Power your agent
คำสั่งของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
uvx docker-mcp-server==2.3.0Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
claude mcp add io.github.l337-org-docker-mcp-server -- uvx docker-mcp-server==2.3.0รันในเทอร์มินัล
{ "mcpServers": { "io.github.l337-org-docker-mcp-server": { "command": "uvx", "args": [ "docker-mcp-server==2.3.0" ] } }}claude_desktop_config.json
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รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
More than just a fully featured MCP server that lets AI agents manage Docker - containers, images, networks, volumes, swarm services, secrets, configs, nodes, plugins, etc., it helps you create workflows to easily manage your Docker environments.
Manage Docker (containers, images, Compose, Swarm, registries) via the Docker SDK and CLI.
It gives you much more control and flexibility than calling the Docker CLI directly: each operation is exposed as its own typed tool, marked read-only or not, with destructive actions separately flagged. This means a client can auto-approve reads while always confirming anything destructive, and the whole server can also be switched into a read-only or no-destructive mode as a blanket safeguard. Output is bounded rather than left to grow unboundedly - capped with a truncated flag instead of silently overflowing the agent's context.
For simple cases, you can just install and go with no configuration required - once loaded it will discover your local Docker socket and expose the full command surface to your AI agent. For more advanced users it can manage multiple Docker daemons, e.g. both your local dev environment and also a remote production environment over TCP, TLS or SSH in a single session. It can also be configured to mark some daemons as read-only, so you can monitor them without the risk of making accidental changes.
It can even be run on a machine without Docker installed and manage remote daemons over SSH, TLS or TCP (some features require SSH). The AI itself does not require shell or SSH access.
The MCP server also exposes things like logs and stats as resources so that you can monitor and triage, enabling you to answer questions like 'why did my container crash?', 'what is the state of my swarm?', 'am I suffering memory pressure?', 'what is the disk usage of my volumes?', 'what differences are there between my test and production systems?', and more...
Documentation is built for the agent, not just the person configuring it: an MCP resource exposes the Docker SDK reference in-session (with a tool-callable fallback for clients that can't read resources), and a live tool-catalog resource reports exactly what's registered under the current configuration. Each tool's own description names its nearest siblings and when to prefer each, states preconditions and side effects in plain language, and is honest about when it can still fail - so an agent can pick the right tool on the first try among 150+ options, not guess.
docker-mcp-server is optimized to work efficiently with the new generation of MCP clients that support lazy tool loading. For clients that still eagerly load all tools, the server can optionally be configured to exclude tools from a subset of domains (e.g. exclude 'swarm' and 'scout' tools) to reduce the tool list size. It's also possible to put the MCP server into 'read-only' or 'no-destructive' modes that prevent any tools with write or destructive capabilities from being registered, which again reduces the footprint.
The server runs entirely on your machine, either natively, as an mcpb bundle, as a Claude plugin, or containerized, and sends no telemetry. You are entirely in control - see the Privacy Policy.
Note: If you're using the containerized MCP server or MCPB bundle, the Python and uv requirements are taken care of for you.
DOCKER_HOST / unix socket conventions apply)uvx/pip, the Claude plugin, or the
.mcpb bundle, all of which resolve dependencies locally) requires Rust and OpenSSL 3.x, because
cryptography - a transitive dependency, via mcp -> pyjwt[crypto] - has shipped no x86_64
macOS wheel since version 49.0.0 and must be built from source there. If you'd rather not install
a build toolchain, use the container image instead - it runs the same
prebuilt Linux binary regardless of your Mac's CPU architecture, so this doesn't apply to it. See
Security considerations for more.The server is published to PyPI as docker-mcp-server. Add an entry to your AI tool's MCP configuration (commonly mcp.json or the equivalent in your client) pointing uvx at it - uv will fetch and cache the package on first use:
{
"mcpServers": {
"docker-mcp-server": {
"command": "uvx",
"args": ["docker-mcp-server"],
"env": {}
}
}
}
To pin a specific version, append ==<version> to the package name (e.g. docker-mcp-server==1.5.0). If you'd rather install it onto your PATH, pipx install docker-mcp-server gives you the docker-mcp-server console script (a docker-mcp alias is also installed).
Installing from git instead. To run an unreleased revision straight from this repository:
{
"mcpServers": {
"docker-mcp-server": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/L337-org/docker-mcp.git",
"docker-mcp-server"
],
"env": {}
}
}
}
To pin a specific revision, append @<tag-or-commit> to the git URL.
For Claude Desktop, a one-click bundle is attached to each
GitHub Release as
docker-mcp-server-<version>.mcpb (with a matching .sha256). Download it and drag it into
Settings > Extensions, or use Settings > Extensions > Advanced settings > Install extension...
and pick the file. The install dialog surfaces a Docker host(s) field and the read-only /
no-destructive / disabled-domain switches, so no manual JSON editing is needed.
It's a uv-type bundle: Claude Desktop's managed
uv resolves the dependencies and runs the server, so the only host prerequisite is Docker itself -
no separate Python, uv, or git. Leave the Docker host(s) field blank to use your default
Docker context; set one endpoint (ssh://user@host) for a remote daemon, or list several (see
Managing several daemons).
The claude-plugin/ folder packages the server as a plugin for Claude Code and
Cowork, for Anthropic's plugin directory. It runs uvx docker-mcp-server==<version>, pinned to
the release the plugin was built from, so it needs uv on the host.
It ships a lockfile pinning every dependency by hash, which the directory describes its locked
launch as installing exactly; loaded any other way, uvx resolves the dependencies at install time.
It offers the same settings as the .mcpb install dialog (Docker host(s), read-only,
no-destructive and disabled domains), set in the plugin's settings in Claude Code (/plugin). The
release pipeline updates the plugin the directory serves only after a release has been published
to PyPI and the plugin has been installed and tested from it.
Running the server as a container removes the Python / uv / git prerequisites entirely - the only
thing the host needs is Docker, which you already have. Prebuilt multi-arch images (linux/amd64 +
linux/arm64) are published on each release to Docker Hub (gavinlucas/docker-mcp-server) and
GHCR (ghcr.io/l337-org/docker-mcp-server) - the two are identical. Point your MCP client at
docker run:
{
"mcpServers": {
"docker-mcp-server": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"-v", "/var/run/docker.sock:/var/run/docker.sock",
"gavinlucas/docker-mcp-server:latest"
],
"env": {}
}
}
}
-i is required (the server speaks MCP over stdio); --rm cleans up when the client disconnects. To
pin a version, replace :latest with a release tag (e.g. :1.5.1). To pull from GHCR instead, use
ghcr.io/l337-org/docker-mcp-server:latest.
Image renamed. As of 1.5.0 the image is published as
docker-mcp-server(matching the PyPI name). The oldghcr.io/gavinlucas/docker-mcpimage is frozen at 1.4.0 and no longer updated - point new pulls atghcr.io/l337-org/docker-mcp-server.
Image variants. Two variants are published to both registries (gavinlucas/docker-mcp-server on
Docker Hub and ghcr.io/l337-org/docker-mcp-server on GHCR), both built from one Dockerfile. The
CLI-backed domains (Compose, Stack, Buildx, Scout, Context) shell out to the docker CLI and its
plugins; in Stack, only stack_deploy does.
Scout's plugin binary alone accounts for the ~195 MB jump from no-scout to full. The no-scout
image also defaults DOCKER_MCP_SERVER_DISABLE=scout, so the scout tools don't register - the agent is
never offered tools whose CLI plugin isn't present (it sees a smaller, fully-working tool list rather
than scout tools that error on every call). Override at runtime with -e DOCKER_MCP_SERVER_DISABLE=... if you
ever need to change the disabled set (note it replaces, not appends).
Building it yourself. All variants build from the repo's Dockerfile via build args:
docker build -t docker-mcp-server:full . # full (default)
docker build --build-arg INSTALL_SCOUT=0 --build-arg DISABLE_DOMAINS=scout \
-t docker-mcp-server:no-scout . # no-scout
docker build --build-arg INSTALL_CLI=0 -t docker-mcp-server:lite . # lite (SDK-only, ~165 MB)
The lite image (docker-py SDK tools only - Compose/Buildx/Scout/Context degrade to "plugin
unavailable") is buildable but not published.
Reaching the daemon from inside the container. The image defaults DOCKER_HOST to
unix:///var/run/docker.sock, so mounting your host's socket onto that path is all that's needed.
Where the host socket is, however, varies - and the server prints a platform-aware hint to stderr
if it can't connect at startup:
-v /var/run/docker.sock:/var/run/docker.sock (rootless: -v $XDG_RUNTIME_DIR/docker.sock:/var/run/docker.sock).~/.docker/run/docker.sock - mount it onto the in-container path: -v $HOME/.docker/run/docker.sock:/var/run/docker.sock (or enable Settings > Advanced > Allow the default Docker socket and use /var/run/docker.sock).-e DOCKER_HOST=tcp://host.docker.internal:2375 (enable the TCP endpoint in Docker Desktop). That endpoint is unauthenticated and unencrypted - keep it bound to localhost, disable it when you're not using it, and use TLS or DOCKER_HOST=ssh://... for any remote daemon.-e DOCKER_HOST=... (plus the TLS vars below) - see Talking to a remote daemon.Host filesystem access. Inside a container, the file-path tools (image_save / container_export
with dest_path, image_load / container_archive_put with from_file, container_archive_get_to_file,
and compose project_dir / files) resolve paths inside the container,
not on your host. Bind-mount any directory you want to exchange files through - using the same
path inside and out keeps host and container paths identical:
-v $HOME/docker-work:$HOME/docker-work
If you call one of these tools with a path that isn't on a bind mount, the server refuses up front
with a message telling you exactly which -v to add - a write to an unmapped path would otherwise be
silently discarded when the container exits. (The in-band byte tools, capped at 32 MiB, need no
mount.) Configuration env vars (DOCKER_MCP_SERVER_READONLY, DOCKER_HOST, etc.) go in the client's env
block exactly as for the uvx install.
When DOCKER_HOST is set the server uses it directly (via docker.from_env(), so DOCKER_TLS_VERIFY / DOCKER_CERT_PATH are honoured too). Common overrides via env:
"env": {
"DOCKER_HOST": "tcp://remote-host:2375",
"DOCKER_TLS_VERIFY": "1",
"DOCKER_CERT_PATH": "/path/to/certs"
}
Default daemon (no DOCKER_HOST). With DOCKER_HOST unset, the server resolves the daemon the way the docker CLI does, rather than assuming /var/run/docker.sock: it follows the active Docker context (DOCKER_CONTEXT, else currentContext from ~/.docker/config.json, reading the endpoint from that context's meta.json), and if that yields nothing it probes the well-known socket locations (~/.docker/run/docker.sock for Docker Desktop 4.13+, $XDG_RUNTIME_DIR/docker.sock for rootless, then /var/run/docker.sock). This matters because docker.from_env() alone ignores contexts and would fall back to /var/run/docker.sock - which Docker Desktop 4.13+ no longer creates by default (it uses the desktop-linux context unless you enable Settings > Advanced > Allow the default Docker socket), so a stock Desktop install reachable by your CLI would otherwise fail here. Precedence: a non-empty DOCKER_HOST always wins and goes straight through docker.from_env() (which ignores contexts); DOCKER_CONTEXT / currentContext is consulted only when DOCKER_HOST is unset, and the socket probe only when neither resolves. TLS material attached to a remote context is not applied automatically - a tcp:// + TLS context still needs DOCKER_HOST / DOCKER_CERT_PATH.
DOCKER_HOST=ssh://user@remote-host is supported via a pure-Python transport (paramiko, pulled in by the docker[ssh] dependency) - there is no system ssh binary requirement, so it works the same on the host install and inside the container images. It authenticates with your normal SSH setup:
ssh-add) and make sure SSH_AUTH_SOCK is set in the server's environment (or place the key at the default ~/.ssh/id_* path).~/.ssh/known_hosts and rejects an unknown host. Add the host key only after verifying its fingerprint through a trusted channel - connect once interactively with ssh user@remote-host and confirm the prompt, or compare ssh-keyscan remote-host | ssh-keygen -lf - against a known-good fingerprint before appending it. Avoid blindly piping ssh-keyscan straight into known_hosts, which trusts whatever key is returned (including a MITM's).-v $HOME/.ssh:/root/.ssh:ro (key + known_hosts) - or forward your agent socket; no socket mount and no ssh package needed.CLI-backed tools (Compose, Stack, Buildx, Scout, Context) shell out to the docker CLI, which would otherwise use the system ssh binary over an ssh:// endpoint. Instead, run_docker() detects DOCKER_HOST=ssh://... and transparently starts a per-call local TCP proxy (docker_mcp/tools/_ssh_proxy.py) that opens the same paramiko connection docker-py would, runs docker system dial-stdio over it, and points the CLI subprocess at tcp://127.0.0.1:<ephemeral port> for the duration of that one call. So the CLI-backed tools authenticate with the exact same credentials and host-key policy as the docker-py-backed tools above - no system ssh binary for the direct connection, identical on the host install and inside the container images. The one exception is a ProxyCommand in ~/.ssh/config (bastion/jump-host setups): paramiko runs that command as-given, and it's commonly ssh -W %h:%p ..., so a jump-host hop still shells out to the system ssh client even though the direct connection does not.
That ephemeral 127.0.0.1 listener bridges to the remote (root-equivalent) daemon with your SSH credentials for the duration of a single CLI call, so any process sharing the same loopback could reach it during that brief window. The exposure is narrow - localhost-only and torn down when the call returns - and inside a container it's narrower still, reachable only by processes within that container's network namespace. The daemon remains the trust boundary either way (see Security considerations).
If Docker is not installed locally - or the plugin a call needs is missing - most tools will still operate on a remote host exactly as before. A few tool families (Compose, Buildx, Scout, and stack_deploy) will be run on the target host itself, over SSH. For these commands TCP and TLS connections will not work without a local Docker install.
Worth knowing:
.dockerignore) - into a private 0700 temp directory that is removed when the call returns - best-effort, since a dropped SSH connection leaves nothing able to run the cleanup. A survivor is named docker-mcp-server.stage.* and the failure is logged.~/.docker/config.json, so a private-registry compose_pull, a stack deploy --with-registry-auth, or most Scout operations may need docker login there. system_login talks to the daemon and does not write the remote CLI's config.build:, env_file:, include: all name arbitrary paths). Point tools at a project directory rather than a large parent, or the call is refused with a size-limit error (200 MiB / 50 000 entries).compose_cp relays whichever side of the copy is local, over the same SSH connection: a host source is staged like any other input, and a container result is fetched back to the real destination once the copy succeeds - the actual copy still runs through the real docker compose cp on the far host, so every parameter behaves as it does locally. The one difference: a container-to-host copy is refused if the local destination already exists, since only this host knows that.buildx_build a filesystem dest= in output/cache_to, a local src= in cache_from, or any ssh= - each would resolve on the remote machine.cmd/PowerShell sshd is refused with an explanatory error, as is a uname from MSYS/MinGW/Cygwin; a Windows dialect is architected but not implemented. A Windows sshd whose shell is wsl.exe runs commands in WSL while its SFTP subsystem stays on the Windows side, so file-copying tools are refused there - the ones that only run a command still work.context_* tools never fall back. They manage this host's CLI context registry, which a remote host knows nothing about.Everything above targets one daemon. To manage several in a single session - e.g. local dev plus a remote production daemon - set DOCKER_MCP_SERVER_HOSTS to a comma-separated list of name=endpoint pairs:
"env": {
"DOCKER_MCP_SERVER_HOSTS": "local=auto, prod=ssh://[email protected](ro)"
}
endpoint is auto (your default context/socket, as above), local (the platform-local socket, ignoring contexts), or a unix:// / tcp:// / ssh:// / npipe:// URL. ssh:// is the recommended remote transport (per-host auth via your SSH keys, no TLS cert plumbing). A tcp:// daemon over TLS takes a (tls=<dir>) marker pointing at a cert directory, e.g. prod=tcp://prod:2376(tls=/etc/docker/prod). That directory must hold ca.pem (the daemon is always verified against it - so a self-signed daemon works, you just pin its cert here); add cert.pem and key.pem only if the daemon requires a client certificate (mutual TLS). There is no unverified-TLS mode - a TLS connection always authenticates the daemon, so encryption never comes without verification.(ro) after an endpoint marks that host read-only: mutating and destructive tools refuse to act on it. This is a per-host guard enforced at call time, independent of the server-wide DOCKER_MCP_SERVER_READONLY switch - mark production (ro) and the agent can inspect it all day but can't change it, while local stays read-write.(nd) marks a host non-destructive: only destructive tools (removals, prunes, kills) refuse to act on it - reads and ordinary mutations (start/stop/create) still work. This mirrors the server-wide DOCKER_MCP_SERVER_NO_DESTRUCTIVE switch, per host. (ro) already implies it, so (ro)(nd) combines without error but is redundant.name= is shorthand for one host - DOCKER_MCP_SERVER_HOSTS=ssh://ops@prod (or auto, or blank). So this one field also covers the single-remote case. DOCKER_HOST keeps working when DOCKER_MCP_SERVER_HOSTS is unset, but DOCKER_MCP_SERVER_HOSTS takes over when set (DOCKER_HOST is then ignored, with a one-time notice to stderr).How the agent drives it. With two or more hosts, every daemon-targeting tool gains an optional host argument constrained to your configured names: read-only tools default to the first host when you omit it, while mutating and destructive tools require an explicit host (so the agent can't change the wrong daemon by accident). host_list (and the docker-mcp://hosts resource) report the configured hosts and which is the default; the container/service observability resources become host-aware - the default host's log tail is docker-logs:///{id} (note the empty authority) and a named host's is docker-logs://{host}/{id}, with the same pattern for docker-stats://, service-logs:// and service-tasks://; the single-host bare forms (docker-logs://{id}, ...) are not registered once several hosts are configured. The survey_hosts prompt sweeps every host read-only. The auto/local endpoints are resolved to concrete URLs and pinned at startup, so the SDK and CLI always agree on which daemon a name means - restart to re-resolve after changing a Docker context.
Once loaded, the agent gets MCP tools grouped by Docker domain. A few examples:
container_run, container_list (managed_only=True to list only what this server created - see Provenance labels), container_exec, container_logs, container_stop, container_commit, container_export / container_archive_get_to_file / container_archive_put (stream tar archives to/from a host path)image_build, image_pull, image_push, image_tag, image_prune (with build_cache=True, the daemon's build cache too - a separate resource from dangling images), image_save / image_load (stream image tarballs to/from a host path via dest_path / from_file), image_import (build a single-layer image from a flat rootfs tarball, URL, or existing image - docker import, not to be confused with image_load)plugin_install (pull from a registry; dry_run=True shows what host access the plugin demands before installing it - the daemon grants it non-interactively), plugin_enable / plugin_disable, plugin_configure, plugin_upgrade (with the same dry_run), plugin_list / plugin_inspect, plugin_remove (managed engine plugins - volume/network/logging drivers - not the CLI plugins that extend the docker command itself)network_create, network_connect, volume_create, volume_pruneswarm_init, swarm_join_tokens (close the init-to-join loop), swarm_update (rotate join tokens / unlock key), service_create, service_scale, service_rollback (re-apply the previous service spec), service_ps (one service's tasks, or with no service every task in the cluster, filterable by node or desired state - no single CLI command does this), service_logs (a service's logs, or one task's), node_list, node_remove, secret_create, config_createsystem_wait (the one wait: until a container exits, is healthy or logs a line, a service or stack converges, a stack is removed, a node is ready, a compose project is running, healthy or exited, or a tag lands in a registry - every wait has a timeout, 600 s by default, and reports one rather than raising), system_ping, system_info, system_version, system_df, system_events, host_list (the configured daemons and which is the default - see Managing several daemons), system_login / system_logout (cache or clear registry credentials), system_reconnect (rebuild a host's SDK client to recover a wedged connection)compose_up, compose_down, compose_stop, compose_start, compose_restart, compose_pause / compose_unpause, compose_kill, compose_ps, compose_list, compose_images, compose_top, compose_port, compose_logs, compose_config, compose_build, compose_pull, compose_run, compose_exec, compose_cp (wraps the docker compose CLI plugin)stack_deploy, stack_list, stack_ps, stack_services, stack_remove (deploy a Compose file to a swarm as a stack - stack_deploy wraps docker stack deploy, which translates the Compose file; the rest use the Engine API - requires a swarm manager)context_list, context_inspect, context_create, context_use, context_remove (wraps the docker context CLI)registry_tags, registry_manifest, registry_image_config (read an image's env/entrypoint/labels without pulling), hub_tags, hub_repo_info, hub_rate_limit (remaining pull budget) (HTTPS to OCI v2 registries and the Docker Hub API - no daemon required; transparent retry on a brief 429)buildx_build, buildx_bake, buildx_imagetools_inspect, buildx_imagetools_create, buildx_list, buildx_inspect, buildx_du, buildx_history_list / buildx_history_inspect (drill into past build records), buildx_prune, buildx_create, buildx_use, buildx_remove (wraps the docker buildx CLI plugin). Use buildx_imagetools_* in place of docker manifest - that command is in maintenance mode and lacks support for OCI image indexes and attestations.scout_cves, scout_quickview, scout_recommendations, scout_compare, scout_sbom (wraps the docker scout CLI plugin; every tool but scout_sbom needs docker login on the host running the CLI).The SDK-backed surface mirrors the Docker SDK reference - if it's documented there, it's available here. The Compose and Context surfaces follow the Compose CLI and docker context references.
The server also publishes the Docker SDK for Python reference and selected Docker CLI / registry references as MCP resources so the agent can consult them at runtime: read docker-docs://contents for the section index, then docker-docs://<section> (e.g. docker-docs://containers, docker-docs://compose, docker-docs://oci-distribution-spec, docker-docs://dockerfile, docker-docs://build-best-practices, docker-docs://engine-security, docker-docs://engine-api) for the rendered page. For MCP clients that can't read resources (e.g. Claude Desktop, Cursor), the docs_lookup tool mirrors the same content - call it with no arguments for the section index, or docs_lookup(section=...) for a page; it's always available regardless of DOCKER_MCP_SERVER_DISABLE. A further resource, docker-mcp://tool-catalog, lists every tool this server knows about with its domain, mutation category, and whether the active configuration registered it - useful for confirming the blast radius of a tool, or why one is absent from the live list. The tool_list tool mirrors it for clients that can't read resources, and adds filtering: tool_list(domain="buildx") for a one-line-per-tool briefing on an area, tool_list(category="destructive") to see what can destroy data, or tool_list(keyword="logs one task") to search names, summaries and parameter names - every tool matching any of the words comes back, those matching the most first. It lists only what the current configuration registered, and returns an explicit empty result when nothing matches. Like docs_lookup it is always available regardless of DOCKER_MCP_SERVER_DISABLE.
Container and service observability is also exposed as resources, so a client can attach live state as context without a tool call: docker-logs://<id-or-name> for a bounded tail of a container's logs (readable even after it exits - handy for diagnosing why) and docker-stats://<id-or-name> for a computed resource-usage summary (CPU %, memory, network and block I/O) of a running container. Swarm services get the same pattern - service-logs://<id-or-name> for a bounded log tail and service-tasks://<id-or-name> for a computed task/rollout summary (running vs. desired task counts, failing tasks, and the current rolling-update state). Enumerate with container_list / service_list to find the id or name to plug in. These complement the equivalent tools (container_logs/container_stats, service_logs) and the service ones are hidden when services is disabled (containers cannot be).
Many AI clients let you invoke registered MCP prompts directly (in Claude Code, type / to see them). The server ships a small library of templates in docker_mcp/tools/prompts.py that scaffold multi-step workflows - they emit a structured plan that the agent then carries out using the docker tools.
Looking things up in the SDK docs
/lookup_docker_docs section=services
/verify_docker_method method=containers.run section=containers
...or just ask in plain English:
Read
docker-docs://networksand tell me the difference betweencreateandconnect. Before changing any code, checkdocker-docs://containersand confirmrunaccepts arestart_policyargument.
Creating and managing containers
/deploy_container image=nginx:1.27 name=web
/monitor_container_fleet
/triage_incident window_minutes=30
/troubleshoot_container container=api-1
/migrate_container container=api-1 new_image=myorg/api:v2
/inspect_stack label=com.example.app=web
/clean_environment scope=stopped
/plan_compose_stack description="wordpress + mysql sharing a named volume"
Compose, contexts, and registries
/deploy_compose_project project_dir=/srv/myapp
/troubleshoot_compose_project project_dir=/srv/myapp
/deploy_swarm_stack stack_name=web compose_file=/srv/myapp/docker-stack.yml
/audit_docker_contexts
/find_latest_image_tag image=ghcr.io/org/repo
Auditing, security, and host operations
/review_dockerfile dockerfile_path=/srv/myapp/Dockerfile
/audit_container_security
/debug_container_networking source=web target=db
/investigate_disk_usage
/backup_volume volume=pgdata dest_path=/backups/pgdata.tar
/restore_volume volume=pgdata source_path=/backups/pgdata.tar
/audit_swarm_health
Buildx, Scout, and multi-arch manifests
/plan_multiarch_build image=ghcr.io/org/app:v1 platforms=linux/amd64,linux/arm64
/audit_image_cves image=alpine:3.19
/compare_image_versions old_image=org/app:v1 new_image=org/app:v2
/recommend_base_image image=org/app:v1
/inspect_multiarch_manifest image=alpine:3.19
/create_multiarch_manifest target_tag=org/app:v1 source_tags=org/app:v1-amd64,org/app:v1-arm64
/migrate_from_docker_manifest
...or in plain English:
Pull
redis:7-alpineand run it as a container calledcacheon a newapp-netnetwork, exposing port 6379 only inside that network. Containerapi-1keeps restarting - grab the last 200 log lines, inspect its state and exit code, and tell me what's wrong before changing anything. Replace the runningwebcontainer withnginx:1.27while keeping its current ports, mounts, and restart policy. Plan a wordpress + mysql stack on a private network with a named volume for the database. Show me the plan before creating anything. Show every container, network, and volume taggedcom.example.app=webas one table. Don't change anything. We're tight on disk - showsystem_df, prune stopped containers and dangling images, then showsystem_dfagain. Skip volumes. Bring up the compose project in/srv/myapp, but show me the rendered config and pull the images before starting anything. List my Docker contexts and tell me which daemon this MCP server is currently talking to. Find the most recent stable tag forghcr.io/org/repowithout pulling it, and tell me which platforms it supports.
Env var naming. The server's environment variables are namespaced
DOCKER_MCP_SERVER_*to match the published package name. The pre-2.0DOCKER_MCP_*alias spellings are no longer honoured.
To choose which daemon(s) the server talks to, see Talking to a remote daemon and Managing several daemons (DOCKER_MCP_SERVER_HOSTS / DOCKER_HOST). The variables below instead restrict which tools are registered.
Three environment variables restrict which tools are registered when the server starts. Because they drop tools at registration time, a disabled tool never appears in the client's tool list - this is a server-side guarantee, not a client-side prompt. Set the two boolean switches to 1 / true / yes / on:
DOCKER_MCP_SERVER_READONLY - register only read-only tools (queries, log/data reads, scans). Every tool that changes state is omitted. Use this for monitoring or inspection agents that must not be able to modify anything.DOCKER_MCP_SERVER_NO_DESTRUCTIVE - register everything except destructive tools (remove_*, prune_*, container_kill, compose_down, swarm_leave, context_remove, buildx_prune, buildx_remove). A "no data loss" mode that still allows creating and starting resources. DOCKER_MCP_SERVER_READONLY is stricter and wins if both are set.DOCKER_MCP_SERVER_DISABLE - a comma-separated list of domains (feature areas) to drop wholesale, regardless of category: e.g. DOCKER_MCP_SERVER_DISABLE=swarm,services,nodes,configs,secrets removes the entire swarm surface from a single-host server, and DOCKER_MCP_SERVER_DISABLE=scout,buildx trims build/scan tooling an agent will never use. A domain is a tool module's name - images, networks, volumes, compose, stack, context, buildx, scout, registry, swarm, services, nodes, plugins, configs, secrets. system and containers cannot be disabled, so naming either is ignored: system holds what an agent needs to orient itself and recover, and the one wait tool; containers is what a Docker server is for, and the containers the compose, stack and service tools start can only be waited on, read and removed through it. Disabling containers never stopped the agent running code anyway (compose, stack, service_create and image_build all do); DOCKER_MCP_SERVER_READONLY is the switch that does, and that keeps it from logging in to a registry. Disabling a domain also drops its targets from system_wait. Names are case-insensitive; an unrecognised name disables nothing. Either mistake - a name that matches no domain, or one that cannot be disabled - is reported three ways, because an MCP client rarely shows a server's stderr to anyone: a WARNING line on stderr at startup, a configuration_warnings entry in tool_list's result (and unknown_disabled_domains / ignored_disabled_domains in the tool catalog), and a line at the top of the server instructions telling the agent to check tool_list and tell the user. This stacks with the category switches - a tool registers only if its category survives and its domain is enabled. Disabling a domain drops more than its tools: the matching workflow prompts are skipped (so the agent isn't handed a prompt that drives a feature area this server no longer exposes - e.g. disabling scout removes the audit_image_cves prompt that would otherwise tell the agent to call a tool that isn't registered) and the matching documentation resources are hidden from docker-docs://contents (e.g. the scout / scout-cli sections). The tool catalog's prompts list and disabled_doc_sections field make both auditable. Trimming domains an agent doesn't need also cuts the tool-list size the client has to reason about, which matters at this server's size.None of the three is a network control. DOCKER_MCP_SERVER_READONLY restricts what the agent can change, not what the server can reach: the registry and Hub tools are read-only, so a read-only server still makes outbound HTTPS requests to whichever registry host a tool argument names - from wherever the server runs, which may be a machine with more network reach than the daemon it manages. Those requests carry no credentials unless the registry answers with a token challenge, and a credential-bearing token endpoint is validated first (scheme, no plaintext to a public host, and no public registry pointing its realm at a private or loopback address), but the request itself is still made and its response is returned to the agent. docs_lookup and the docker-docs:// resources likewise fetch from a fixed list of documentation hosts. If outbound requests are what you need to stop, DOCKER_MCP_SERVER_DISABLE=registry removes that domain entirely, and network policy on the host is the control for the rest - see PRIVACY.md for the full list of what this server contacts.
Independently, every registered tool carries MCP ToolAnnotations - readOnlyHint on queries and destructiveHint on destructive operations (plus idempotentHint on the prune family) - so a client like Claude Code can auto-allow safe reads and gate destructive calls. The classification lives in TOOL_CATEGORIES in docker_mcp/server.py. To see the full picture at runtime - every tool with its domain, category, and whether the active switches registered it - read the docker-mcp://tool-catalog MCP resource.
For private registries, the HTTPS-backed registry_* tools fall back to DOCKER_MCP_SERVER_REGISTRY_USERNAME / DOCKER_MCP_SERVER_REGISTRY_PASSWORD from the server's environment when no explicit username/password arguments are passed (explicit arguments win; the env pair is only used when both arguments are unset). Setting credentials in the environment keeps them out of tool arguments, which many MCP clients log verbatim - the password may be a personal-access token.
Every Docker object the agent creates through this server - containers, networks, volumes, swarm services, configs, and secrets - is stamped with a small set of docker-mcp-server.* labels recording that this server made it (docker-mcp-server.managed=true), the server version, the originating tool, and a creation timestamp. This lets you (or a cleanup job) later enumerate exactly the footprint the agent created with a single docker ... --filter label=docker-mcp-server.managed=true; the managed_only=True argument on container_list, network_list, volume_list, and service_list is the in-tool shortcut (it combines with any other filters you pass). The stamping is additive (a label you pass yourself always wins on a key collision) and uniquely namespaced, so it's safe by default; DOCKER_MCP_SERVER_NO_LABELS=1 turns it off entirely. Image builds are deliberately not stamped, because a build label changes the resulting image digest.
To tear down only what the server created - and nothing else - use the prune_managed workflow prompt, which scopes every removal step to the docker-mcp-server.managed=true label (volumes only when you pass include_volumes=True, and only after confirmation).
All of these go in the env block of the server entry in your MCP client config (the same place as DOCKER_HOST above). For example, a read-only inspection server against a remote daemon:
{
"mcpServers": {
"docker-mcp-server-readonly": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/L337-org/docker-mcp.git",
"docker-mcp-server"
],
"env": {
"DOCKER_HOST": "tcp://staging-host:2376",
"DOCKER_TLS_VERIFY": "1",
"DOCKER_MCP_SERVER_READONLY": "1"
}
}
}
}
Swap DOCKER_MCP_SERVER_READONLY for DOCKER_MCP_SERVER_NO_DESTRUCTIVE to allow create/start/deploy while still making remove_* / prune_* / container_kill impossible. You can also register the same server twice under different names - a full-access entry you enable when needed and a read-only entry for everyday use. With claude mcp (Claude Code), the equivalent is:
claude mcp add docker-mcp-server-readonly \
--env DOCKER_MCP_SERVER_READONLY=1 \
-- uvx --from git+https://github.com/L337-org/docker-mcp.git docker-mcp-server
l337-docker agent skillA fair question about any MCP server is whether it needs to be one. This repo answers it in the open: skills/l337-docker/ is a Claude Code agent skill that drives Docker purely through the docker CLI - no server process, no Python, nothing beyond a docker binary (plus jq and curl for a few registry recipes).
It exists to mark the ceiling of the skill approach honestly, so you can judge the trade rather than take our word for it. It is written to be as good as that approach gets: a router plus per-domain references and workflows, every command verified against a real daemon, and its shell snippets executed by CI so they cannot drift. For a single local daemon and everyday work, it may well be all you need - if so, use it.
Where it stops is not coverage but enforcement. The skill's safety rules are instructions in a prompt; the server's are refusals in code:
The distinction matters most where it is easiest to overlook: a rule in a prompt can be skipped under pressure, or talked around by a prompt injection sitting in a container log the agent just read. A tool that was never registered cannot be called at all. So prefer the server for anything pointed at production, or wherever you need a guarantee rather than an instruction - and prefer the skill when the stakes are low and the simplicity is worth more.
MCP_VS_SKILLS.md is the full, honest comparison: measured token cost for both - the skill is far cheaper on an eager-loading client, especially at idle, while the server is cheaper per task on a lazy-loading one - plus what each genuinely does better and a tool-by-tool coverage map of every tool, prompt and resource.
To install, download the archive from a release and extract it into your skills directory. It is rooted at l337-docker/, so it lands correctly as-is:
# personal (all projects)
tar -xzf l337-docker-skill-<version>.tar.gz -C ~/.claude/skills
# or per-project, checked into version control
tar -xzf l337-docker-skill-<version>.tar.gz -C .claude/skills
Verify it with the accompanying checksum (sha256sum -c l337-docker-skill-<version>.tar.gz.sha256); the archive is byte-reproducible, so rebuilding from the tag gives the same hash. The skill and the server can coexist - they label the resources they create differently and neither reads the other's.
Connecting this server to an AI agent grants it the same level of access as a local Docker CLI session against the configured daemon. That is broad: the daemon's socket is effectively root-equivalent on the host running it. Treat the agent as a privileged user and weigh the risks below before enabling the server.
DOCKER_HOST at a daemon dedicated to workloads the agent is allowed to touch (a development VM, a remote sandbox, Docker Desktop, a rootless install) rather than your production socket. The daemon is the trust boundary - there is no per-tool authorization layer./var/run/docker.sock into the container grants it the same root-equivalent access to that daemon as the uvx install has - no more, no less, but now explicit in the docker run line. The same scoped-daemon advice applies: prefer mounting a socket for, or pointing DOCKER_HOST at, a daemon the agent is allowed to control. Note that when containerized the file-path tools read and write the container's filesystem, so they can only reach host directories you bind-mount in (see Run as a container). As an accident guard, the destructive container-lifecycle tools (container_remove, container_kill, container_stop, container_restart, container_pause) refuse to act on the server's own container so the agent can't end its own session mid-call; this is convenience, not a security boundary (it's bypassable with DOCKER_MCP_SERVER_ALLOW_SELF_TERMINATE=1, and a human can always recover the container from the host shell), and it does not constrain the many other ways a daemon-privileged agent can affect the host.container_run accepts privileged=True and arbitrary volumes. A privileged container, or one that bind-mounts / from the host, can trivially escape to the host filesystem. Avoid letting the agent set these unless you have reviewed the request. Compose files can declare the same - review the rendered compose_config output before approving compose_up on an unfamiliar project.extra_kwargs / updates bypass the visible schema. container_run, container_create, service_create (extra_kwargs) and container_update, service_update (updates) forward an arbitrary dict straight into the Docker SDK. A client that gates on, say, privileged=False in the tool's declared parameters can still be bypassed via extra_kwargs={"privileged": True, "pid_mode": "host"}. These escape hatches are consistent with the "daemon is the trust boundary" model, but any allow/deny policy you build at the MCP-client layer must account for them rather than trusting the named parameters alone.auth_config you pass through a tool as exposed.
system_login, image_push, image_registry_data) accept credentials directly and can reuse credentials cached by docker login in ~/.docker/config.json. Prefer running docker login once on the host running this MCP server and leaving the credential parameters unset. (Note: this is the host running the server, not the daemon - relevant when DOCKER_HOST points at a remote daemon.) A credential passed to system_login is cached in the server's memory for the life of the client; system_logout clears that in-memory cache (all registries, or one) without touching ~/.docker/config.json, and system_close / system_reconnect clear it by discarding the client. There is no daemon-side session to end - the Engine's /auth endpoint only validates.registry_tags, registry_manifest, registry_image_config, hub_tags, hub_repo_info, hub_rate_limit, and system_wait for a registry tag) talk to the registry directly over HTTPS and do NOT read ~/.docker/config.json. The registry_* tools accept username / password for private registries - or, better, read DOCKER_MCP_SERVER_REGISTRY_USERNAME / DOCKER_MCP_SERVER_REGISTRY_PASSWORD from the server's environment so credentials never transit tool arguments (see Configuration); the hub_* tools support public Hub repositories only. If passing credentials as arguments, use a per-invocation token with the minimum required scope rather than a long-lived password. When a registry answers with a Bearer auth challenge, the server validates the token realm it points at before sending anything: the scheme must be http/https, plaintext http to a non-local host is rejected, and a public registry is not allowed to redirect the credentialed token request at a private/loopback address (an SSRF guard). A genuinely local dev registry (e.g. localhost:5000) may still use a local realm. Pagination is pinned the same way: a Docker Hub next URL must stay on Hub's own origin, and an OCI Link header's host is discarded in favour of the registry already being queried, so a can never redirect this server at a host of its choosing. Redirects themselves are followed across hosts, deliberately - registries answer blob fetches with a redirect to a CDN on another host as normal operation - but the HTTP client strips the header on any cross-origin redirect, so credentials cannot follow one.secret_create(data=...) and config_create(data=...) take the payload as an argument, secret_inspect / config_inspect return the stored object, swarm_join(join_token=...) and swarm_unlock(key=...) take cluster join/unlock secrets, and swarm_unlock_key and swarm_join_tokens return cluster credentials (rotation via swarm_update invalidates old tokens) - a manager join token lets its holder join the swarm as a manager (root-equivalent on the cluster). Treat all of these as exposed in any client that records tool traffic, and prefer provisioning swarm secrets and reading join tokens out-of-band on the host rather than through the agent. If an agent never needs to admit nodes, drop the whole surface with DOCKER_MCP_SERVER_DISABLE=swarm (see Configuration).container_exec, compose_exec, and compose_run run arbitrary commands. When any part of the command is derived from agent-controlled input, use an exec-form argv list that does not invoke a shell (e.g. ["python", "-V"]). A list like ["sh", "-c", template] that invokes a shell will interpret shell metacharacters in the untrusted substrings.container_archive_get and container_archive_put forward the supplied path verbatim to the daemon. The container is the trust boundary - if you do not trust its filesystem, do not assume .. traversal will be rejected.image_save, container_export (with dest_path), and container_archive_get_to_file write to a dest_path on the host running this MCP server (refusing to overwrite an existing file unless overwrite=True); image_load, image_import, and container_archive_put (with from_file) read a host path; compose_cp copies between a service container and a host path in either direction. image_import(from_url=...) is the one that leaves the host entirely: the daemon fetches the URL the caller named, in the daemon's own network namespace, so it can reach anything that daemon can reach (the same caller-chooses-the-destination footing as image_pull's registry argument). These run as the server's user, so the agent can write any file that user can write and read any file it can read. Prefer the in-band byte tools (capped at 32 MiB) when you don't trust the agent with host filesystem access. DOCKER_MCP_SERVER_READONLY also drops the host-writing tools - but note it is not targeted at them: it registers only read-only tools, so image_load and container_archive_put (and every other mutating/destructive tool) go too. There is no switch that drops just the file-writers.prune_*, remove_*, container_kill, swarm_leave, compose_down(volumes=True), compose_kill, stack_remove (tears down every service in a stack), buildx_prune (always runs with --force), and buildx_remove execute immediately. These tools carry the destructiveHint annotation, so a client like Claude Code can gate them, and the shipped clean_environment prompt asks the agent to confirm before pruning volumes - but tool calls themselves are not gated by the server. For a hard guarantee, run with DOCKER_MCP_SERVER_NO_DESTRUCTIVE=1 (drops them entirely) or DOCKER_MCP_SERVER_READONLY=1 (see Configuration); for an approval step, configure it at the MCP client.docker subprocesses on the host running this MCP server. Every invocation passes arguments as a list (no shell, no metacharacter interpretation), resolves the binary via shutil.which, and runs against a scrubbed environment (DOCKER_HOST and related vars only). Positional values (image refs, service / context / builder names, build contexts, bake targets) are additionally rejected if they start with -, so an argument can't be smuggled in as a CLI flag (e.g. a service named --output=...); the one deliberate exception is the trailing command in compose_exec / compose_run, which is meant to be an arbitrary argv. Filesystem paths supplied to compose_* (project_dir, files) are read by the docker CLI on the server host - passing an unfamiliar path can expose any compose file the server's user can read. With no local docker CLI and an ssh:// target those subprocesses run on the remote host instead (above), which shifts two things: the command executes as the remote SSH user with its registry credentials, and the files a command reads are copied to that host's temp directory first - so a buildx_build --secret src= file, or anything else in a staged directory, exists briefly on the remote disk (mode 0700, removed when the call returns; only a dropped connection can leave it behind). Point staging-backed tools at directories you would be content to copy.DOCKER_HOST / DOCKER_MCP_SERVER_HOSTS are unset, the server's initial SDK connection follows your active Docker context (DOCKER_CONTEXT / currentContext) - the same daemon your docker CLI targets - so if that context points at a remote or production daemon, the agent connects there too. Set DOCKER_MCP_SERVER_HOSTS (or DOCKER_HOST, or select a scoped context) before starting the server to pin the target(s) deliberately; with DOCKER_MCP_SERVER_HOSTS the auto/local endpoints are resolved and pinned at startup, so they can't drift if a context changes later. After startup, context_use only changes the CLI default for subsequent CLI-backed tools; SDK-backed tools keep using the daemon their pooled client connected to. There is no runtime way to introduce or retarget a daemon at an arbitrary endpoint - system_reconnect only rebuilds an already-configured host's client (to recover a wedged connection), it can't point it elsewhere; to add or change a daemon, edit DOCKER_MCP_SERVER_HOSTS and restart. This deliberately closes a trust-expansion vector (an agent can't move the root-equivalent boundary to an unvetted endpoint mid-session). context_create(skip_tls_verify=True) disables TLS verification for a context; use only against trusted local daemons.(ro) in DOCKER_MCP_SERVER_HOSTS makes mutating/destructive tools refuse to act on it at call time (and, with several hosts, writes require naming the target host explicitly - so the agent can't change the wrong daemon by omission). Like guard_not_self, this is in-process convenience: it constrains the agent through this server's tools, but the daemon itself is still the trust boundary, so for a host the agent must never modify, prefer pointing it at a genuinely read-only or scoped daemon over relying on the marker alone.(nd) refuses only destructive tool calls (kills, removals, prunes) while reads and ordinary mutations still go through. Like (ro), this is in-process convenience enforced by this server's guard, not a daemon-level boundary.cryptography (a transitive dependency, via mcp -> pyjwt[crypto]) has shipped no x86_64/universal2 macOS wheel since 49.0.0 - confirmed permanent, not transient - so a native install there (uvx, pip, or the .mcpb bundle) resolves to a current version and builds it from source, needing Rust and OpenSSL 3.x. This project briefly tried to avoid that build step with its own cryptography<49 cap scoped to Intel macOS - that cap turned out to hold back every platform, not just the one it named (a platform-scoped upper bound alone doesn't make uv lock fork the resolution), so it silently left everyone on a version within a later high-severity CVE's range (a PKCS#7 EnvelopedData decryption Bleichenbacher oracle). The cap was removed rather than replaced: this project doesn't decrypt PKCS#7 envelopes itself, but a dependency carrying a known-exploitable flaw is not something to ship knowingly just to keep one platform wheel-only. If you'd rather not install a build toolchain, use the container image instead (built centrally on Linux, so it never hits this).docker-mcp-server collects no data, sends no telemetry, and has no author-operated backend. It runs locally and talks only to the Docker daemon and container registries you point it at, as part of the operations you request. The full statement is in PRIVACY.md.
Contributions are welcome. The project values a tight mapping between the Docker SDK's public surface and the MCP tools we expose. See CONTRIBUTING.md for the project layout, tool conventions, the checklist for adding a new tool module, and local development setup.
docker-mcp-server is an independent open-source project. It is not affiliated with, endorsed by or sponsored by Docker, Inc. Docker is a trademark or registered trademark of Docker, Inc.
Manage Docker (containers, images, Compose, Swarm, registries) via the Docker SDK and CLI.
{ "mcpServers": { "io.github.l337-org-docker-mcp-server": { "command": "uvx", "args": [ "docker-mcp-server==2.3.0" ] } }}เครื่องมือ
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คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
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Docker MCP Server ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-13 Manage Docker (containers, images, Compose, Swarm, registries) via the Docker SDK and CLI.
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