Best MCP Servers for DevOps Engineers in 2026

DevOps engineering is defined by breadth. On any given day you might be debugging a failing container, writing a Kubernetes deployment manifest, configuring CDN cache rules, reviewing a CI pipeline, and investigating a production incident. Each of these tasks involves a different tool with a different interface, and the constant switching between terminals, dashboards, and documentation is where hours disappear.

Model Context Protocol (MCP) servers help by bringing these disparate systems into a single interface: your AI coding assistant. Instead of having six browser tabs open -- one for Kubernetes dashboards, one for Cloudflare, one for GitHub Actions, one for Sentry -- the assistant queries each system directly and works with the data in context.

This guide covers five MCP servers that address the core DevOps workflow: container management, cluster orchestration, edge infrastructure, CI/CD and code collaboration, and production monitoring.

Docker MCP -- Container Lifecycle Management

Author: Community | Tools: 14 | Setup: Zero-config (npx)

Docker is the foundation of modern DevOps. Every service you deploy, every test you run, every local development environment you maintain -- it all starts with containers. Docker MCP gives your assistant full control over the Docker daemon, letting it manage the entire container lifecycle without you typing a single docker command.

What It Does

The server exposes fourteen tools that cover container, image, volume, and network management. Your assistant can list, start, stop, and remove containers. It can pull and build images, inspect layer details, and manage tags. It handles volumes for persistent storage and networks for container communication. It can read container logs, inspect runtime configuration, and execute commands inside running containers.

What makes this particularly useful for DevOps work is the assistant's ability to correlate information across multiple containers. It can check the logs of one container, compare them with the configuration of another, inspect the network that connects them, and diagnose the issue -- all in one step where you would normally run five or six commands sequentially.

How It Helps in Practice

Your staging environment has a multi-container setup: an API service, a worker, a database, and a Redis cache. The worker container keeps restarting. Instead of running docker ps to check the status, docker logs worker to see the error, docker inspect worker to check the environment variables, and docker network inspect staging to verify connectivity, you ask the assistant to diagnose the problem. It checks all four pieces of information, correlates them, and tells you that the worker is failing because an environment variable referencing the Redis hostname was changed in the last update but the worker container was not rebuilt with the new value.

For Dockerfile optimization, the assistant can analyze your current Dockerfile, compare the image size and layer count with best practices, identify unnecessary dependencies, suggest multi-stage build improvements, and rebuild the image to verify the optimization. It can even compare the before and after image sizes to quantify the improvement.

Docker Compose workflows benefit too. The assistant can read your docker-compose.yml, spin up the stack, verify all services are healthy, run integration tests against the running stack, and tear it down when finished.

Configuration

{
  "mcpServers": {
    "docker": {
      "command": "npx",
      "args": ["-y", "docker-mcp"]
    }
  }
}

No API keys. The server communicates with the local Docker daemon via the Docker socket.

Kubernetes MCP -- Cluster Management Without kubectl Gymnastics

Author: Community | Tools: 12 | Setup: Zero-config (npx)

Kubernetes is powerful but verbose. A simple debugging session might require kubectl get pods, kubectl describe pod, kubectl logs, kubectl get events, and kubectl get svc -- just to understand why a deployment is not healthy. Kubernetes MCP gives your assistant direct access to your cluster, letting it perform these operations and reason about the results in the context of your infrastructure.

What It Does

The server provides twelve tools that map to the most common kubectl operations. Your assistant can list and inspect pods, deployments, services, and other resources. It can read pod logs, check events, describe resource details, apply manifests, and delete resources. It uses your existing kubeconfig, so it has the same access you would have from the command line.

The advantage over running kubectl manually is not just convenience -- it is context. When the assistant inspects a pod, it can simultaneously check the deployment configuration, the service endpoints, the recent events, and the pod logs. It builds a complete picture of the resource state rather than giving you isolated pieces of information.

How It Helps in Practice

A deployment is stuck in a rollout. Pods are in CrashLoopBackOff. Instead of running through the debugging checklist manually -- check pod status, read logs, describe the pod for events, check the deployment's rollout history, inspect the configmap -- you ask the assistant to diagnose the failed deployment. It gathers all the relevant information, identifies that the new image version references a config key that does not exist in the configmap, and suggests either updating the configmap or reverting the deployment.

Writing Kubernetes manifests is another area where the assistant adds real value. You describe what you need -- "a deployment with three replicas, a horizontal pod autoscaler scaling between 3 and 10 based on CPU, a service, and an ingress with TLS" -- and the assistant generates the YAML, applies it to the cluster, and verifies that everything comes up healthy. When a manifest has an error, it reads the Kubernetes events to identify the issue and corrects it.

For incident response, the assistant can quickly survey the cluster state: which pods are unhealthy, which deployments have recent changes, which services have endpoint mismatches, and which events indicate problems. This triage step, which normally takes five minutes of kubectl commands, happens in seconds.

Configuration

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "mcp-server-kubernetes"]
    }
  }
}

No API keys. The server uses your local kubeconfig (~/.kube/config). Make sure your context is set to the cluster you want to manage.

Cloudflare MCP -- Edge Infrastructure Management

Author: Cloudflare | Tools: 18 | Requires: Cloudflare API token and account ID

Cloudflare sits in front of most production applications, handling DNS, CDN caching, DDoS protection, and increasingly, edge compute via Workers. Managing this infrastructure typically means logging into the Cloudflare dashboard or using the CLI. Cloudflare MCP brings all of this into your editor, letting your assistant manage Workers, KV storage, R2 object storage, D1 databases, and DNS records directly.

What It Does

With eighteen tools, this is one of the most comprehensive MCP servers available. Your assistant can deploy and update Cloudflare Workers, manage KV namespaces and key-value pairs, interact with R2 buckets and objects, query D1 databases, and manage DNS records. It covers the full Cloudflare platform, from edge compute to storage to networking.

For DevOps engineers, the Workers and DNS management capabilities are the most immediately valuable. Deploying a Worker, updating its environment variables, and verifying it is serving correctly -- that entire workflow happens within your editor.

How It Helps in Practice

You need to deploy a new edge function that handles rate limiting at the CDN level. Instead of switching to the Cloudflare dashboard, creating a Worker, writing the code, configuring routes, and testing it, you describe the rate-limiting logic to your assistant. It writes the Worker code, deploys it via the MCP server, sets up the route binding, and verifies it is responding correctly. If the function needs to store rate counters, it creates a KV namespace and wires it up -- all in the same conversation.

DNS management is another frequent task. Adding a new subdomain, updating a CNAME for a service migration, or verifying that DNS propagation is correct -- the assistant handles all of this through the API. For DNS changes that affect production traffic, having the assistant double-check the configuration before applying it adds a valuable safety layer.

For teams using Cloudflare's data platform (D1 for SQL, R2 for objects, KV for key-value), the MCP server provides a unified interface. The assistant can create a D1 database, run the schema migration, seed it with data, and deploy the Worker that queries it -- a complete edge application deployment in one session.

Configuration

{
  "mcpServers": {
    "cloudflare": {
      "command": "npx",
      "args": ["-y", "@cloudflare/mcp-server-cloudflare"],
      "env": {
        "CLOUDFLARE_API_TOKEN": "your-cloudflare-token",
        "CLOUDFLARE_ACCOUNT_ID": "your-account-id"
      }
    }
  }
}

Generate an API token from the Cloudflare dashboard with the appropriate permissions for the resources you want to manage.

GitHub MCP -- CI/CD Pipelines and Infrastructure as Code

Author: Anthropic | Tools: 20 | Requires: GitHub personal access token

For DevOps engineers, GitHub is not just where the code lives -- it is where the CI/CD pipelines run, where the infrastructure-as-code changes are reviewed, and where the deployment automation is triggered. GitHub MCP connects your assistant to the GitHub API with twenty tools covering repositories, pull requests, issues, workflows, branches, and code search.

What It Does

The assistant can read and create pull requests, review diffs, check workflow run statuses, trigger workflows, manage branches, search code across repositories, and manage issues. For DevOps specifically, the workflow management and code search capabilities are the most valuable. The assistant can check whether a CI pipeline passed, read the failure logs, identify the issue, and suggest a fix -- without you opening the GitHub Actions tab.

How It Helps in Practice

A CI pipeline fails on a pull request that updates Terraform configurations. Instead of opening GitHub, navigating to the PR, clicking through to the failed workflow run, reading the logs, and trying to identify the error in a wall of Terraform plan output, you ask the assistant to check the PR status. It reads the workflow logs, identifies that the Terraform plan failed because a new resource references a module that was not initialized, and suggests adding the required terraform init step or fixing the module reference.

For infrastructure-as-code reviews, the assistant can read the PR diff, check the Terraform or Kubernetes manifests for common issues (missing resource limits, overly permissive security groups, hardcoded values that should be variables), and provide a structured review. It can even compare the proposed changes against the current live state if you have the relevant MCP servers configured.

Release management is another strong use case. The assistant can create release branches, generate changelogs from commit history, create GitHub releases with proper tags, and trigger deployment workflows -- the full release cycle managed from your editor.

Configuration

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-github-token"
      }
    }
  }
}

Create a personal access token with repo, workflow, and admin:org permissions as needed for your use case.

Sentry MCP -- Production Monitoring Integrated Into Incident Response

Author: Sentry | Tools: 8 | Requires: Sentry auth token

When an incident happens, the first thing a DevOps engineer does is check the monitoring. What errors are spiking? Which services are affected? When did it start? How many users are impacted? Sentry MCP brings this data directly into your editor, so you can investigate and respond to incidents without switching between your code and the Sentry dashboard.

What It Does

The server provides eight tools for querying Sentry's error tracking and performance monitoring. Your assistant can list recent issues, read detailed stack traces, query error frequency and trends, check affected user counts, and access performance transaction data. It sees the same metrics and traces available in the Sentry UI, but in a format that it can reason about alongside your infrastructure code and configurations.

How It Helps in Practice

You are on-call and get paged for a spike in 500 errors. Instead of opening Sentry, finding the error group, reading through the events, and then switching to your infrastructure tooling to investigate, you ask your assistant to check Sentry. It finds the error spike, reads the stack traces, identifies that the errors started at 14:32 UTC, and correlates that with a deployment that went out at 14:30. It then checks the GitHub PR for that deployment via the GitHub MCP server, reads the changes, and identifies the root cause: a misconfigured environment variable in the new container image.

The performance monitoring integration is equally useful for capacity planning. The assistant can query transaction latency trends, identify services where P99 latency has been creeping up, and correlate that with infrastructure changes. If a Kubernetes deployment was recently scaled down, or a CDN cache rule was changed, the assistant can connect those changes to the performance degradation.

Post-incident reviews also benefit. The assistant can pull the timeline of errors, correlate them with deployments and infrastructure changes, and draft an incident report with a clear sequence of events, root cause analysis, and action items.

Configuration

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": ["-y", "sentry-mcp"],
      "env": {
        "SENTRY_AUTH_TOKEN": "your-sentry-token"
      }
    }
  }
}

Create an auth token from your Sentry account with appropriate project access.

The DevOps Stack -- Combining Everything

These five servers cover the operational surface area that DevOps engineers manage daily. Together, they create an integrated incident-response and infrastructure-management workflow:

1. Containers: Docker MCP manages the build, deployment, and debugging of containerized services.
2. Orchestration: Kubernetes MCP provides cluster visibility and resource management.
3. Edge: Cloudflare MCP handles CDN configuration, DNS, and edge compute deployment.
4. CI/CD: GitHub MCP manages pipelines, code reviews, and releases.
5. Monitoring: Sentry MCP feeds production health data into every conversation.

Here is what an integrated incident response looks like. Sentry MCP alerts you to a spike in errors on your API. The assistant reads the stack trace and identifies the affected service. It checks the Kubernetes cluster via Kubernetes MCP and finds the deployment was recently updated. It reads the deployment PR via GitHub MCP and identifies the configuration change. It checks the Docker container via Docker MCP and confirms the new image has the issue. It deploys a Cloudflare Worker via Cloudflare MCP to serve a maintenance page while you roll back. The rollback happens through Kubernetes MCP, and the assistant verifies the error rate drops back to normal via Sentry MCP.

Six systems, one conversation, fifteen minutes instead of an hour.

Getting Started

Start with the servers that match your daily operational focus:

• Managing containers and Docker Compose setups? Docker MCP eliminates the container debugging dance.
• Running Kubernetes clusters? Kubernetes MCP replaces long kubectl sessions with contextual diagnostics.
• Using Cloudflare for CDN, DNS, or edge compute? Cloudflare MCP consolidates all Cloudflare management.
• Reviewing infrastructure PRs and CI pipelines? GitHub MCP streamlines the review-merge-deploy cycle.
• On-call and dealing with production incidents? Sentry MCP brings monitoring data into your investigation workflow.

The total token overhead for the full stack is approximately 36,900 tokens. This is the heaviest stack in this article series because DevOps servers tend to expose many tools (Cloudflare alone has 18). If token budget is a concern, start with Docker, Kubernetes, and Sentry -- those three cover the most critical operational workflows at around 19,500 tokens.

For a pre-configured setup with all five servers, check out the DevOps & Cloud Stack on stackmcp.dev. It includes ready-to-paste configurations for Claude Code, Cursor, Windsurf, and other supported clients.