L337-org/docker-mcp

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.
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.
Requirements
Note: If you're using the containerized MCP server or MCPB bundle, the Python and uv requirements are taken care of for you.
- A running Docker daemon reachable from the host that runs the server (the standard DOCKER_HOST / unix socket conventions apply)
- Python ≥ 3.14
- uv for dependency management
- **Intel (x86_64) macOS only:** installing natively (via 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.
Using the server
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:
To pin a specific version, append == 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).