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    GenieACS-MCP is a Go-based MCP server that bridges any GenieACS (TR-069 ACS) instance, exposing device data, firmware management, and CPE actions (reboot, parameter refresh, firmware download) over JSON-RPC
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    GPL 3.0
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    TypeScript stdio MCP server for operating Docker-based local-ydb deployments from AI coding agents. Supports local and SSH-backed profiles, bootstrap, diagnostics, auth hardening, schema permissions, storage workflows, dump/restore, upgrades, and plan-first mutating operations.
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    MIT
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    An MCP server for managing Incus virtual machines through structured tools for command execution, file management, and snapshot operations. It enables AI agents to puppeteer VMs on a masternode by wrapping the Incus CLI.
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    An MCP server that enables LLMs to execute Python code on GPU-accelerated compute nodes within SLURM-managed HPC environments. It bridges local clients to remote clusters by launching JupyterLab sessions via SLURM jobs to facilitate high-performance notebook-based computation.
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    All-in-one homelab management MCP server. Docker monitoring, volume backup/restore (with compose and env files), Wake-on-LAN, network scanning, and multi-server SSH management.
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    MIT
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    Manage and monitor Hadoop clusters via Apache Ambari API, enabling service operations, configuration changes, status checks, and request tracking through a unified MCP interface for simplified administration. * Guide: https://call518.medium.com/llm-based-ambari-control-via-mcp-8668a2b5ffb9
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    Provides unified control for both physical robots (ROS-based like Moorebot Scout, Unitree) and virtual robots in Unity3D/VRChat, enabling multi-robot coordination, environment generation, and automated 3D model creation.
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    An MCP server that allows AI agents to remotely control Windows, Linux, and macOS systems via VNC. It provides tools for mouse and keyboard interaction, text input, and screen capturing.
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    A Model Context Protocol server for conducting Linux binary analysis and guest system forensics within QEMU virtual machines. It enables automated VM lifecycle management, memory dumping, and interactive debugging workflows for analyzing processes and artifacts.
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    Exposes vLLM capabilities to AI assistants, enabling chat completions, model management, and platform-aware container control with automatic detection of Docker/Podman and GPU availability across Linux, macOS, and Windows.
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    Apache 2.0
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    Enables AI assistants to manage homelab infrastructure through automated service installation (Jellyfin, Pi-hole, Ollama, Home Assistant, Frigate NVR), VM operations, AI accelerator support (MemryX, Coral TPU, Hailo-8), and Terraform state management with SSH-based discovery and deployment.
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    MIT