"How to execute custom code in a Linux Docker container" matching MCP servers:

MCP server for declarative intent execution. Agents submit intents — transfers, swaps, multi-hop routes — and the engine resolves and executes them atomically.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

MCP server enabling AI agents to control a real Linux browser with live view, human takeover, and safety guardrails.

Provides an AI agent with a fixed, isolated Linux desktop workspace, enabling bounded screen capture, input control, and application launching via MCP.

Controls a real Linux desktop from any MCP host. Reads accessibility trees, takes screenshots, and drives clicks, scrolls, and keystrokes across GNOME, KDE/KWin, Hyprland, i3, and COSMIC.

Enables 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.

Enables natural language management of Docker containers, images, networks, and volumes with support for both local and remote Docker engines. Features automated container composition, debugging capabilities, and persistent data management through an intuitive conversational interface.

Provides safe Linux desktop automation for Codex through focused MCP tools for inspecting, clicking, typing, dragging, and verifying native applications on Wayland and X11.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

A standalone MCP server for Linux desktop control, enabling screenshots, mouse/keyboard input, app/window management, and clipboard access via a Python bridge.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

An MCP server for Microsoft's Cognitive Services Custom Vision Training API that enables AI agents to create, train, and manage custom image classification and object detection models through natural language interactions.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables multi-agent council workflows for coding assistants, providing parallel independent reviews, plans, brainstorms, validation, and research with strict read-only boundaries.

A security filter that blocks dangerous code patterns by comparing normalized structural syntax trees against a blacklist of known threats using vector embeddings. It acts as a gatekeeper to prevent malicious code execution by identifying dangerous structures regardless of specific identifiers or literals.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

A Model Context Protocol server that converts Markdown content to HTML format.

An implementation of Claude Code as a Model Context Protocol server that enables using Claude's software engineering capabilities (code generation, editing, reviewing, and file operations) through the standardized MCP interface.

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.