"Proof-of-Concept Examples for Multi-Component Processes (Model Context Protocol)" matching MCP servers:

A gateway for Generative AI systems to interact with multiple Kubernetes clusters through Model Context Protocol, enabling comprehensive Kubernetes resource operations and multi-cluster management.

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 deployment and management of GPU workloads across multiple cloud providers (RunPod, Vast.ai) with intelligent GPU selection, resource monitoring, and telemetry tracking through Redis, ClickHouse, and SkyPilot integration.

Enables managing multiple Kubernetes clusters simultaneously with over 60 tools for cluster operations, resource management, monitoring, RBAC, storage, networking, and more through AI assistants.

Enables managing E2B cloud sandboxes, templates, filesystems, and processes through Model Context Protocol tools.

A Node.js application that provides a JSON-RPC interface to interact with CO2 sensor data, working in both simulation mode and with real Raspberry Pi Pico hardware connections.

A Model Context Protocol server for the resQ emergency response system that integrates with digital twin simulations and coordination engines. It allows users to trigger simulations, generate deployment strategies, and monitor real-time drone status for incident response analysis.

An MCP server that enables natural language querying and troubleshooting of Kubernetes clusters, supporting multi-cluster switching and providing tools for pods, nodes, events, and logs.

A Model Context Protocol (MCP) server that interfaces with the Apache YuniKorn Scheduler, allowing AI agents to observe and reason about Kubernetes batch workload resource management, queue hierarchies, and application states.

A Linux system monitoring MCP server that provides real-time information on CPU, memory, disk, network, processes, Docker, security, and more via MCP tools.

Enables AI assistants to remotely control Sipeed NanoKVM hardware for BIOS-level management of servers and headless machines. It provides tools for power control, keyboard and mouse emulation, screen capture, and ISO image mounting via the Model Context Protocol.

Enables querying local MySQL databases and managing Docker containers (listing, logs) through natural language via the Model Context Protocol.

Enables AI agents to run CP/M, MP/M, and DOS packages on Z80/x86 emulators, inspect byte-exact results and files, and debug the emulators themselves over the Model Context Protocol.

A Model Context Protocol server that enables AI assistants to create, control, and interact with virtual desktop environments through E2B's secure cloud sandboxes.

A Model Context Protocol server that provides integration with the Coolify API, enabling DevOps teams to manage Coolify deployments, applications, services, and servers through MCP tools.

A Model Context Protocol server that enables AI clients to interact with virtual Ubuntu desktops, allowing them to browse the web, run code, and control instances through mouse/keyboard actions and bash commands.

A Model Context Protocol server that enables Docker container management through natural language interactions using a custom GPT interface.

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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MCP server that integrates with Coolify to let AI assistants manage Coolify instances via a clean toolkit wrapping the official REST API.