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TL;DR

The DevOps Stack is 6 MCPs (GitHub, Docker, AWS, Sentry, Grafana, Slack) that cover CI/CD, cloud, observability, and incident response. One Claude session handles deploys, debugs, and incidents end-to-end. Replaces ~$400/mo of disparate SaaS subscriptions and cuts incident response time by 50%.

🐙🐳☁️🐛📊+1
Stack · 6 MCPs

The DevOps Automation Stack

CI, deploy, observability, and infrastructure — automated end-to-end

Install the whole stack

$ mcpizy install github docker aws sentry grafana slack

One command installs and configures all 6 MCPs for Claude Code, Cursor, Windsurf, or any MCP-compatible client.

Why this stack?

DevOps engineers manage six tools at once, all day: git + CI, containers, cloud, errors, metrics, comms. This stack is those six as MCPs — one Claude session for the whole ops workflow. When a 3am PagerDuty alert fires, you're not re-learning each CLI under pressure; you're describing what you see and Claude is pulling from every MCP to give you a full picture in 30 seconds.

This isn't a replacement for your existing monitoring — it's an amplifier. Grafana + Sentry + CloudWatch are already great; the MCP layer makes them conversational, so the right data shows up without you context-switching.

MCPs in this stack (6)

🐙

GitHub

CI/CD + source of truth

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🐳

Docker

Container builds & runtime ops

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☁️

AWS

Cloud primitives (S3, Lambda, ECS)

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🐛

Sentry

Error tracking & triage

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📊

Grafana

Metrics & dashboards

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💬

Slack

Incident comms & ops channel

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What this stack lets you do

Container deploy on push

See recipe
  1. 1Push to main — GitHub event
  2. 2Docker MCP builds + tags image
  3. 3Image pushed to ECR via AWS MCP
  4. 4ECS service updated with new task definition
  5. 5Slack notification posted

3am incident response

See recipe
  1. 1PagerDuty fires — 'API latency spiked'
  2. 2Claude pulls Grafana for the window + top-10 slow endpoints
  3. 3Sentry checked for new errors
  4. 4Docker MCP inspects container CPU on affected hosts
  5. 5Postgres MCP queries for runaway SQL
  6. 6Claude identifies root cause, posts status to #incidents in Slack
  7. 7Linear ticket created for tomorrow's fix

Cost optimization sweep

  1. 1Claude queries AWS MCP for last-30-day spend by service
  2. 2Flags top anomalies (idle RDS, oversized EC2, unused EBS)
  3. 3Cross-references Grafana for utilization metrics
  4. 4Drafts optimization proposal in Notion + Linear tickets
  5. 5Slack summary posted to ops channel

Estimated value

Typical DevOps team of 3: ~$400/mo saved on tooling (Datadog add-ons, Runbook.io, operator seats). More importantly: 6–10h/week per engineer reclaimed, 50% reduction in incident mean-time-to-mitigation.

Frequently asked questions

Is it safe to give Claude write access to AWS?

Scope the IAM role — read-only for most work, with a separate role for scoped write (e.g., only Lambda updates, no IAM changes). Most teams start read-only for a week, then widen carefully. Treat the MCP like a junior engineer.

Can Claude actually run kubectl / terraform apply?

Yes (via Kubernetes MCP / Terraform MCP). Most teams gate writes behind a confirmation or restrict to non-prod. For prod, GitOps is safer: Claude opens a PR to the manifest repo, humans merge.

Does this replace Datadog or Sentry?

No — it complements them. Datadog/Sentry/Grafana stay as your source of truth. The MCP layer makes them conversational, so queries are instant instead of 5 clicks.

What about Kubernetes — should I add a Kubernetes MCP?

Yes if you run K8s. Community Kubernetes MCP covers pod inspection, logs, rolling restarts, and kubectl parity. Add it to this stack if you're on K8s — it's the 7th MCP for K8s shops.

Can I use this stack for on-call rotation?

Add PagerDuty MCP (community) for rotation management + incident ack. The 6 above + PagerDuty give you a complete on-call toolkit, all callable from Claude when you're half-asleep at 3am.

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Install this stack

$ mcpizy install github docker aws sentry grafana slack

🐙GitHub🐳Docker☁️AWS🐛Sentry
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