Virtualbox MCP Server

Virtualbox MCP Server

A Model Context Protocol server for managing Virtualbox and Vagrant virtual machines via a TypeScript interface. It enables users to programmatically create new VMs and monitor their status through simple JSON-RPC tools.

Category
Visit Server

README

πŸ–₯️ VirtualBox MCP Server

<div align="center">

TypeScript MCP SDK License: MIT Turborepo

A powerful Model Context Protocol (MCP) server for managing VirtualBox VMs via Vagrant.

AI agents can now provision, manage, and debug virtual development environments with full observability.

Features β€’ Quick Start β€’ Tools β€’ Workflows β€’ Examples β€’ Configuration

</div>


✨ Features

  • 38 MCP Tools for complete VM lifecycle management
  • Real-time Observability with logs, dashboards, and progress tracking
  • Snapshot Management for safe rollback and recovery
  • Process Control with kill/list capabilities
  • File Synchronization with conflict resolution
  • Async Operations with progress tracking and cancellation
  • System Guardrails for zombie VM detection and cleanup
  • Sequential Thinking for AI problem-solving

πŸ“¦ Architecture

Virtualbox-mcp-server/          # Turborepo Monorepo
β”œβ”€β”€ apps/
β”‚   └── mcp-server/             # Main MCP server (38 tools)
β”‚       └── src/
β”‚           β”œβ”€β”€ index.ts        # Tool definitions & handlers
β”‚           β”œβ”€β”€ error-handler.ts
β”‚           β”œβ”€β”€ port-manager.ts
β”‚           └── sequential-thinking.ts
β”œβ”€β”€ packages/
β”‚   β”œβ”€β”€ vagrant-client/         # Vagrant CLI wrapper
β”‚   β”œβ”€β”€ sync-engine/            # Chokidar + file sync
β”‚   └── shared-utils/           # Logger utilities
β”œβ”€β”€ turbo.json
└── package.json
flowchart TB
    subgraph AI["πŸ€– AI Agent"]
        Claude["Claude / GPT / Gemini"]
    end

    subgraph MCP["πŸ“‘ MCP Server"]
        McpServer["virtualbox-mcp-server"]
        Tools["38 Tools"]
        Thinking["Sequential Thinking"]
    end

    subgraph Packages["πŸ“¦ Internal Packages"]
        VC["vagrant-client"]
        SE["sync-engine"]
        SU["shared-utils"]
    end

    subgraph VM["πŸ’» VirtualBox / Vagrant"]
        VM1["dev-vm"]
        VM2["test-vm"]
        VM3["prod-replica"]
    end

    Claude -->|"MCP JSON-RPC"| McpServer
    McpServer --> Tools
    McpServer --> Thinking
    Tools --> VC
    Tools --> SE
    VC --> SU
    SE --> SU
    VC -->|"vagrant up/ssh"| VM1
    VC -->|"vagrant ssh"| VM2
    VC -->|"vagrant status"| VM3

πŸš€ Quick Start

Prerequisites

  • Node.js 18+
  • VirtualBox 6.x or 7.x
  • Vagrant 2.3+

Installation

# Clone the repository
git clone https://github.com/usemanusai/Virtualbox-mcp-server.git
cd Virtualbox-mcp-server

# Install dependencies
npm install

# Build all packages
npm run build

Running the Server

# Start the MCP server
npm run start --workspace=virtualbox-mcp-server

# Or directly
node apps/mcp-server/dist/index.js

πŸ› οΈ All 38 Tools

VM Lifecycle (6 tools)

Tool Description
create_vm Create a new Vagrant VM
create_dev_vm Create VM with full config (CPU, memory, ports, sync)
ensure_dev_vm Start or create VM if not exists
get_vm_status Get VM state
list_vms List all VMs
destroy_vm Destroy VM (force)

Execution (3 tools)

Tool Description
exec_command Execute command in VM (with timeout)
exec_with_sync Execute with rsync before/after
run_background_task Run nohup background task

Environment Setup (3 tools)

Tool Description
setup_dev_environment Install runtimes (node, python, go, etc.)
install_dev_tools Install tools (git, docker, nginx, etc.)
configure_shell Configure aliases and env vars

File Operations (7 tools)

Tool Description
upload_file Upload file to VM
search_files Grep search in VM
configure_sync Configure file watcher
sync_to_vm Rsync host→VM
sync_from_vm Rsync VM→host
sync_status Get sync state
resolve_conflict Resolve sync conflicts

πŸ‘οΈ Observability (3 tools)

Tool Description
tail_vm_log Read last N lines of a log file (e.g., /var/log/syslog)
get_task_output Get stdout/stderr of background tasks
grep_log_stream Search for patterns in log files

πŸ“Έ Snapshots (4 tools)

Tool Description
snapshot_save Create named snapshot before risky operations
snapshot_restore Revert to a specific snapshot
snapshot_list List all available snapshots
snapshot_delete Delete a specific snapshot

βš™οΈ Process Control (2 tools)

Tool Description
list_processes Return structured list of running processes (ps aux)
kill_process Send SIGTERM/SIGKILL to a process

🌐 Network (1 tool)

Tool Description
check_vm_port Verify if port is listening in VM & accessible from host

πŸ“Š Dashboard (1 tool)

Tool Description
get_vm_dashboard Comprehensive dashboard: CPU, RAM, Disk, tasks, logs

⏳ Progress Awareness (5 tools)

Tool Description
start_download Start tracked download, returns operation_id
get_operation_progress Get real-time progress (bytes, %, ETA)
wait_for_operation Block until operation completes or times out
cancel_operation Cancel a running operation
list_active_operations List all active operations

πŸ›‘οΈ Guardrails & Maintenance (2 tools)

Tool Description
scan_system_health Check disk/memory, identify Zombie VMs
cleanup_zombies Safely destroy orphaned VMs (with dry-run option)

🧠 AI Reasoning (1 tool)

Tool Description
sequentialthinking Dynamic problem-solving with reflection & branching

πŸ”„ Architectural Workflows

🟒 Easy Workflows

1. The "Daily Standup" (Environment Prep)

Quickly bring a dev environment online and ensure it's ready:

flowchart LR
    A["ensure_dev_vm"] --> B["sync_to_vm"]
    B --> C["install_dev_tools"]
    C --> D["get_vm_dashboard"]
  1. ensure_dev_vm β€” Boot or create the VM automatically
  2. sync_to_vm β€” Push local code changes to VM
  3. install_dev_tools β€” Verify tools are present
  4. get_vm_dashboard β€” Confirm VM is healthy

2. The "Dataset Fetch" (Async Download)

Download large files without blocking:

flowchart LR
    A["start_download"] --> B["wait_for_operation"]
    B --> C["search_files"]
  1. start_download β€” Initiate download, get operation_id
  2. wait_for_operation β€” Block until download completes
  3. search_files β€” Verify file exists at expected path

3. The "Service Pulse" (Basic Debugging)

Quickly diagnose why localhost:8080 isn't loading:

flowchart LR
    A["list_vms"] --> B["check_vm_port"]
    B --> C["tail_vm_log"]
  1. list_vms β€” Identify which VM handles the service
  2. check_vm_port β€” Check if app is listening (vs. port forwarding issue)
  3. tail_vm_log β€” Pull last 50 lines of error log

πŸ”΄ Advanced Workflows

4. The "Safety First" Update (Transactional Rollback)

Apply risky updates with a safety net:

flowchart TB
    A["snapshot_save"] --> B["start_download"]
    B --> C["wait_for_operation"]
    C --> D["exec_command"]
    D --> E{"check_vm_port"}
    E -->|"❌ Failed"| F["snapshot_restore"]
    E -->|"βœ… Success"| G["snapshot_delete"]
  1. snapshot_save β€” Create checkpoint "pre-update-v2"
  2. start_download β€” Download new binary/patch
  3. wait_for_operation β€” Block until complete
  4. exec_command β€” Run installation script
  5. check_vm_port β€” Verify service is back online
    • IF FAILED: snapshot_restore to rollback
    • IF SUCCESS: snapshot_delete to clean up

5. The "Resource Reclamation" (System Hygiene)

Identify and clean up orphaned Zombie VMs:

flowchart LR
    A["scan_system_health"] --> B["sequentialthinking"]
    B --> C["cleanup_zombies"]
    C --> D["get_vm_dashboard"]
  1. scan_system_health β€” Identify Zombie VMs consuming resources
  2. sequentialthinking β€” Analyze which are safe to delete
  3. cleanup_zombies β€” Safely terminate with dry_run first
  4. get_vm_dashboard β€” Confirm resources are freed

6. The "Deep Fix" Loop (Intelligent Debugging)

Autonomous diagnosis and repair of stuck processes:

flowchart TB
    A["get_vm_dashboard"] --> B["list_processes"]
    B --> C["grep_log_stream"]
    C --> D["sequentialthinking"]
    D --> E["kill_process"]
    E --> F["exec_with_sync"]
  1. get_vm_dashboard β€” Detect CPU spike or stuck task
  2. list_processes β€” Find the specific PID causing issues
  3. grep_log_stream β€” Search logs for error signature
  4. sequentialthinking β€” Formulate hypothesis
  5. kill_process β€” Send SIGTERM to stuck process
  6. exec_with_sync β€” Upload patched config and restart

πŸ’¬ Example Prompts

Here are 30 natural language prompts with their corresponding tool calls:

<details> <summary><strong>VM Lifecycle Examples</strong></summary>

1. Provision a New Database Server

"I need a fresh Redis server. Create a VM named 'redis-cache' using the 'hashicorp/bionic64' box."

{
  "name": "create_vm",
  "arguments": {
    "name": "redis-cache",
    "box": "hashicorp/bionic64"
  }
}

2. Status Check

"Is the 'frontend-react' VM currently running?"

{
  "name": "get_vm_status",
  "arguments": {
    "name": "frontend-react"
  }
}

3. Inventory Overview

"Show me a list of all the virtual machines we are currently managing."

{
  "name": "list_vms",
  "arguments": {}
}

4. Force Termination

"The 'experiment-01' VM is completely unresponsive. Destroy it immediately."

{
  "name": "destroy_vm",
  "arguments": {
    "name": "experiment-01"
  }
}

5. Idempotent Environment Check

"Ensure the 'integration-test' VM is running. If it's not there, create it."

{
  "name": "ensure_dev_vm",
  "arguments": {
    "name": "integration-test"
  }
}

</details>

<details> <summary><strong>Execution Examples</strong></summary>

6. Install Dependencies

"Run pip install -r requirements.txt inside the 'api-server' VM."

{
  "name": "exec_command",
  "arguments": {
    "vm_name": "api-server",
    "command": "pip install -r requirements.txt"
  }
}

7. Build and Sync

"Sync the latest changes to 'builder' and then run make build immediately."

{
  "name": "exec_with_sync",
  "arguments": {
    "vm_name": "builder",
    "command": "make build"
  }
}

8. Long-Running Job

"Start the data ingestion script (python ingest.py) on 'data-lake' in the background."

{
  "name": "run_background_task",
  "arguments": {
    "vm_name": "data-lake",
    "command": "python ingest.py"
  }
}

</details>

<details> <summary><strong>File Operations Examples</strong></summary>

9. Deploy Configuration

"Upload my local .env.production file to /app/.env on the 'worker-node' VM."

{
  "name": "upload_file",
  "arguments": {
    "vm_name": "worker-node",
    "source": ".env.production",
    "destination": "/app/.env"
  }
}

10. Locate Error Logs

"Search for any files named error.log inside the /var/log directory."

{
  "name": "search_files",
  "arguments": {
    "vm_name": "monitor",
    "query": "error.log",
    "path": "/var/log"
  }
}

11. Setup File Watcher

"Configure a file sync. Map my local ./src folder to /usr/src/app on 'dev-main'."

{
  "name": "configure_sync",
  "arguments": {
    "vm_name": "dev-main",
    "host_path": "./src",
    "guest_path": "/usr/src/app",
    "direction": "bidirectional"
  }
}

12. Conflict Resolution

"There's a sync conflict on README.md. Use my local version."

{
  "name": "resolve_conflict",
  "arguments": {
    "vm_name": "docs-site",
    "file_path": "README.md",
    "resolution": "use_host"
  }
}

</details>

<details> <summary><strong>Observability Examples</strong></summary>

13. Live Debugging

"Show me the last 50 lines of the nginx error log on the 'proxy' VM."

{
  "name": "tail_vm_log",
  "arguments": {
    "vm_name": "proxy",
    "path": "/var/log/nginx/error.log",
    "lines": 50
  }
}

14. Monitor Background Task

"What is the output so far for task task_12345?"

{
  "name": "get_task_output",
  "arguments": {
    "vm_name": "data-lake",
    "task_id": "task_12345"
  }
}

15. Search Logs

"Search the syslog on 'auth-service' for any 'segfault' errors."

{
  "name": "grep_log_stream",
  "arguments": {
    "vm_name": "auth-service",
    "path": "/var/log/syslog",
    "pattern": "segfault"
  }
}

</details>

<details> <summary><strong>Snapshot Examples</strong></summary>

16. Pre-Update Backup

"I'm about to upgrade the database. Save a snapshot called 'before-v14-upgrade'."

{
  "name": "snapshot_save",
  "arguments": {
    "vm_name": "postgres-primary",
    "snapshot_name": "before-v14-upgrade"
  }
}

17. Disaster Recovery

"The upgrade failed! Restore to the 'before-v14-upgrade' snapshot."

{
  "name": "snapshot_restore",
  "arguments": {
    "vm_name": "postgres-primary",
    "snapshot_name": "before-v14-upgrade"
  }
}

18. List Snapshots

"What snapshots are available for the 'kafka-broker' VM?"

{
  "name": "snapshot_list",
  "arguments": {
    "vm_name": "kafka-broker"
  }
}

</details>

<details> <summary><strong>Process Control Examples</strong></summary>

19. Investigate High Load

"The 'ml-trainer' VM is slow. List the running processes."

{
  "name": "list_processes",
  "arguments": {
    "vm_name": "ml-trainer"
  }
}

20. Kill Stuck Process

"Process ID 9982 is stuck on 'worker-01'. Kill it."

{
  "name": "kill_process",
  "arguments": {
    "vm_name": "worker-01",
    "pid": 9982,
    "signal": "SIGKILL"
  }
}

</details>

<details> <summary><strong>Network & Dashboard Examples</strong></summary>

21. Check Service Availability

"Is port 8080 open and listening on the 'jenkins' VM?"

{
  "name": "check_vm_port",
  "arguments": {
    "vm_name": "jenkins",
    "guest_port": 8080
  }
}

22. System Health Dashboard

"Get me a full dashboard with CPU, RAM, and disk usage."

{
  "name": "get_vm_dashboard",
  "arguments": {
    "vm_name": "production-replica"
  }
}

</details>

<details> <summary><strong>Progress & Download Examples</strong></summary>

23. Initiate Large Download

"Download the 10GB dataset from example.com to /data/ on the 'ai-model' VM."

{
  "name": "start_download",
  "arguments": {
    "vm_name": "ai-model",
    "url": "http://example.com/data.tar.gz",
    "destination": "/data/data.tar.gz"
  }
}

24. Blocking Wait

"Wait for the download operation op_5592 to finish."

{
  "name": "wait_for_operation",
  "arguments": {
    "operation_id": "op_5592",
    "timeout_seconds": 600
  }
}

</details>

<details> <summary><strong>System Maintenance Examples</strong></summary>

25. Detect Zombie VMs

"Scan the system to see if we have any orphaned Zombie VMs."

{
  "name": "scan_system_health",
  "arguments": {}
}

26. Clean Zombies (Dry Run)

"Check what would happen if we cleaned up 'zombie-1' and 'old-test'."

{
  "name": "cleanup_zombies",
  "arguments": {
    "vm_names": ["zombie-1", "old-test"],
    "dry_run": true
  }
}

</details>


βš™οΈ MCP Configuration

Claude Desktop / Cline / Cursor

Add to your claude_desktop_config.json or mcp_config.json:

{
  "mcpServers": {
    "vagrant-mcp": {
      "command": "node",
      "args": ["C:\\path\\to\\Virtualbox-mcp-server\\apps\\mcp-server\\dist\\index.js"],
      "env": {
        "LOG_LEVEL": "info",
        "PATH": "C:\\Program Files (x86)\\Vagrant\\bin;C:\\Program Files\\Oracle\\VirtualBox;%PATH%"
      }
    }
  }
}

πŸ”₯ Top 5 MCP Configuration Examples

1. Development Environment (Node.js + Docker)

{
  "mcpServers": {
    "vagrant-mcp": {
      "command": "node",
      "args": ["/home/user/Virtualbox-mcp-server/apps/mcp-server/dist/index.js"],
      "env": {
        "LOG_LEVEL": "debug",
        "VAGRANT_HOME": "/home/user/.vagrant.d",
        "VM_DEFAULT_BOX": "ubuntu/jammy64",
        "VM_DEFAULT_MEMORY": "4096",
        "VM_DEFAULT_CPU": "4"
      }
    }
  }
}

2. CI/CD Pipeline (Jenkins/GitHub Actions)

{
  "mcpServers": {
    "vagrant-mcp": {
      "command": "node",
      "args": ["/opt/mcp/vagrant-mcp-server/dist/index.js"],
      "env": {
        "LOG_LEVEL": "warn",
        "VAGRANT_HOME": "/var/lib/jenkins/.vagrant.d",
        "VM_AUTO_DESTROY": "true",
        "VM_SNAPSHOT_BEFORE_TEST": "true"
      }
    }
  }
}

3. Windows Workstation

{
  "mcpServers": {
    "vagrant-mcp": {
      "command": "node.exe",
      "args": ["C:\\Users\\Developer\\mcp\\Virtualbox-mcp-server\\apps\\mcp-server\\dist\\index.js"],
      "env": {
        "LOG_LEVEL": "info",
        "PATH": "C:\\Program Files (x86)\\Vagrant\\bin;C:\\Program Files\\Oracle\\VirtualBox;C:\\Windows\\System32",
        "VAGRANT_HOME": "C:\\Users\\Developer\\.vagrant.d"
      }
    }
  }
}

4. macOS with Homebrew

{
  "mcpServers": {
    "vagrant-mcp": {
      "command": "/opt/homebrew/bin/node",
      "args": ["/Users/dev/Projects/Virtualbox-mcp-server/apps/mcp-server/dist/index.js"],
      "env": {
        "LOG_LEVEL": "info",
        "PATH": "/opt/homebrew/bin:/usr/local/bin:/usr/bin",
        "VAGRANT_HOME": "/Users/dev/.vagrant.d"
      }
    }
  }
}

5. Production/Enterprise (Restricted Environment)

{
  "mcpServers": {
    "vagrant-mcp": {
      "command": "node",
      "args": ["/srv/mcp/vagrant-server/dist/index.js"],
      "env": {
        "LOG_LEVEL": "error",
        "VAGRANT_HOME": "/srv/vagrant",
        "VM_MAX_COUNT": "10",
        "VM_ALLOWED_BOXES": "company/base-ubuntu,company/base-centos",
        "VM_REQUIRE_SNAPSHOT": "true",
        "GUARDRAILS_STRICT": "true"
      }
    }
  }
}

πŸ§ͺ Development

# Watch mode (rebuild on changes)
npm run dev

# Lint
npm run lint

# Format
npm run format

πŸ“ License

MIT Β© usemanusai


<div align="center">

Made with ❀️ for AI-powered infrastructure management

⬆ Back to Top

</div>

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured