"Codebeamer application lifecycle management platform" matching MCP servers:

MCP server for the Agent Platform API, enabling management of sandbox environments through MCP protocol. Exposes tools to interact with sandbox lifecycle operations such as start, stop, and status checks.

Enables deploying multiple isolated Odoo MCP connector containers from a single shared codebase, each with its own configuration and credentials for different clients.

A VMware ESXi/vCenter management server based on MCP (Machine Control Protocol), providing simple REST API interfaces for virtual machine management.

Open-source MCP server for managing Hetzner Cloud infrastructure with two management layers: * Layer 1 — Hetzner Cloud API (35 tools): Server power control, metrics, snapshots, backups, firewalls, DNS zones and records, rescue mode, server rebuild and rescale. Works even when the server OS is unresponsive. * Layer 2 — SSH (25 tools): Service management (systemd), Nginx config and reload,

MCP server for managing Proxmox VE resources, VM/CT lifecycle, snapshots, backups, and clones via natural language.

Enables AI-assisted management of vCenter Server and standalone ESXi hosts, including lifecycle, deployment, guest operations, cluster management, datastore browsing, alarms, and investigations through natural language.

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.

AI-powered VMware vCenter/ESXi monitoring and operations. 20 MCP tools for inventory queries, health monitoring, VM lifecycle management, fast provisioning (Linked Clone, OVA, template deploy), snapshot management, and datastore browsing. Supports vSphere 6.5–8.0. Works with local models via Ollama/LM Studio.

An MCP server for managing standalone Cisco C-Series rack servers through the Cisco Integrated Management Controller (CIMC) XML API. It provides 16 tools for server lifecycle management, including power control, hardware health monitoring, and BIOS configuration.

Manages QNAP Virtualization Station (VMs, snapshots, disks, ISOs) via the QVS REST API, enabling VM lifecycle operations and resource monitoring through natural language.

Manage Docker infrastructure across multiple homelab hosts including Unraid, Proxmox, and bare metal servers. Enables container lifecycle management, log retrieval, resource monitoring, and Docker operations across your entire homelab from a single interface.

MCP server for YouGPU REST API that enables GPU catalog browsing, instance lifecycle management (create, stop, start, delete), and balance checking.

Enables LLM agents to monitor and manage a Proxmox VE cluster, including cluster status, VM/container lifecycle, and node actions via MCP tools.

Enables AI agents to manage WSL containers and images via the wslc CLI, supporting container lifecycle, network, volume, and registry operations through natural language.

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 management of GNOME Boxes virtual machines through libvirt/virsh with lifecycle and snapshot operations.

Enables AI tools to manage containerized applications through Podman, supporting container lifecycle operations, command execution, log viewing, image management, and resource monitoring. Features automatic network discovery for seamless integration with MCP Discovery Hub.

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.

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.