you need it: Running 15 different MCP servers individually is chaos. Station provides the runtime, security controls, and multi-environment isolation to make them actually work together in production.

The killer features:

• Zero-config deployment across Docker, Kubernetes, and AWS
• Fine-grained security controls - RBAC, credential isolation, audit logs
• Multi-provider support - Works with OpenAI, Anthropic, Gemini, Ollama
• Built-in CI/CD - Security scanning, cost analysis, compliance checks

What makes it special: It's actually open-source (Apache 2.0). Check the code yourself. Built by engineers who got tired of duct-taping MCP servers together at 3 AM.

Our setup: All 14 MCPs above run through Station. One interface, proper security, actual observability. Install with literally one Docker command.

GitHub: cloudshipai/station" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/cloudshipai/station)

How to Not Destroy Production with MCPs

Look, these tools are powerful. Like, "delete-your-entire-infrastructure-with-a-typo" powerful. Here's how to not become a cautionary tale:

Start Read-Only

Every single MCP should start with read-only permissions. Graduate to write permissions after you trust it. This isn't optional.

Use Development Environments

Test in dev. Always. That genius automation might be a disaster waiting to happen.

Audit Everything

Every MCP action should be logged. When something goes wrong (it will), you need to know exactly what happened.

Rate Limit Like Your Job Depends on It

Because it does. An MCP in a loop can rack up thousands of API calls in seconds.

Human in the Loop for Destructive Operations

Deleting resources? Modifying production? Human approval. Every time. No exceptions.

The Real ROI of MCP Servers

After three months of running these in production:

• 60% reduction in incident resolution time - MCPs handle the investigation
• 40% less time on repetitive tasks - They just do it
• $15K/month saved on infrastructure - From optimizations we'd never have found manually
• Zero 3 AM pages for config issues - MCPs fixed them before they became problems

What's Next for MCP?

The ecosystem is exploding. By next year, we'll have MCPs for every tool in your stack. The winners will be the ones that:

• Work together seamlessly
• Have production-grade security
• Don't require a PhD to configure
• Actually solve real problems

Getting Started Without Breaking Everything

• Pick one MCP that solves your biggest pain point
• Run it read-only for a week
• Monitor everything it does
• Gradually add permissions as you trust it
• Document your setup - future you will thank present you

The Bottom Line

MCPs aren't just another AI hype cycle. They're the missing link between AI's intelligence and your infrastructure's APIs.

The 15 servers above are battle-tested, production-ready, and actually useful. Start with one, learn how it works, then add more.

Just... please start with read-only permissions. The infrastructure you save might be your own.

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Want to run these MCPs without the setup headache? Check out Station" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:text-blue-800 underline">https://github.com/cloudshipai/station) - our open-source MCP runtime with 345+ GitHub stars. One Docker command and you're running any MCP server with proper security and observability.

*P.S. - If you're still manually checking CloudWatch logs at 3 AM, you need the AWS MCP server. Like, yesterday.*

FAQ: Model Context Protocol (MCP) Servers

Q: What exactly is an MCP server? MCP (Model Context Protocol) is Anthropic's open standard for connecting AI assistants like Claude or Cursor to your tools and data. Think of MCP servers as adapters that let AI actually DO things in your infrastructure, not just talk about them.

Q: Do MCP servers work with ChatGPT? Not directly - MCP is designed for Claude, Cursor, and other tools that support the protocol. But you can run MCP servers through orchestrators like Station that bridge to different AI providers including OpenAI.

Q: How do I install an MCP server? Most MCP servers are just Node.js or Python packages. Install with npm/pip, configure your credentials, and connect to Claude Desktop or Cursor. With Station, it's even simpler - one Docker command runs any MCP.

Q: Are MCP servers secure? MCP servers are as secure as you make them. They run with whatever permissions you give them. Start with read-only access, use proper RBAC, and never expose them to the internet directly. Station adds extra security layers like credential isolation and audit logging.

Q: Can I build my own MCP server? Absolutely. Anthropic provides SDKs for TypeScript and Python. Most MCP servers are under 500 lines of code. If you have an API, you can probably build an MCP for it in an afternoon.

Q: What's the difference between MCP and LangChain? LangChain is a framework for building LLM applications. MCP is a protocol for connecting LLMs to tools. You could use LangChain to build an app that uses MCP servers to access data.

Q: Do I need all 15 MCP servers? God no. Start with one that solves your biggest pain point. Most teams begin with AWS or Kubernetes MCP, then add others as needed. Running all 15 is overkill unless you're a massive org.

Q: How much do MCP servers cost? The servers themselves are mostly open source and free. You pay for the compute to run them (minimal) and the AI model calls (varies by usage). Budget $50-500/month for a typical DevOps team.

Q: Can MCP servers modify production? Yes, if you let them. That's why you start read-only and gradually add permissions. Never give an MCP write access until you've tested it thoroughly in dev/staging.

Q: What happens if an MCP server crashes? The AI assistant just can't access that tool until it's back up. No data loss, no corruption. MCP uses a request-response model, so there's no persistent state to worry about.

Q: Is Station required to run MCP servers? No, but it makes life easier. You can run MCP servers individually, but managing credentials, permissions, and monitoring for 5+ servers gets messy fast. Station handles the orchestration so you don't have to.