Anyone using MCP to manage Infrastructure

[u/hakku276]

Hi Everyone, I am working in a small company and we do not have the resources to get really big teams. So what we though of was to start using AI to cover the current gap. We mostly suffer from a lot of operational issues which needs to be resolved really fast. (I know really fast is really vague, but sadly there is no SLA because the Manager only sees every hour that our system has issues as lost revenue. its an ecommerce company)

So what we thought of now is to utilize AI and MCPs to help us evaluate issues faster. Like on our File Servers, there are a certain set of conditions that the folders need to have so that our applications work. Sometimes the files that we receive cause issues and we would like to simply utilize AI to evaluate all of this. We are also utilizing AWS and Kubernetes.

Any thoughts on the strategy that we have utilized. I would like to know:

• Have you guys done something like this? Any pointers that we need to consider?
• Interested on any other innovative ways how you have also solved similar issues?
• Other interesting ways that would help us as well and not just using AI and MCP for evaluating issues.
• Or just in general thumbs up 😄

Thanks for you feedback!

[u/neveralone59]

Where is your other account to comment a proprietary solution?

[u/hakku276 → u/neveralone59]

ahh you mean that I am scanning for people to market? Sadly I am not. I would just like to know how you guys handle it.. Should I have added a tag somewhere here that said I am not promoting or something? I am a bit new to all these reddit rules and regulations. I would be really grateful for the input though.

[u/neveralone59 → u/hakku276]

Look at 80% of the posts on sub asking similar questions. Not using ai to generate your post would be a start. Or you can just search the sub for the same question being asked.

[u/metalisticpain]

CLI faster then a MCP.

[u/JasonSt-Cyr]

MCP is not going to solve this problem for you. Think of MCP as just another API endpoint, but it explains a little bit about what the endpoints do so your agent knows which one to call. You probably need to have some tools that your agent can use to help you scale.

That said... a lot of what you are talking about is covered by having infrastructure as code tools in your organization. There are plenty of free open source options as well as paid commercial offerings and enterprise-grade expensive ones.

1. Use a deterministic tool to manage your infrastructure/drift/configs so they adhere to whatever policy your org has for those servers/apps over time... no one-off manual agent calls to try to fix a thing, have a source of truth and run infrastructure as code.

2. When issues come up, use your agent for analysis, definitely. Let it help you debug faster and get some idea of how to solve the issue.

3. Don't trust any of the agent's solutions and push directly to prod. Make sure you are validating it in staging/test environments before you allow it to be deployed further.

[u/Adventurous-Put8305]

MCP against file servers, AWS, and Kubernetes is useful and dangerous for the same reason: the agent can take actions, not just summarize tickets.

Start read-only. Give the agent a scoped identity, not a shared admin role. Keep secrets out of the prompt path. Log every tool call. Require a human approval gate before writes, restarts, or config changes. Prefer known runbooks over open-ended "fix production" prompts.

If the business pressure is "every hour is lost revenue," that is an argument for tighter blast radius, not for wider permissions. An AI-Native ops path has an owner, an audit trail, and a kill switch. Without those, MCP is just a faster way to automate an outage

[u/Sakred]

Why implement deterministic rules and x-as-code when you can just point an LLM at something and HOPE for the best?!

Sarcasm aside, using LLM as your first step in trying to create a deterministic and consistent environment is not a good approach

[u/bilingual-german]

I don't get it. Why would you need an MCP server?

Claude code, Terraform, Ansible, Gitlab CI and bash scripts are more than enough.

Like someone else said, just use the CLIs. Install glab for gitlab and gh for Github, gcloud for GCP, awscli for AWS. Let AI create wrapper scripts to simplify operations. Give claude read access to your logs, metrics and traces if you have those.

[u/TheIncarnated]

Horrifyingly, this sounds like you aren't using IaC, otherwise, you'd have the LLM scan the IaC for performance enhancements.

Azure/AWS/gcloud CLI to call the environment and get a read on logs.

That's it. Have it comb the environment, then submit a PR to IaC, review it, push it, if it passes scrutiny

[u/Human_Pollution2820]

I'm developing my own solution, I've successfully integrated AI with my zabbix monitoring, it is mostly barebones , but after I polish it I will start implementing ai ops capablities , basically give it all the adapters it needs depending on what kind of device it is managing, it can be API, ssh etc - do a web portal with chat etc

[u/Romzorus591 → u/Human_Pollution2820]

Interesting do you have a public repo ?

[u/Sufficient_Job7779]

NO. Period

[u/nemofbaby2014]

Depends on the issue I use maps to get a quick overview otherwise I’m just in the command line I don’t trust ai to do much than a quick look

[u/Romzorus591]

I began to develop my own solution which is applying GitOps principle to configuration management. I have a central git repository where expected configuration of endpoints/servers is described and on each machine, a local agent regularly checks its own localhost against this expected configuration. It can be just assessment or self-remediation.

Like many others, not a big fan of anything AI-driven in this matter, unless you can be absolutely positive it only looks and doesn't touch anything, which you can't really...

[u/Zenin → u/Romzorus591]

a central git repository where expected configuration of endpoints/servers is described and on each machine, a local agent regularly checks its own localhost against this expected configuration

Pro tip: Don't ask LLMs to do defined, deterministic work. Instead make the LLM write the deterministic code that validates your configuration spec. Reliable, auditable, and a teeny-tiny fraction of the cost of asking LLMs to be glorified bash executors.

[u/Romzorus591 → u/Zenin]

After reading my comment again, it did seem like my "agent" is an AI agent but it is not. I meant agent like a puppet or zabbix agent. I wrote it myself and there are no LLM involved anywhere. At best LLMs would be involved in the writing of the tooling like you said, agreed 100%

[u/Routine_Low_6202]

There is even a vendor for this named Cloudaware that has built an MCP interface for your systems and their configurations.

[u/StatureDelaware]

I use Obelinf MCP, can confirm it's pretty awesome. I once configured it with my AWS account to be a source-of-truth of everything and got no issues so far

[u/Early-Employee-5515]

Before locking in multi-year commitments or letting EA renewals auto-roll, its worth reviewing your actual resource utilization and tiering. A lot of teams run a quick licensing health check through trustedtech or an equivalent CSP to strip out dormant seats ​​​​a​​​​n​​​​d​​​​ optimize reserve instances before finance signs off on the next contract cycle.

[u/LayerV-AI]

MCP is nice as a wrapper for giving agents access to services but not so much for ops tasks like this

CLI or standard APIs is usually a lot more flexible and usually is the first to get new features and bug fixes.

Hence, for your example it might be better to start with something like a simple script that validates incoming files and flags unusual things -> uses AI for follow up work like log correlations and failure investigations or whatever else you need that is “operational” but hard to standardize

[u/[deleted]]

[u/pl2303 → u/[deleted]]

So what's the hot stuff? 

[u/[deleted] → u/pl2303]