Guides7 min read

Best MCP Servers for Docker & Container Development in 2026

The top MCP servers for Docker, Kubernetes, and container workflows. Manage containers, inspect images, query clusters, and automate deployments through AI.

By MyMCPTools Team·

Container development involves a lot of context-switching: checking container status, reading logs, inspecting images, managing Kubernetes deployments. MCP servers collapse this into your AI conversation — letting Claude manage your containers the way it manages your files.

Here are the best MCP servers for Docker and container workflows.

1. Docker MCP Server — The Essential Starting Point

The Docker MCP server wraps the Docker Engine API, giving your AI assistant direct control over your local Docker environment.

Key capabilities:

  • List running and stopped containers
  • Start, stop, restart, and remove containers
  • Inspect container configuration, environment variables, and resource usage
  • Stream container logs in real-time
  • Build images from Dockerfiles
  • List, pull, and inspect images
  • Manage Docker networks and volumes

Typical prompt: "Which containers are currently using more than 500MB of memory? Stop any that have been running for over 7 days and aren't in production."

Installation:

npx docker-mcp-server

2. Kubernetes MCP Server — Cluster Operations via AI

For teams running Kubernetes, the K8s MCP server connects your AI to kubectl — making cluster exploration, debugging, and management conversational.

Key capabilities:

  • List and describe pods, deployments, services, and namespaces
  • Read pod logs (including multi-container pods)
  • Check resource quotas and limits
  • Inspect ConfigMaps and Secrets (with RBAC)
  • Check deployment rollout status
  • Scale deployments
  • Port-forward to services

Typical prompt: "My API deployment is in a CrashLoopBackOff. Fetch logs from the failing pods and tell me what's causing the crash."

npx @flux159/mcp-server-kubernetes

3. Docker Compose MCP Server — Multi-Service Management

The Docker Compose MCP server understands your docker-compose.yml configuration and lets your AI manage your entire service stack.

Key capabilities:

  • Read and explain docker-compose service configuration
  • Start, stop, and restart individual services or the full stack
  • View logs across all services simultaneously
  • Inspect service dependencies and networking
  • Scale services up or down

Best for: Local development environments with multiple services (web + database + cache + queue). Instead of remembering 15 docker-compose commands, just describe what you want.

4. Terraform MCP Server — Infrastructure as Code

The Terraform MCP server lets your AI read, plan, and explain your infrastructure code. Useful for understanding complex Terraform configurations and debugging plan outputs.

Key capabilities:

  • Read and explain Terraform configurations
  • Run terraform plan and interpret the output
  • Check state files for drift
  • Validate configurations
  • Generate Terraform modules from descriptions

Typical prompt: "Read my terraform/main.tf. What resources will be created when I apply? Flag any configurations that differ from AWS best practices."

5. Prometheus MCP Server — Metrics and Alerting

If you run Prometheus for container/cluster monitoring, the Prometheus MCP server gives your AI access to your metrics data.

Key capabilities:

  • Execute PromQL queries against your Prometheus instance
  • Inspect alert rules and their firing status
  • Browse available metrics and their labels
  • Analyze metric trends over time

Typical prompt: "Check CPU and memory usage for all containers in the production namespace over the last hour. Which services are most resource-intensive?"

Docker MCP Security Considerations

Docker MCP servers have significant power — they can stop and remove containers, which is destructive. Best practices:

  • Use read-only mode during exploration: Many Docker MCP servers support a read-only flag. Use it when you only need to inspect, not modify.
  • Scope Kubernetes RBAC tightly: Create a dedicated ServiceAccount with only the permissions your AI needs. Don't use cluster-admin for AI tooling.
  • Review before applying: For Terraform, use plan mode first and review the output before applying.
  • Avoid production write access: Give AI assistants full write access to development environments, limited access to staging, and read-only access to production.

Recommended Setup for Container Developers

Start with Docker + Filesystem as your foundation:

{
  "mcpServers": {
    "docker": {
      "command": "npx",
      "args": ["docker-mcp-server"]
    },
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "~/projects"]
    }
  }
}

Add Kubernetes when you're ready to expand to cluster management.

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🔧 MCP Servers Mentioned in This Article

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Docker MCP Server

The Docker MCP server connects your AI assistant directly to your local or remote Docker daemon, exposing container lifecycle management and image orchestration as Model Context Protocol tools. With this integration, developers can prompt Claude, Cursor, or Windsurf to inspect running containers, view real-time logs, build new images from Dockerfiles, start and stop services using Docker Compose, and prune unused system resources through natural language. Rather than switching to a terminal to type complex docker inspect commands, you can simply ask your AI to "find out why the postgres container keeps crashing" or "tail the last 100 lines of the frontend container logs and find the React error". This is a game-changer for DevOps engineers, backend developers, and system administrators who want to streamline container debugging, automate compose cluster orchestration, and troubleshoot networking issues faster. The server interacts securely with the Docker Engine API, meaning it can both read system state and execute commands like port binding or volume inspection. It works cross-platform wherever Docker Desktop or the Docker daemon is running. Docker's official implementation ships as the Docker MCP Gateway (docker/mcp-gateway), a `docker mcp` CLI plugin that acts as a single secure gateway in front of many containerized MCP servers from the Docker MCP Catalog — each downstream server runs in its own isolated container with resource limits and secret injection, so an assistant connects once to the gateway instead of wiring up dozens of individual servers. Start it with `docker mcp gateway run`, then point Claude Desktop, Cursor, or another client at the gateway; `docker mcp server enable <name>` toggles which catalog servers (including the Docker/container-management tools) are exposed. This container-per-server isolation is the key security benefit over running MCP servers directly on the host.

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Kubernetes MCP Server

The Kubernetes MCP server (mcp-server-kubernetes, built by Flux159) brings cluster management capabilities into AI assistant workflows, letting developers and platform engineers query and manage Kubernetes resources through natural-language interactions with Claude, Cursor, and other MCP-compatible clients. It loads your existing kubeconfig automatically, so it works with any cluster — local minikube and kind setups, Amazon EKS, Google GKE, Azure AKS, or on-premises deployments — with no separate credential setup required. Core tools exposed by the server include: listing pods, deployments, services, and namespaces; describing individual resources and their status; fetching pod logs for debugging; applying and updating manifests; scaling deployments; checking rollout status and history; and querying resource utilization and cluster events. A built-in non-destructive mode can disable delete/scale-down operations entirely, making it safe to point at production clusters for read-only diagnostics. DevOps engineers use it to debug failing deployments by asking Claude to inspect pod logs and recent events, identify resource constraints causing OOMKilled pods, or summarize the current state of a namespace before a production release. For SREs responding to incidents, it enables rapid triage through conversational commands — no memorizing kubectl flags or switching terminal windows mid-incident — and optional OpenTelemetry integration adds observability into what the AI agent actually did against the cluster. Install with: `npx mcp-server-kubernetes`. Pairs well with the GitHub MCP server for full GitOps review workflows.

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Terraform MCP Server

The Terraform MCP Server is HashiCorp's official integration that brings Terraform's infrastructure-as-code capabilities into AI assistants via the Model Context Protocol. It connects Claude Desktop, Cursor, VS Code, and other MCP clients to the Terraform ecosystem — letting you explore providers, look up module schemas, validate configurations, and work with HCP Terraform (Terraform Cloud) all through natural-language conversation. Core tools include: search the Terraform Registry for modules and providers by keyword, retrieve full provider schema documentation including resource arguments and attribute types, look up specific module input/output variables and their defaults, resolve provider version constraints and compatibility matrices, and run Terraform operations against HCP Terraform workspaces including plan, apply, and state inspection. A key use case is AI-assisted IaC authoring: ask Claude to "generate a Terraform module for an AWS VPC with public and private subnets using the latest aws provider schema" and the server fetches the live provider schema to ensure accurate attribute names and types rather than hallucinating outdated syntax. For HCP Terraform users, workspace integration supports listing workspaces, triggering runs, and checking plan output. HashiCorp maintains the server at hashicorp/terraform-mcp-server and distributes it as a pre-built binary for Linux, macOS (arm64 + amd64), and Windows. Install via: `npx @hashicorp/terraform-mcp-server`. Pairs well with GitHub MCP for full IaC PR review workflows.

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Docker Compose

Manage multi-container Docker applications with Compose MCP. Start, stop, and scale services, inspect container logs, and manage service dependencies.

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Kubernetes Dashboard

Advanced Kubernetes cluster management MCP. Manage namespaces, deployments, services, ingresses, and custom resources beyond what the basic kubectl server offers.

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