"A YouTube channel named Franko Leban" matching MCP servers:

Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Enables AI assistants to autonomously investigate and manage Sophos Channel Server incidents by connecting via SSH, checking Zabbix alerts, searching Jira, pattern matching known root causes, and creating LINFRA tickets.

A Model Context Protocol server for executing commands via SSH on remote servers and groups using secure authentication and regex-based command policies. It supports hot-reloading configurations and both sequential and parallel command execution across multiple named hosts.

Enables AI agents to execute local commands and multi-step workflows with durable checkpoints, operator controls, and safe resume/revise capabilities. It also provides local web and YouTube research tools, with optional Windows UI automation.

A persistent, pooled PowerShell MCP server that keeps long-lived named pwsh sessions with clean I/O, enabling stateful command execution and multi-session isolation.

A MCP server that can run Kubernetes commands with a given kubeconfig path and provide interpretation of the commands.

Enables AI assistants to execute security testing tools on a Kali Linux machine over SSH, including reconnaissance, web app scanning, and static/dynamic analysis.

Enables Claude to execute PowerShell commands and scripts in a persistent interactive terminal on Windows, with real-time output streaming to a web terminal.

A secure, local communication runtime bridge that interfaces a cloud-based Large Language Model (Claude Desktop) with a local machine execution environment using the Model Context Protocol (MCP).

Enables AI clients to view screens, run commands, and transfer files across a fleet of LAN-connected devices through a single MCP interface.

A desktop launcher and local MCP workspace server that enables ChatGPT and other MCP clients to securely read, edit, search, run commands, and show changes in selected local project folders.

Enables MCP clients to connect to a local workspace over a public tunnel and lets them run shell commands and transfer files bidirectionally.

Enables AI agents to execute commands in a shared tmux session that is simultaneously visible to a human via a web-based terminal UI.

A server that exposes Pi-hole functionality as tools for AI assistants, allowing them to retrieve local DNS settings and query history through natural language.

A unified mesh control layer that provides 30 MCP tools for managing distributed infrastructure, enabling AI agents to execute commands, transfer files, and manage Docker containers across multiple nodes. It features persistent SSH2 connection pooling and cross-node configuration synchronization to unify disparate servers into a single interface.

Simple MCP Runner makes it effortless to safely expose system commands to language models via a lightweight MCP server—all configurable with a clean, minimal YAML file and zero boilerplate.

An MCP server that gives AI agents SSH capabilities to execute commands, transfer files, and inspect remote systems through a preconfigured host list.

A single MCP server that gives an AI assistant comprehensive access to manage a homelab, including SSH, Docker, Proxmox, Synology, Cloudflare, and more, with 85 tools and a centralized configuration.