"Using Docker for windsurfing-related applications or tools" matching MCP servers:

Provides Docker management tools for AI agents, including inspecting containers, images, Dockerfiles, and Compose configurations.

Wraps the Docker Engine API as MCP tools for container and image management. Reduces token consumption by providing typed tools with default summaries and field selection.

Enables an AI agent to run and inspect containers, build and pull images, manage volumes and networks, drive Compose projects, clean up, and run self-checking practice labs. Startup modes restrict it to read-only, labs-only, or full Docker control with confirmation before dangerous operations.

Enables LLMs to search Docker Hub repositories, discover container images, and manage Docker Hub repositories and tags through natural language queries. Supports both public content access and authenticated operations with Docker Personal Access Tokens.

Enables inspecting, orchestrating, and troubleshooting Docker Engine and Docker Compose environments through six consolidated high-leverage MCP tools.

A tool that helps AI agents interact with Docker containers on local or remote machines, allowing users to manage and troubleshoot containers without Docker knowledge.

Universal Docker MCP server for AI assistants (Cursor, Claude Desktop). Manage Docker containers, execute commands, query databases, and handle environment configurations — all through natural language.

Docker container, image, volume, network, and compose management from your AI assistant.

Enables AI assistants to manage Docker containers and Compose stacks through natural language, including lifecycle operations, logs, stats, and secure remote access.

An MCP server that enables AI assistants to manage Docker containers, images, networks, and volumes through a standardized interface. It supports comprehensive operations like container lifecycle management and image pulling while ensuring correct handling of command and environment variable arrays.

Enables AI agents to manage Docker containers, images, Compose stacks, health checks, and logs through a unified MCP interface, ensuring containers stay running with self-healing capabilities.

Enables spawning ephemeral Linux sandbox containers using Docker and executing commands through an interactive TTY interface. Supports collaborative terminal sessions where both AI clients and humans can simultaneously interact with the same container.

Manages Docker containers, images, and Compose stacks on a remote host over SSH, with file sync and deploy orchestration.

Enables AI assistants to search, analyze, and manage Docker images on Docker Hub through standardized MCP tools, with features including security scanning, layer analysis, and image comparison.

Provides a local API to manage Docker containers and volumes, enabling operations like listing, inspecting, starting, stopping, and removing containers, as well as managing volumes, all through HTTP endpoints without shell commands.

Enables AI assistants to manage Docker containers, images, networks, volumes, and Compose services through the Model Context Protocol. It supports system operations, command execution within containers, and integration with Docker Hub and GitHub Container Registry.

A local MCP server that enables AI applications like Claude Desktop to securely access and work with Obsidian vaults, providing capabilities for reading notes, executing templates, and performing semantic searches.

Enables secure execution of shell commands and file operations within isolated Docker containers. Provides process management, interactive input handling, and comprehensive file system operations for containerized development environments.

An MCP server that provides full lifecycle management of lightweight VMs backed by Docker containers, enabling creation, SSH access, command execution, and resource monitoring through natural language.

Enables deterministic calculations for AI API and subscription costs, meeting time zones, recurring schedules, and cron next-run previews, returning normalized inputs, units, assumptions, dated sources, and links to results.

Enables AI assistants to manage Docker containers and Kubernetes resources through natural language, supporting operations like container management, image building, and pod/deployment/service management.

Enables an AI assistant to control a Hyprland/Wayland Linux desktop through 178 tools across 30 categories, covering audio, window and workspace management, themes and wallpapers, screenshots and recording, Bluetooth, media playback, notifications, system stats, and chained multi-step macros. Everything runs locally via hyprctl, PipeWire, and standard Linux utilities, with no cloud dependencies.

Provides deterministic tools for transforming, formatting, and inspecting structured data for AI agents.

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.

A local Model Context Protocol (MCP) server that exposes custom tools to Claude Desktop, enabling direct interaction with your local environment. It provides a framework for building and integrating custom TypeScript tools into the Claude interface.

Enables Claude Agent SDK agents to safely operate a remote user machine via a WebSocket tunnel, providing six client__* MCP tools with path fencing and phase-switch adjudication.

Connects the Docker Desktop MCP gateway as an MCP server, enabling profile discovery, selection, and reconnection from the DSH web GUI.

Enables AI clients to interact with WhatsApp via MCP tools for searching contacts, sending/receiving messages, and managing chats, running in a Docker container with HTTP/SSE transport.

Collection of MCP servers for Forgejo, NetBox, Authentik, and CouchDB, enabling issue tracking, device management, authentication, and database operations via MCP and CLI.

Provides five rigorous reasoning protocols (debate, red team, audit_argument, threat_model, check_study) that run on the AI you're already using, requiring no extra API keys or costs.