docker-mcp
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- AlicenseNot gradedqualityNot gradedmaintenanceEnables AI assistants to interact with Docker containers through safe, permission-controlled access to inspect, manage, and diagnose containers, images, and compose services with built-in timeouts and AI-powered analysis.-
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to manage Docker containers, images, volumes, networks, and Compose projects through natural language, with secret masking and destructive action grants.9Apache 2.0
- AlicenseAqualityDmaintenanceEnables agentic Docker container operations including file read/write, command execution, and interactive TTY sessions via the local Docker binary.1912 npmMIT
- AlicenseAqualityBmaintenanceEnables AI assistants to manage Docker containers and Compose stacks through natural language, including lifecycle operations, logs, stats, and secure remote access.218 npm3MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to inspect and manage Docker environments across one or multiple hosts, including listing containers, retrieving logs and stats, and starting, stopping, restarting, or removing containers, with read-only and secret-redaction safeguards.423 npmMIT
- FlicenseNot gradedqualityDmaintenanceEnables AI assistants to manage Docker containers and images, including starting, stopping, listing, and building operations.2-
TDQS
Scored across 47 tools
Most tools target distinct resources and actions (containers, images, volumes, networks, compose, labs), and descriptions clarify boundaries. However, the lab_* group closely mirrors compose_* operations (lab_up vs compose_up, lab_exec vs compose_exec, lab_logs vs compose_logs), and bulk cleanup tools like prune overlap conceptually with individual remove_* tools, which could cause occasional misselection.
The dominant pattern is verb_noun snake_case (list_containers, remove_network, create_volume), which is applied consistently across most tool families. Deviations include resource-first names like container_logs, container_stats, and container_processes, plus compose_ps instead of a list-style verb, but the overall scheme remains predictable.
With 47 tools, the surface is heavy for a Docker management server and exceeds the typical well-scoped range. Many operations are naturally grouped (container lifecycle, image lifecycle, compose, labs), and some could be consolidated without losing capability.
Coverage is broad: complete container lifecycle (run, start, stop, restart, remove, exec, copy, logs, stats, processes, inspect), image operations, volume and network CRUD, compose lifecycle, and lab workflows. Minor gaps exist (e.g., container pause/unpause, rename/update, image save/load, network connect/disconnect), but core workflows are well supported.