Docker MCP Server

# 🐋 Docker MCP server An MCP server for managing Docker with natural language! ## 🪩 What can it do? - 🚀 Compose containers with natural language - 🔍 Introspect & debug running containers - 📀 Manage persistent data with Docker volumes ## ❓ Who is this for? - Server administrators: connect to remote Docker engines for e.g. managing a public-facing website. - Tinkerers: spin up containers locally, without running a single command yourself. ## 🏎️ Quickstart ### Install #### Claude Desktop On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json` On Windows: `%APPDATA%/Claude/claude_desktop_config.json` <details> <summary>Install with `uv`</summary> ``` "mcpServers": { "mcp-server-docker": { "command": "uv", "args": [ "--directory", "/path/to/repo", "run", "mcp-server-docker" ] } } ``` </details> <details> <summary>Install with Docker</summary> After cloning this repository, build the Docker image: ```bash docker build -t mcp-server-docker . ``` And then add the following to your MCP servers file: ``` "mcpServers": { "mcp-server-docker": { "command": "docker", "args": [ "run", "-i", "--rm", "-v", "/var/run/docker.sock:/var/run/docker.sock", "mcp-server-docker:latest" ] } } ``` </details> ## 📝 Prompts ### 🎻 `docker_compose` Use natural language to compose containers. Provide a Project Name, and a description of desired containers, and let the LLM do the rest. This prompt instructs the LLM to enter a `plan+apply` loop. Your interaction with the LLM will involve the following steps: 1. You give the LLM instructions for which containers to bring up 2. The LLM calculates a concise natural language plan and presents it to you 3. You either: - Apply the plan - Provide the LLM feedback, and the LLM recalculates the plan #### Examples - name: `nginx`, containers: "deploy an nginx container exposing it on port 9000" - name: `wordpress`, containers: "deploy a WordPress container and a supporting MySQL container, exposing Wordpress on port 9000" #### Resuming a Project When starting a new chat with this prompt, the LLM will receive the status of any containers, volumes, and networks created with the given project `name`. This is mainly useful for cleaning up, in-case you lose a chat that was responsible for many containers. ## 📔 Resources The server implements a couple resources for every container: - Stats: CPU, memory, etc. for a container - Logs: tail some logs from a container ## 🔨 Tools ### Containers - `list_containers` - `create_container` - `run_container` - `recreate_container` - `start_container` - `fetch_container_logs` - `stop_container` - `remove_container` ### Images - `list_images` - `pull_image` - `push_image` - `build_image` - `remove_image` ### Networks - `list_networks` - `create_network` - `remove_network` ### Volumes - `list_volumes` - `create_volume` - `remove_volume` ## 🚧 Disclaimers ### Sensitive Data **DO NOT CONFIGURE CONTAINERS WITH SENSITIVE DATA.** This includes API keys, database passwords, etc. Any sensitive data exchanged with the LLM is inherently compromised, unless the LLM is running on your local machine. If you are interested in securely passing secrets to containers, file an issue on this repository with your use-case. ### Reviewing Created Containers Be careful to review the containers that the LLM creates. Docker is not a secure sandbox, and therefore the MCP server can potentially impact the host machine through Docker. For safety reasons, this MCP server doesn't support sensitive Docker options like `--privileged` or `--cap-add/--cap-drop`. If these features are of interest to you, file an issue on this repository with your use-case. ## 🛠️ Configuration This server uses the Python Docker SDK's `from_env` method. For configuration details, see [the documentation](https://docker-py.readthedocs.io/en/stable/client.html#docker.client.from_env). ## 💻 Development Prefer using Devbox to configure your development environment. See the `devbox.json` for helpful development commands.