This server provides comprehensive virtualization management capabilities across VMs, networks, storage, snapshots, sandboxes, and AI-driven workflows.
VM Lifecycle Management
List, create, start, stop, delete, clone, reset, pause, and resume VMs
Support for a wide range of OS types (Ubuntu, Debian, Fedora, Windows 10/11, macOS, etc.)
Configure memory (MB) and disk size (GB) at creation time
Network Management
List, create, and remove host-only networks
List and configure VM network adapters (NAT, bridged, host-only, internal, generic, NAT network)
Snapshot Management
List, create, restore, and delete VM snapshots with optional descriptions
Storage Management
Manage storage controllers (IDE, SATA, SCSI, SAS, USB, PCIe): list, create, remove
Create and attach virtual disks to VMs
System Diagnostics
Get host system info, VirtualBox version, and supported OS types
Retrieve VM performance metrics and take screenshots of running VMs
Docker Sandbox Management
Run ephemeral code snippets (Python, JavaScript, Bash) in throwaway containers
Execute host files in isolated containers
Create and manage persistent stateful Docker sessions (run commands, read/write files, destroy sessions)
Agentic VM Workflows
Get AI-suggested VM configurations for a given use case (e.g., CI runner, malware sandbox)
Generate step-by-step sandbox workflow plans (spin-up → work → snapshot → tear-down)
Execute autonomous multi-step VM orchestration goals via natural language
Tool Discovery
List all available tools, filter by category or search term
Get detailed info and JSON schemas for any tool
Access general server help and quick-start documentation
Allows downloading Debian ISO images and setting up automated installations with dev tools via autoinstall.yaml.
Allows downloading Ubuntu ISO images and setting up automated installations with dev tools via autoinstall.yaml.
Provides tools for creating, managing, and controlling VirtualBox virtual machines, including lifecycle operations (start, stop, pause, snapshot, delete), networking (NAT, bridged, host-only, port forwarding), and unattended installations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@virtualization-mcp create an Ubuntu 24.04 VM, attach the ISO, and set up port forwarding"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Spin up VMs, sandboxes, and dev environments from Claude Desktop, Cursor, or the fleet webapp.
VirtualBox & Hyper-V — create, start, stop, snapshot, clone VMs
Libvirt / KVM / QEMU — native Linux and WSL2 hypervisor management
Windows Sandbox & MCP Tools — consumer (nearly naked) and dev-infra bringups with direct MCP tool triggers (win_sandbox_launch_consumer, win_sandbox_launch_devinfra, win_sandbox_terminate) and singleton auto-cleanup
ResourceGuard Safety — physical host CPU & RAM quota enforcement (default 95% threshold) to protect host stability
Prefab UI Cards — interactive FastMCP UI cards (show_vm_card, show_hypervisor_health_card, show_sandbox_status_card)
Cloud-Init ISO Generator — create non-interactive Linux VM seed ISOs (user-data & meta-data)
ISO pipeline — download Ubuntu, Debian, Windows ISOs into assets/vbox
noVNC console — browser VM console from the webapp
Modular Webapp Backend — structured FastAPI routers (/api/vms, /api/snapshots, /api/system)
Fleet dashboard — health-check and launch registered MCP webapps
Related MCP server: CUA MCP Server
Virtual machines are older than you think. The concept dates to the 1960s, driven by a problem that still sounds familiar: expensive hardware that was mostly idle.
IBM pioneered virtualization on the IBM System/360-67 (1966) and later the System/370 (1972). The CP/CMS operating system (precursor to VM/370) introduced the idea of a hypervisor — a thin layer that partitions physical hardware into isolated "virtual machines," each running its own OS. This was pure pragmatism: a mainframe cost millions and ran batch jobs at night. Virtualization let multiple research teams share the machine concurrently without stepping on each other.
IBM's VM/370 became the production version, and its design — a privileged "control program" managing guest operating systems — is the direct ancestor of every Type-1 hypervisor today. The term "virtual machine" itself comes from this era.
When computing moved from mainframes to x86 workstations and servers, virtualization was essentially lost. The x86 architecture had no concept of privilege rings that could trap and emulate guest OS instructions efficiently. The few attempts (like VMware Workstation 1.0 in 1999) used binary translation — dynamically rewriting guest instructions on the fly — which was slow and fragile but proved it could be done.
Intel and AMD eventually added hardware virtualization extensions: Intel VT-x (2005) and AMD-V (2006). These introduced a new "root mode" that lets the CPU natively execute guest instructions without binary translation. This was the unlock that made x86 virtualization performant enough for production.
With hardware assist in place, virtualization exploded:
VMware dominated the enterprise with ESX/vSphere, building a multi-billion-dollar business on server consolidation (replacing 10 underutilized physical servers with 1 host running 10 VMs).
Xen (2003, Cambridge University) became the open-source standard, powering early AWS (EC2 ran on Xen until 2017). Amazon chose Xen because it was free and could be customized for multi-tenant isolation at scale.
KVM (2007, Avi Kivity/Qumranet) turned Linux itself into a Type-1 hypervisor by adding the kvm kernel module. Red Hat acquired Qumranet in 2008 and made KVM the default for RHEV and OpenStack. KVM is now the most widely deployed hypervisor on the planet by sheer host count (every Android phone runs a KVM-based protected VM for Trusty/AVB, every Chromebook runs KVM for Linux containers, every major public cloud uses KVM or a derivative).
VirtualBox (2007, Sun Microsystems, later Oracle) targeted the desktop and developer market — free, cross-platform, easy to use. It never aimed at the datacenter but became the de-facto standard for "I need a VM on my laptop."
Hyper-V (2008, Microsoft) was Microsoft's response, a Type-1 hypervisor built into Windows Server and later Windows Pro/Enterprise. It competed with VMware on Windows workloads and came free with the OS.
Parallels started as a Windows/Linux desktop hypervisor (Parallels Workstation, 2005) but pivoted hard to macOS after acquiring the Mac code in 2006. Parallels Desktop for Mac became the dominant solution for running Windows VMs on Apple hardware — first on Intel Macs via full virtualization, then on Apple Silicon via a custom hypervisor that translates x86 to ARM on the fly (Rosetta-like). There is also Parallels RAS (Remote Application Server), an enterprise product for virtual app delivery on Windows Server, but it is a much smaller business than the desktop Mac product. The Linux/Windows Workstation versions were discontinued in the early 2010s. Effectively: Parallels is macOS-only today, and its sole market is "run Windows on a Mac."
The rise of AWS, Azure, and GCP changed the question from "which hypervisor do I install?" to "which API do I call?" Nobody cared whether EC2 ran on Xen or KVM (it's KVM now) — they cared about the RunInstances API.
Containers (Docker, 2013) then questioned whether full VMs were needed at all. Why run a whole OS when a process-level sandbox with cgroups and namespaces was faster and lighter? Kubernetes (2014) orchestrated containers at scale, and for a while it seemed like VMs were legacy.
But containers don't actually replace VMs — they run on top of them. Every Kubernetes node is a VM (or bare metal, but mostly VMs in the cloud). The two technologies are complementary, not competing. The industry settled on: containers for application packaging, VMs for isolation and infrastructure.
Today's landscape is stratified:
Public cloud — AWS Nitro (KVM-based with custom silicon), Azure (Hyper-V), GCP (KVM). Customers consume VMs through APIs, never touching a hypervisor.
Enterprise on-prem — VMware (declining post-Broadcom), Hyper-V, Nutanix AHV, Proxmox VE (rising). The Broadcom VMware disaster accelerated a migration wave that will take years to play out.
Developer laptops — VirtualBox, Hyper-V (via Docker Desktop/WSL2), Parallels (macOS), UTM (Apple Silicon QEMU), and Multipass (Canonical's lightweight Ubuntu VMs). The trend is toward lightweight, API-driven VMs that can be provisioned in seconds and discarded just as fast.
Edge / IoT — k3s (Kubernetes on VMs or bare metal), KVM-on-arm (Raspberry Pi 5 can run VMs now), and embedded hypervisors like Jailhouse and ACRN.
This project sits in the developer laptop and edge segment — managing VirtualBox and Hyper-V VMs, Windows Sandbox ephemeral environments, and Docker sandbox containers, all from a unified MCP tool surface. It's a pragmatic snapshot of the 2026 virtualization landscape: free tools, local execution, API-driven, AI-friendly.
This project integrates with a deliberately curated set of virtualization backends. Here is the full landscape and why each technology was chosen or rejected.
Status: ✅ Supported as primary VM backend
Oracle VM VirtualBox is the default hypervisor for this project. It is free, open-source (GPLv2), runs on Windows/Linux/macOS, and exposes a mature CLI (VBoxManage) that covers VM lifecycle, snapshots, networking, storage, VRDP, unattended installs, and more. The pyvbox Python API provides direct bindings for Python 3.12. VirtualBox's strength is its zero-cost entry and broad OS compatibility — any developer can install it without a license server or subscription.
Feature | Via VBoxManage |
VM lifecycle |
|
Snapshots |
|
Networking |
|
Storage |
|
VRDP/remote |
|
Unattended install |
|
Status: ✅ Supported as secondary VM backend
Microsoft Hyper-V is a Type-1 hypervisor built into Windows Pro/Enterprise/Education. It is managed via PowerShell (Get-VM, New-VM, Start-VM, etc.) and provides native Windows VM performance with no additional install. Hyper-V is used for Gen2 VMs with UEFI boot and TPM support (required for Windows 11 guest VMs without workarounds).
Feature | Via PowerShell |
VM lifecycle |
|
Generation | Gen1 (BIOS) or Gen2 (UEFI) |
Limitations | No snapshot API via PowerShell, no VRDP passthrough |
Hyper-V is not available on Windows Home edition — the app detects this and degrades gracefully.
Status: ✅ Supported for ephemeral sandbox environments
Windows Sandbox is a lightweight VM built on Hyper-V technology that provides a disposable, isolated Windows environment. Each launch creates a fresh image from the host's base Windows installation. Changes are discarded when the sandbox closes.
The project supports three sandbox modes:
Consumer — completely naked Windows, no pre-installed tooling. Used for testing install walkthroughs on a simulated "naked PC."
Dev Infra — includes winget, uv, git, and network access. Used for testing fleet deployment scripts and MCP server installs.
Full Dev — user-selectable tooling (Python, Node, VS Code, Cursor, etc.) installed automatically at first boot.
Status: ✅ Partially supported for container execution
Docker is supported for ephemeral sandbox-style container execution (run isolated commands, compile code, execute scripts) via the sandbox_management portmanteau tools. This is not a full Docker Compose or Kubernetes replacement — it is a lightweight "run a command in a throwaway container" feature for development workflows.
Status: ❌ Not supported — see below
VMware was not selected for this project. The reasons are both technical and ethical:
The Broadcom takeover (Nov 2023): Broadcom completed its $69B acquisition of VMware in November 2023. Within months, Broadcom:
Terminated all perpetual license sales — customers are forced into subscription-only pricing.
Bundled products into massive "VMware Cloud Foundation" suites with 2–5× price increases.
Killed free versions (vSphere Hypervisor, VMware Player for commercial use lost functionality, Workstation Pro was made free only after public backlash in May 2024 — and then Workstation Pro/Fusion Pro were open-sourced in Nov 2024, likely to offload maintenance).
Laid off thousands of VMware engineers, gutting product teams.
Imposed punitive audit terms on existing enterprise customers, with some reporting 300–500% renewal cost increases.
The customer rip-off pattern: Broadcom's playbook is consistent across acquisitions (CA Technologies, Symantec, VMware):
Acquire a critical infrastructure vendor
Eliminate perpetual licenses → force subscriptions
Bundle products into expensive suites
Ratchet prices after lock-in
Cut R&D to the bone
For a virtualization management tool like this one, depending on VMware would mean:
Requiring users to have a paid vSphere/vCenter license (most individual developers don't)
Being at the mercy of Broadcom's licensing terms and price changes
Supporting a shrinking ecosystem as customers migrate away
The post-Broadcom landscape (2024–2026): The VMware exodus is real. Enterprises are migrating to:
Microsoft Hyper-V (already supported here)
Proxmox VE (open-source KVM-based, growing rapidly)
Nutanix AHV (proprietary but Broadcom-free)
Oracle VirtualBox (already supported here)
KVM/libvirt (Linux-native, open-source)
We may add Proxmox VE support in a future release, as it is the most natural open-source replacement for vSphere in the small-to-mid datacenter segment.
Status: ✅ Supported via REST API
Proxmox VE is an open-source (GNU AGPLv3) virtualization platform based on KVM and LXC. It provides a web UI, REST API, clustering, and live migration — similar to vSphere but without the licensing cost.
It is already supported as a remote backend in this project. If you have a Proxmox host (even a single node), set three environment variables and the MCP server will discover and manage Proxmox VMs alongside VirtualBox and Hyper-V:
# In your shell or start.ps1
export PROXMOX_HOST=192.168.1.100
export PROXMOX_USER=root@pam
export PROXMOX_PASSWORD=your-password
# Optional:
# export PROXMOX_NODE=pve1 # autodetected if not set
# export PROXMOX_VERIFY_SSL=0 # default: 0 (self-signed certs)That's it. No Proxmox-specific plugins, no agent installs, no separate service. The client authenticates via the Proxmox ticket API and supports:
Operation | Endpoint |
List VMs |
|
Start/Stop/Shutdown |
|
Create VM | Configurable CPU, RAM, disk, ISO, network bridge |
Delete VM |
|
Snapshots | Create, list, delete via |
Node status | CPU, memory, disk via |
Cluster resources |
|
Proxmox is not harder than VirtualBox to set up from the MCP side — the hard part was always the Proxmox installation itself (Debian ISO, install, configure storage). But once Proxmox is running, plugging it into this MCP server is just those three env vars. We did the complicated part (the REST API client) so you don't have to.
Status: ✅ Supported as native Linux/WSL2 hypervisor backend
KVM (Kernel-based Virtual Machine) is the Linux-native Type-1 hypervisor. It is managed via libvirt and virsh via the libvirt_management portmanteau tool. Provides VM creation, domain lifecycle control, and snapshot management for Linux hosts and WSL2 environments.
Status: ❌ Not supported — enterprise scope
Nutanix AHV is a Type-1 hypervisor built into the Nutanix Acropolis hyperconverged infrastructure (HCI) platform. It is proprietary, licensed per-node as part of the Nutanix AOS/Prism bundle. AHV is KVM-based under the hood but managed exclusively through Nutanix Prism (UI and REST API). It competes with VMware vSphere in the enterprise HCI space and has been a major beneficiary of the Broadcom VMware exodus.
Reasons not currently supported:
Requires a Nutanix cluster — not available to individual developers or small shops
The Prism REST API is complex and targets infrastructure teams, not ad-hoc VM management
Licensing is per-node subscription, typically $5k–$20k/node/year depending on bundle
The open-source KVM layer underneath is not directly accessible when managed by Acropolis
AHV support would make sense for a future "fleet datacenter" tier that targets enterprise Nutanix customers, but it is out of scope for the current single-machine developer workflow.
Status: ❌ Not supported — DIY complexity
OpenStack is a set of open-source (Apache 2.0) projects that together provide infrastructure-as-a-service (Compute via Nova, Storage via Cinder/Swift, Networking via Neutron, Identity via Keystone, etc.). It is the de-facto open-source cloud platform, used by massive deployments (OVH, Rackspace, CERN, Walmart) and telcos.
Reasons not currently supported:
DIY infrastructure cost: OpenStack is free software, but operating it requires a cluster of bare-metal hosts, shared storage (Ceph/CEPH or SAN), and at least 3 controller nodes for HA. A minimal production deployment starts at 6–10 physical servers. There is no "OpenStack on a laptop" — even dev environments (DevStack, MicroStack, Kolla) need significant RAM and multiple VMs.
Operational complexity: OpenStack has 30+ core services. Upgrades are painful, networking (Neutron + OVS/OVN) is notoriously brittle, and troubleshooting requires deep knowledge of RabbitMQ, MySQL/Galera, and distributed system internals.
Wrong abstraction layer: This project manages individual VMs and sandboxes on a single Windows machine. OpenStack is a multi-tenant cloud orchestrator for datacenter-scale deployments. The API surface (Nova boot with flavors, networks, security groups, availability zones) is designed for a cloud operator, not a developer spinning up a single Ubuntu VM.
Alternatives exist: For those who want OpenStack-like capabilities at smaller scale, Proxmox VE provides a similar VM management API with 1% of the operational overhead. We may support Proxmox before OpenStack.
If you are running OpenStack in production and want MCP integration, the right approach is to run a lightweight MCP bridge on your OpenStack controller node that translates MCP tool calls to OpenStack REST API calls (nova, cinder, neutron). That bridge is not part of this repo but could be a separate openstack-mcp server.
Status: ⚠️ Not directly managed by this server, but adjacent
Kubernetes is a container orchestration platform that schedules and manages containerized workloads across a cluster of machines. It is not a VM hypervisor — it runs on top of one (Docker, containerd, CRI-O) — but it competes for the same "where do I run my workload?" mindshare.
Perception vs reality on complexity:
The conventional wisdom is that Kubernetes is too complex for individual developers. This is true for a manually-configured production cluster with etcd, CNI plugins, ingress controllers, cert-manager, service meshes, monitoring stacks, and persistent storage. Setting that up from scratch is a multi-day slog even for experienced ops teams.
However, the lightweight distributions have changed the calculus significantly:
Distribution | Install | Footprint | Use case |
k3s (Rancher) | Single binary, | ~50 MB, runs on a Raspberry Pi | Edge, IoT, dev clusters |
MicroK8s (Canonical) |
| ~200 MB, includes add-ons | Local dev, CI, offline |
kind (Kubernetes in Docker) |
| Container nodes | CI testing, ephemeral clusters |
minikube | Binary + driver | VM-based (Docker or Hyper-V) | Local development, learning |
K3d (k3s in Docker) |
| k3s clusters as Docker containers | Dev, CI, multi-node testing |
On a modern machine (16+ GB RAM, SSD), any of these can boot a functional Kubernetes cluster in under 5 minutes. The real time sink was always configuration — picking the right CNI, storage class, ingress, cert management — and this is precisely where AI assistance (Claude, ChatGPT, Codex) shines. An AI agent given "spin up a k3s cluster on this machine with Traefik, Longhorn, and cert-manager" can:
Install k3s (one-line curl pipe)
Write the Helm values or YAML manifests for each component
Apply them in dependency order
Verify the cluster is healthy
The total human effort is "type the prompt, review the plan, press enter." The AI handles the five years of Kubernetes tribal knowledge.
Why it's not directly managed by this server:
This project manages VMs (VirtualBox, Hyper-V) and sandboxes (Windows Sandbox). Kubernetes is a layer above — it expects a running cluster (on VMs or bare metal) and manages containers within it. The MCP server could expose kubectl wrappers (get pods, apply manifests, port-forward), but that is a separate project (kubernetes-mcp or similar). The local-llm-mcp server in the fleet already uses k3s internally for containerized model serving, proving the lightweight-Kubernetes-on-a-single-machine pattern works in production.
Bottom line: Kubernetes is complex, but AI makes the configuration pain disappear. The lightweight distros make the infrastructure cost near-zero. If you need container orchestration alongside VM management, run k3s on the same host and use a separate kubernetes-mcp server for kubectl access.
Technology | License | Cost | Type | Windows | Linux | macOS | API |
VirtualBox | GPLv2 | Free | Type-2 | ✅ | ✅ | ✅ |
|
Hyper-V | Proprietary | Windows license | Type-1 | ✅ | ❌ | ❌ | PowerShell |
Windows Sandbox | Proprietary | Windows Pro/Ent | Type-1 | ✅ | ❌ | ❌ | WSB XML |
Docker | Apache 2.0 | Free | Container | ✅ | ✅ | ✅ | Docker CLI/API |
VMware | Proprietary | Subscription | Type-1/2 | ✅ | ✅ | ✅ |
|
Proxmox VE | AGPLv3 | Free | Type-1 | ❌ | ✅ | ❌ | REST API |
KVM | GPLv2 | Free | Type-1 | ❌ | ✅ | ❌ |
|
Nutanix AHV | Proprietary | Per-node subscription | Type-1 | ❌ | ✅ | ❌ | REST API ( |
OpenStack | Apache 2.0 | Free (DIY infra cost) | Type-1 (KVM) | ❌ | ✅ | ❌ | REST API ( |
Kubernetes (k3s) | Apache 2.0 | Free | Orchestrator | ✅ | ✅ | ✅ |
|
Download virtualization-mcp-*.mcpb from Releases
Drag into Claude Desktop
Other methods: INSTALL.md
Create an Ubuntu 24.04 VM with 8 GB RAM and attach the ISO from assets.
Launch a consumer Windows Sandbox so I can test a naked INSTALL.md walkthrough.
Restore snapshot clean-base on NakedWin11 before the next install test.
Doc | Contents |
Options A–D, sandbox launchers | |
Env vars, VirtualBox paths | |
| |
Common errors | |
VM lifecycle, snapshots | |
Consumer vs dev bringup | |
System design |
Windows 11 Pro/Enterprise/Education for Hyper-V and Windows Sandbox
VirtualBox 7+ with VBoxManage on PATH (VM features)
Python 3.12+ — only for Options C/D
MIT
Comprehensive tool discovery and help portmanteau tool.
This tool consolidates application-specific help and introspection operations into a single interface. Provides information about available tools, their operations, and usage. Use the 'action' parameter to specify which operation to perform.
Note: This is separate from MCP protocol's native tools/list method. MCP clients get tool schemas automatically - this tool provides app-specific help content and detailed introspection for users.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | The operation to perform. Must be one of: - "list_tools": List all virtualization-mcp tools with their operations Returns both portmanteau and individual tools (in testing mode) Optional filters: category, search - "tool_info": Get detailed information about a specific tool (requires: tool_name) Returns: tool type, operations list, description - "tool_schema": Get JSON schema information for a tool's parameters (requires: tool_name) Returns: schema information and notes about Literal types Note: Actual schemas are auto-generated by FastMCP from type hints - "help": Get general help and server information (no parameters required) Returns: server version, tool modes, quick start info, documentation links | |
| search | No | Search term to filter tools by name or description (optional, for list_tools action only) | |
| category | No | Filter tools by category (optional, for list_tools action only). Valid values: "vm", "network", "snapshot", "storage", "system", "discovery", "hyperv" | |
| tool_name | No | Name of the tool to get info/schema for (required for tool_info, tool_schema actions). Valid values: vm_management, network_management, snapshot_management, storage_management, system_management, discovery_management, hyperv_management |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool consolidates operations, is separate from MCP, and behaves differently in testing mode. It also notes that schemas are auto-generated. However, it does not explicitly state that the tool is read-only or non-destructive, though that is implied by its nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured. It starts with a clear one-sentence summary, then uses bullet points under the 'action' parameter to detail each operation. The note at the end is important and doesn't waste words. Every sentence adds value, making it easy to scan for an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple actions, optional parameters, and an output schema), the description is remarkably complete. It covers the return values for each action, explains the testing mode distinction, and provides valid parameter values. No significant gaps are apparent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, but the description adds substantial value beyond the schema. For the 'action' parameter, it explains each enum value's purpose, required parameters, and what the tool returns. For optional parameters like 'category' and 'search', it provides valid values and usage context. This greatly aids an agent in selecting the correct action and parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states that the tool is a 'Comprehensive tool discovery and help portmanteau tool' that provides information about available tools, their operations, and usage. It clearly differentiates itself from the MCP protocol's native tools/list method, establishing a distinct purpose from sibling management tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: it explains that the tool is for app-specific help content and detailed introspection, separate from MCP's native tools/list. It also details each action and when to use them, including required parameters and optional filters. This clear differentiation helps an agent choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive network management portmanteau tool.
This tool consolidates all network operations into a single interface. Use the 'action' parameter to specify which operation to perform. Different actions require different parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| action | Yes | The operation to perform. Must be one of: - "list_networks": List all host-only networks (no other parameters required) - "create_network": Create a host-only network (requires: network_name) - "remove_network": Remove a host-only network (requires: network_name) - "list_adapters": List network adapters for a VM (requires: vm_name) - "configure_adapter": Configure network adapter for a VM (requires: vm_name, adapter_slot, network_type) | |
| offset | No | ||
| netmask | No | Network mask for network configuration (optional for create_network) | |
| vm_name | No | Name of the virtual machine (required for list_adapters, configure_adapter) | |
| ip_address | No | IP address for network configuration (optional for create_network) | |
| adapter_slot | No | Network adapter slot number 0-3 (required for configure_adapter) | |
| network_name | No | Name of the host-only network (required for create_network, remove_network, configure_adapter) | |
| network_type | No | Network type for adapter configuration (required for configure_adapter). Valid values: "nat", "bridged", "hostonly", "internal", "generic", "natnetwork" |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions actions but does not disclose behavioral traits such as which actions are destructive (e.g., remove) or require specific permissions. Lacks warnings about side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (two sentences) and to the point. It could be improved by front-loading the most important guidance, but it is still concise and readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description need not explain return values. However, given the complexity (5 actions, 9 parameters), the description is minimal and does not provide usage examples or clarify parameter dependencies beyond the action enum descriptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (78%) and the schema includes detailed descriptions for each action, especially for the 'action' parameter. The description adds little beyond 'Use the action parameter to specify which operation to perform', so it meets the baseline but does not exceed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a comprehensive network management tool consolidating all network operations, and lists the actions via the 'action' parameter. It distinguishes from sibling tools which are for other domains (info, sandbox, etc.). However, being a portmanteau tool reduces focus slightly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description instructs to use the 'action' parameter to specify the operation. Since it is the only network tool among siblings, no when-not-to-use is needed, but explicit context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
[RATIONALE] Consolidated Proxmox VE management — single tool for all remote Proxmox operations. Only available when PROXMOX_HOST is configured in the environment.
Operations:
list_vms List all QEMU VMs on the Proxmox node
start_vm Start a VM by VMID
stop_vm Hard-stop a VM by VMID
shutdown_vm ACPI shutdown a VM by VMID
status Get detailed status for a VM
create_snapshot Create a snapshot
list_snapshots List snapshots for a VM
delete_snapshot Delete a snapshot
node_status Get node CPU/memory/disk usage
cluster_resources List all cluster resources
{"success": bool, "message": str, "data": {...}}
proxmox_management(operation="list_vms") proxmox_management(operation="start_vm", vmid="100")
| Name | Required | Description | Default |
|---|---|---|---|
| iso | No | ||
| name | No | ||
| vmid | No | ||
| cores | No | ||
| memory | No | ||
| disk_size | No | 32G | |
| operation | Yes | ||
| description | No | ||
| snapshot_name | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It lists operations like stop_vm (hard-stop) and delete_snapshot, which are destructive, but does not flag them as such or mention authorization needs, side effects, or idempotency. The return format is given, but behavioral warnings are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with sections for operations, return format, and examples, which aids readability. However, it includes a redundant 'RATIONALE' label and is longer than necessary, containing some redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (9 parameters, multiple operations) and no schema descriptions, the description is incomplete. It adequately covers operations and return format but fails to explain non-trivial parameters like iso, cores, memory, and disk_size, leaving significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 9 parameters with 0% schema description coverage, so the description must explain all parameters. The description only covers 'operation' and 'vmid' via examples, leaving iso, name, cores, memory, disk_size, description, and snapshot_name unexplained. This severely limits parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it is a consolidated tool for all remote Proxmox operations and lists 10 specific operations, making the purpose clear. However, it does not directly differentiate from sibling tools like vm_management or snapshot_management, which may overlap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description only mentions the prerequisite of PROXMOX_HOST being configured but provides no guidance on when to use this consolidated tool versus the more specific sibling tools, nor any scenarios where the tool should not be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Docker-based code sandbox management for safe, isolated code execution.
Requires Docker Desktop running on the host. Two execution modes:
Ephemeral: throwaway container, auto-removed after run (execute_code, execute_file)
Stateful: persistent session, state preserved between calls (session_*)
| Name | Required | Description | Default |
|---|---|---|---|
| code | No | Code string to execute (for execute_code) | |
| image | No | Docker image for session_create (default: python:3.13-slim) | python:3.13-slim |
| action | Yes | Operation to perform. One of: --- EPHEMERAL (throwaway containers) --- - "execute_code": Run code snippet. Requires: code. Optional: language, timeout, network_enabled - "execute_file": Run a host file. Requires: host_path. Optional: language, timeout, network_enabled --- STATEFUL SESSIONS (persistent containers) --- - "session_create": Create session. Optional: image, sandbox_name - "session_run": Run command in session. Requires: sandbox_id, command - "session_write_file": Write file to session. Requires: sandbox_id, container_path, content - "session_read_file": Read file from session. Requires: sandbox_id, container_path - "session_list": List active sessions. No extra args needed. - "session_destroy": Remove session. Requires: sandbox_id | |
| command | No | Shell command for session_run | |
| content | No | File content string for session_write_file | |
| timeout | No | Max execution seconds (default: 30) | |
| language | No | "python" | "javascript" | "bash" (default: python) | python |
| host_path | No | Absolute Windows path to file (for execute_file) | |
| sandbox_id | No | Session ID from session_create (for session_* actions) | |
| sandbox_name | No | Optional container name for session_create | |
| container_path | No | Path inside container for file operations | |
| network_enabled | No | Allow outbound network in container (default: False) |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description effectively conveys behavioral traits: isolated execution, auto-removal for ephemeral, persistence for stateful sessions. It adds value by outlining the two execution modes and the requirement for Docker.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using just a few sentences to convey purpose, prerequisites, and execution modes. It front-loads the core function and avoids unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (12 parameters, many actions) and the presence of an output schema, the description provides sufficient high-level context. It covers the overall purpose, prerequisites, and mode classification, but could include a note on error behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description provides high-level context for parameters (like grouping actions) but does not add new semantic info beyond the already detailed schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is for Docker-based code sandbox management, specifying safe isolated execution. It explicitly distinguishes from sibling tools like info_tools or network_management by focusing on code execution and session management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a key prerequisite (Docker Desktop running) and explains when to use ephemeral vs stateful modes. However, it does not explicitly mention when not to use the tool or list alternatives among siblings, though the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive snapshot management portmanteau tool.
This tool consolidates all VM snapshot operations into a single interface. Use the 'action' parameter to specify which operation to perform. All actions require vm_name, and most require snapshot_name.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| action | Yes | The operation to perform. Must be one of: - "list": List all snapshots for a VM (requires: vm_name) - "create": Create a snapshot of a VM (requires: vm_name, snapshot_name) - "restore": Restore a VM to a snapshot (requires: vm_name, snapshot_name) - "delete": Delete a snapshot from a VM (requires: vm_name, snapshot_name) | |
| offset | No | ||
| vm_name | Yes | Name of the virtual machine (required for all actions) | |
| description | No | Optional description for the snapshot (only used for create action) | |
| snapshot_name | No | Name of the snapshot (required for create, restore, delete actions) |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavioral traits. It fails to mention that restore/delete are destructive, may cause downtime, or require VM power state conditions. This gap reduces transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loading purpose and structure. It is efficient, though the last sentence could be integrated into the parameter descriptions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's multi-action nature, the description is adequate but lacks prerequisities (e.g., VM existence, required states for restore) and does not leverage the presence of an output schema to clarify return types.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 67%, and the description adds minimal value beyond what the schema already provides (e.g., 'All actions require vm_name'). The description does not clarify semantics like default limits or edge cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as comprehensive snapshot management for VMs, consolidating multiple operations (list, create, restore, delete). It differentiates from sibling tools like vm_management by focusing solely on snapshots.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description instructs to use the 'action' parameter to specify operation, but does not contrast with sibling tools like vm_management, nor does it specify when not to use this tool (e.g., for non-snapshot VM tasks).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive storage management portmanteau tool.
This tool consolidates all storage operations into a single interface. Use the 'action' parameter to specify which operation to perform. Different actions require different parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| action | Yes | The operation to perform. Must be one of: - "list_controllers": List storage controllers for a VM (requires: vm_name) - "create_controller": Create a storage controller for a VM (requires: vm_name, controller_name, controller_type) - "remove_controller": Remove a storage controller from a VM (requires: vm_name, controller_name) - "list_disks": List virtual disks for a VM (requires: vm_name) - "create_disk": Create a new virtual disk (requires: disk_name, disk_size_gb) - "attach_disk": Attach a disk to a virtual machine (requires: vm_name, disk_path) | |
| offset | No | ||
| vm_name | No | Name of the virtual machine (required for list_controllers, create_controller, remove_controller, list_disks, attach_disk) | |
| disk_name | No | Name of the virtual disk file (required for create_disk) | |
| disk_path | No | Path to the disk file (required for attach_disk) | |
| disk_size_gb | No | Size of the disk in GB (required for create_disk) | |
| controller_name | No | Name of the storage controller (required for create_controller, remove_controller) | |
| controller_type | No | Type of storage controller (required for create_controller): ide|sata|scsi|sas|usb|pcie |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals that the tool is a portmanteau (multiplexed), but with no annotations, it fails to disclose behavioral traits like destructiveness, side effects, or required permissions. For a tool with destructive actions (e.g., remove_controller), this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but includes redundant listing of actions already in schema. The term 'portmanteau' adds clutter. Could be more efficient without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, conditional dependency on action), the description is too high-level. It doesn't explain parameter relationships, conditional requirements beyond the schema, or output behavior, even though an output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (78%) and the schema already provides detailed parameter descriptions. The description adds little beyond saying to use the 'action' parameter, so it meets baseline but doesn't compensate for remaining gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as a storage management portmanteau and lists operations, making it clear it handles storage tasks. However, the phrase 'portmanteau tool' is jargon and the purpose isn't stated with a single precise verb+resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for any storage operation, but it doesn't explicitly state when to use vs alternatives. Since there are no sibling storage tools, no exclusions are needed, but the description could be more direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive system management portmanteau tool.
This tool consolidates system information and diagnostics operations into a single interface. Use the 'action' parameter to specify which operation to perform. Most actions don't require vm_name.
| Name | Required | Description | Default |
|---|---|---|---|
| width | No | Optional screenshot width for action="screenshot" | |
| action | Yes | The operation to perform. Must be one of: - "host_info": Get host system information (no vm_name required) - "vbox_version": Get VirtualBox version information (no vm_name required) - "ostypes": List available OS types for VM creation (no vm_name required) - "metrics": Get VM performance metrics (requires: vm_name) - "screenshot": Take a screenshot of a running VM (requires: vm_name) | |
| height | No | Optional screenshot height for action="screenshot" | |
| vm_name | No | Name of the virtual machine (required only for metrics and screenshot actions) | |
| output_file | No | Optional screenshot output path for action="screenshot" |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description alone must disclose behavioral traits. It describes the tool as consolidating operations but does not mention side effects, destructive potential, or safety characteristics. The listed actions appear to be read-only, but this is not stated. More transparency is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The main description is only two sentences, clearly stating the tool's consolidated nature and how to use it. It is front-loaded and efficient. The term 'portmanteau' is concise though possibly jargon. Overall, no extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a multi-action tool, with an output schema and 100% parameter coverage, the description provides sufficient context. It highlights the action parameter and vm_name requirements. However, it could better explain the rationale for consolidation or when to use specific actions. Still, completeness is high.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds a summary about the action parameter and vm_name requirement, but this does not significantly exceed what the schema already explains. The tool description itself does not elaborate on individual parameters beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a 'comprehensive system management portmanteau tool' that consolidates system information and diagnostics. The list of actions via the 'action' parameter makes the purpose specific. It distinguishes from sibling tools like 'vm_management' which focus on VM operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises to use the 'action' parameter and notes that most actions don't require vm_name. However, it does not explicitly provide when-to-use or when-not-to-use guidance compared to sibling tools like 'info_tools'. The schema's action descriptions give per-action requirements, but no broader alternative comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Sampling-backed agentic operations for virtualization.
Actions:
suggest_config: Suggest VirtualBox VM settings for a use case via LLM sampling. Optional: use_case (e.g. 'CI runner', 'malware sandbox', 'dev environment')
sandbox_workflow: Generate a step-by-step plan for the spin-up → work → snapshot → tear-down safety pattern. Requires: goal (what dangerous/experimental work to do)
workflow: Autonomous multi-step VM orchestration goal. Requires: goal (natural language objective)
All actions use ctx.sample() when available; fall back to sensible defaults otherwise.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | ||
| action | Yes | ||
| vm_name | No | ||
| use_case | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that actions use ctx.sample() with fallback, but does not mention potential destructive side effects, authorization needs, rate limits, or safety implications of the workflow actions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with bullet points for each action, making it easy to read. It is relatively concise but could be slightly more succinct without losing key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (three actions, multiple parameters) and the existence of an output schema, the description provides a basic understanding but lacks details on behavioral outcomes, error handling, or prerequisites. It is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description adds meaning for the 'goal' and 'use_case' parameters (e.g., 'what dangerous/experimental work to do' for sandbox_workflow). However, it completely omits the 'vm_name' parameter from the description, leaving its purpose unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool provides sampling-backed agentic operations for virtualization, listing three actions with brief explanations. It distinguishes from siblings like vm_management by focusing on workflow orchestration rather than direct VM management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when agentic, sampling-based workflows are desired, but it does not explicitly state when to use this tool over siblings (e.g., vm_management for direct operations). It mentions fallback behavior but lacks explicit when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Virtual machine lifecycle management.
Actions: list, create, start, stop, delete, clone, reset, pause, resume, info. For LLM config suggestions or sandbox workflow planning use vm_agentic_workflow.
vm_name: required for all actions except list and clone. source_vm + new_vm_name: required for clone. os_type, memory_mb, disk_size_gb: required for create. Use system_management(action='ostypes') for valid os_type values.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| action | Yes | ||
| offset | No | ||
| os_type | No | ||
| vm_name | No | ||
| use_case | No | ||
| memory_mb | No | ||
| source_vm | No | ||
| new_vm_name | No | ||
| disk_size_gb | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the full behavioral burden. It lists actions but does not disclose side effects (e.g., irreversibility of delete), concurrency behavior, or authorization needs. The description focuses on parameter rules rather than broader behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficient, using one paragraph to cover purpose, actions, alternative tool, and parameter requirements. It front-loads the core purpose and lists actions clearly. Minor improvement could be using bullet points for readability, but the structure is logical and not verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 10 parameters, an output schema, and no schema descriptions, the description covers essential parameter combinations but omits explanation of pagination parameters (limit, offset) and the use_case field. The presence of an output schema reduces the need to explain return values, but the missing parameter descriptions lower completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It explains that vm_name is required for most actions, clone requires source_vm+new_vm_name, and create requires os_type/memory/disk. It also directs users to system_management for valid os_type values. However, use_case, limit, and offset are not described.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Virtual machine lifecycle management' and lists all supported actions (list, create, start, etc.), making the tool's purpose highly specific. It distinguishes itself from the sibling vm_agentic_workflow by explicitly directing LLM config and sandbox planning to that tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidelines on when to use vm_agentic_workflow instead, and explains parameter requirements per action (e.g., clone needs source_vm+new_vm_name, create needs os_type/memory/disk). However, it does not mention when to use sibling tools like snapshot_management or network_management, so some alternatives are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.3.1proxmox_managementv1.2.0info_toolsnetwork_managementsandbox_managementsnapshot_managementstorage_managementsystem_managementvm_agentic_workflowvm_managementTools are grouped by distinct domains (network, storage, VM lifecycle, etc.), but the portmanteau pattern centralizes multiple operations into one tool via an 'action' parameter, which could cause minor confusion. However, descriptions are clear and domains are well-separated.
Most tools follow a '_management' suffix pattern, but 'info_tools' and 'vm_agentic_workflow' deviate. The naming is not strictly verb_noun but is readable and predictable once the pattern is understood.
9 tools cover the major aspects of virtualization (VMs, storage, network, snapshots, sandbox, system info, Proxmox, agentic workflows). The count is well-scoped and each tool earns its place.
The tool set covers VM lifecycle, storage, network, snapshots, sandbox execution, and system diagnostics. Minor gaps exist (e.g., no dedicated tool for advanced network configuration), but core workflows are well-supported.
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If you are the server author, to access and configure the admin panel.
Provides capabilities that let LLM agents perform a range of infrastructure management tasks.
Validates AI infra code on real VMs. Self-corrects until it works. No containers, no sandboxes.
Your AI Agent's Infrastructure Layer. Connect Claude, Copilot, Codex, or ChatGPT to 200+ managed open source services. Start databases, pipelines, and applications through natural language.
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