io.github.zw008/vmware-aiops

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by zw008 · Python

VMware AIops

AI-powered VMware vCenter/ESXi VM lifecycle and deployment with 31 MCP tools.

Automates VMware VM lifecycle and deployment using AI-powered operations.

Topics

agent-skillsaiopsclaude-codecodexdevopsesxigemini-cliinfrastructuremcppyvmomivmwarevsphereautomationmonitoringvcenterread-only-monitoringai-skillhomelabvm-lifecycle

README.md41 KBView on GitHub

VMware AIops

Author: Wei Zhou, VMware by Broadcom — wei-wz.zhou@broadcom.com This is a community-driven project by a VMware engineer, not an official VMware product. For official VMware developer tools see developer.broadcom.com.

English | 中文

AI-powered VMware vCenter/ESXi VM lifecycle and deployment tool — 31 tools across 6 categories.

Companion skills handle everything else:

Skill Scope Install
vmware-monitor Read-only: inventory, health, alarms, events, metrics uv tool install vmware-monitor
vmware-storage Datastores, iSCSI, vSAN management uv tool install vmware-storage
vmware-vks Tanzu Namespaces, TKC cluster lifecycle uv tool install vmware-vks

Need read-only monitoring only? Use VMware-Monitor — zero destructive code in the codebase.

Quick Install (Recommended)

Works with Claude Code, Cursor, Codex, Gemini CLI, Trae, and 30+ AI agents:

bash

# Via Skills.sh
npx skills add zw008/VMware-AIops

# Via ClawHub
clawhub install vmware-aiops

PyPI Install (No GitHub Access Required)

bash

# Install via uv (recommended)
uv tool install vmware-aiops

# Or via pip
pip install vmware-aiops

# China mainland mirror (faster)
pip install vmware-aiops -i https://pypi.tuna.tsinghua.edu.cn/simple

Claude Code Plugin Install

bash

# Add marketplace
/plugin marketplace add zw008/VMware-AIops

# Install plugin
/plugin install vmware-ops

# Use the skill
/vmware-ops:vmware-aiops

Why this over other VMware MCP servers

Most open-source VMware MCP servers (e.g. bright8192/esxi-mcp-server, giuliolibrando/vmware-vsphere-mcp-server) are single-vCenter VM wrappers: list/power/snapshot a VM, basic monitoring, a confirm=True flag. They explicitly do not cover networking, storage, Kubernetes, ops analytics, load balancing, or compliance — and "logging is documented" is not an audit trail.

This is one skill in an 11-package family that covers the whole estate and runs every tool through a governed harness:

	Other VMware MCP servers	This family
VM lifecycle + monitoring	✅	✅
NSX networking (segments/gateways/NAT/routing/IPAM)	❌	✅ vmware-nsx
NSX security (DFW/groups/IDS-IPS/traceflow)	❌	✅ vmware-nsx-security
Storage (datastore/iSCSI/vSAN)	❌	✅ vmware-storage
Tanzu Kubernetes (Supervisor/Namespace/TKC)	❌	✅ vmware-vks
Aria Operations (metrics/alerts/capacity)	❌	✅ vmware-aria
AVI / NSX ALB load balancing + AKO	❌	✅ vmware-avi
Compliance baselines + drift (CIS/SCG/等保/PCI)	❌	✅ vmware-harden
Governed harness (unified audit, policy engine, token budget + runaway breaker, graduated risk tiers, undo-token, prompt-injection sanitize)	❌	✅ vmware-policy on every tool

If you only ever power-cycle VMs in one vCenter, a single-file server is fine. If you run a real (regulated, NSX-segmented, multi-domain) VMware estate and need an AI operator an auditor can sign off on, that's what this family is for — see docs/compliance-ready.md.

Capabilities Overview

What This Skill Does

Category	Tools	Count
VM Lifecycle	power on/off, TTL auto-delete, clean slate	6
Deployment	OVA, template, linked clone, batch clone/deploy	8
Guest Ops	exec commands, upload/download files, provision	5
Plan/Apply	multi-step planning with rollback	4
Cluster	create, delete, HA/DRS config, add/remove hosts	6
Datastore	browse files, scan for images	2

CLI vs MCP: Which Mode to Use

Scenario	Recommended	Why
Local/small models (Ollama, Qwen <32B)	CLI	~2K tokens context vs ~10K for MCP; small models struggle with many tool schemas
Token-sensitive workflows	CLI	SKILL.md + Bash tool = minimal overhead
Cloud models (Claude, GPT-4o)	Either	Both work; MCP gives structured JSON I/O
Automated pipelines / Agent chaining	MCP	Type-safe parameters, structured output, no shell parsing
Monitoring / storage / K8s	Companion skills	See vmware-monitor, vmware-storage, vmware-vks

Rule of thumb: Use CLI for cost efficiency and small models. Use MCP for structured automation with large models.

Architecture

code

User (Natural Language)
  ↓
AI CLI Tool (Claude Code / Gemini / Codex / Aider / Continue / Trae / Kimi)
  ↓ reads SKILL.md / AGENTS.md / rules
  ↓
vmware-aiops CLI
  ↓ pyVmomi (vSphere SOAP API)
  ↓
vCenter Server ──→ ESXi Cluster ──→ VM
    or
ESXi Standalone Host ──→ VM

Version Compatibility

vSphere / VCF Version	Support	Notes
VCF 9.1 / vSphere 9.1	✅ Full	Released 2026-05-12. pyVmomi `<10.0` resolves and connects via SOAP; new REST-only features (`PATCH /deployment/size`, IPv6-only GOSC) not yet wrapped — see VCF Python SDK for those.
VCF 9.0 / vSphere 9.0	✅ Full	pyVmomi 8.0.3+ connects against vSphere 9 SOAP API. From VCF 9, pyVmomi is also bundled inside the unified VCF Python SDK.
8.0 / 8.0U1-U3	✅ Full	`CreateSnapshot_Task` deprecated → use `CreateSnapshotEx_Task`
7.0 / 7.0U1-U3	✅ Full	All APIs supported
6.7	✅ Compatible	Backward-compatible, tested
6.5	✅ Compatible	Backward-compatible, tested

pyVmomi auto-negotiates the API version during SOAP handshake — no manual configuration needed. The same codebase manages 7.0 / 8.0 / 9.0 / 9.1 environments seamlessly.

Official Broadcom References

SDKs: https://developer.broadcom.com/sdks — VCF Python SDK (recommended for VCF 9+, bundles pyVmomi + vSAN SDK), vSphere Automation SDK for Python (REST APIs)
REST APIs: https://developer.broadcom.com/xapis — vSphere Automation API, VCF API, SDDC Manager API
CLI Tools: https://developer.broadcom.com/tools — PowerCLI 9.1, ESXCLI, OVF Tool

Common Workflows

Deploy a Lab Environment

Browse datastore for OVA images → vmware-aiops datastore browse <ds> --pattern "*.ova"
Deploy VM from OVA → vmware-aiops deploy ova ./image.ova --name lab-vm --datastore ds1
Install software inside VM → vmware-aiops vm guest-exec lab-vm --cmd /bin/bash --args "-c 'apt-get install -y nginx'" --user root
Create baseline snapshot → vmware-aiops vm snapshot-create lab-vm --name baseline
Set TTL for auto-cleanup → vmware-aiops vm set-ttl lab-vm --minutes 480

Batch Clone for Testing

Create plan: vm_create_plan with multiple clone + reconfigure steps
Review plan with user (shows affected VMs, irreversible warnings)
Apply: vm_apply_plan executes sequentially, stops on failure
If failed: vm_rollback_plan reverses executed steps
Set TTL on all clones for auto-cleanup

Migrate VM to Another Host

Check VM info via vmware-monitor → verify power state and current host
Migrate: vmware-aiops vm migrate my-vm --to-host esxi-02
Verify migration completed

VM Lifecycle

Operation	Command	Confirmation	vCenter	ESXi
Power On	`vm power-on <name>`	—	✅	✅
Graceful Shutdown	`vm power-off <name>`	Double	✅	✅
Force Power Off	`vm power-off <name> --force`	Double	✅	✅
Reset	`vm reset <name>`	—	✅	✅
Suspend	`vm suspend <name>`	—	✅	✅
Create VM	`vm create <name> --cpu --memory --disk`	—	✅	✅
Delete VM	`vm delete <name>`	Double	✅	✅
Reconfigure	`vm reconfigure <name> --cpu --memory`	Double	✅	✅
Create Snapshot	`vm snapshot-create <name> --name <snap>`	—	✅	✅
List Snapshots	`vm snapshot-list <name>`	—	✅	✅
Revert Snapshot	`vm snapshot-revert <name> --name <snap>`	—	✅	✅
Delete Snapshot	`vm snapshot-delete <name> --name <snap> [--no-wait]`	—	✅	✅
Task Status	`vm task-status <task-id>`	—	✅	✅
Clone VM	`vm clone <name> --new-name <new>`	—	✅	✅
vMotion	`vm migrate <name> --to-host <host>`	—	✅	❌
Set TTL	`vm set-ttl <name> --minutes <n>`	—	✅	✅
Cancel TTL	`vm cancel-ttl <name>`	—	✅	✅
List TTLs	`vm list-ttl`	—	✅	✅
Clean Slate	`vm clean-slate <name> [--snapshot baseline]`	Double	✅	✅
Guest Exec	`vm guest-exec <name> --cmd /bin/bash --args "..."`	—	✅	✅
Guest Exec (with output)	`vm guest-exec-output <name> --cmd "df -h"`	—	✅	✅
Guest Upload	`vm guest-upload <name> --local f.sh --guest /tmp/f.sh`	—	✅	✅
Guest Download	`vm guest-download <name> --guest /var/log/syslog --local ./syslog`	—	✅	✅

Guest Operations require VMware Tools running inside the guest OS. guest-exec-output auto-detects Linux/Windows shell and captures stdout/stderr.

Plan → Apply (Multi-step Operations)

For complex operations involving 2+ steps or 2+ VMs, use the plan/apply workflow instead of executing individually:

Step	What Happens
1. Create Plan	AI calls `vm_create_plan` — validates actions, checks targets in vSphere, generates plan with rollback info
2. Review	AI shows plan to user: steps, affected VMs, irreversible warnings
3. Apply	`vm_apply_plan` executes sequentially; stops on failure
4. Rollback (if failed)	Asks user whether to rollback, then `vm_rollback_plan` reverses executed steps (irreversible steps skipped)

Plans stored in ~/.vmware-aiops/plans/, auto-deleted on success, auto-cleaned after 24h.

VM Deployment & Provisioning

Operation	Command	Speed	vCenter	ESXi
Deploy from OVA	`deploy ova <path> --name <vm>`	Minutes	✅	✅
Deploy from Template	`deploy template <tmpl> --name <vm>`	Minutes	✅	✅
Linked Clone	`deploy linked-clone --source <vm> --snapshot <snap> --name <new>`	Seconds	✅	✅
Attach ISO	`deploy iso <vm> --iso "[ds] path/to.iso"`	Instant	✅	✅
Convert to Template	`deploy mark-template <vm>`	Instant	✅	✅
Batch Clone	`deploy batch-clone --source <vm> --count <n>`	Minutes	✅	✅
Batch Deploy (YAML)	`deploy batch spec.yaml`	Auto	✅	✅

Cluster Management

Operation	Command	Confirmation	vCenter	ESXi
Cluster Info	`cluster info <name>`	—	✅	❌
Create Cluster	`cluster create <name> [--ha] [--drs]`	—	✅	❌
Delete Cluster	`cluster delete <name>`	Double	✅	❌
Add Host	`cluster add-host <cluster> --host <host>`	Double	✅	❌
Remove Host	`cluster remove-host <cluster> --host <host>`	Double	✅	❌
Configure HA/DRS	`cluster configure <name> [--ha/--no-ha] [--drs/--no-drs]`	Double	✅	❌

remove-host requires the host to be in maintenance mode first; the host is moved out of the cluster into the datacenter's host folder as a standalone host.

Alarm Management

Operation	Command	Confirmation	vCenter	ESXi
List Triggered Alarms	`alarm list [--target <t>]`	—	✅	❌
Acknowledge Alarm	`alarm acknowledge <entity> <alarm>`	—	✅	❌
Clear (Reset) Alarms	`alarm reset <entity> <alarm>`	Double	✅	❌

Blast radius: vSphere has no per-alarm clear API. alarm reset uses AlarmManager.ClearTriggeredAlarms, which clears all triggered alarms matching the named alarm's entity type (host/VM/all) and current status (red/yellow) — not just the named one. The named alarm is looked up first (typos fail fast), and the output's scope field reports exactly what was cleared. Cleared alarms re-trigger automatically if their underlying condition persists.

Datastore Browser

Feature	vCenter	ESXi	Details
Browse Files	✅	✅	List files/folders in any datastore path
Scan Images	✅	✅	Discover ISO, OVA, OVF, VMDK across all datastores

Scheduled Scanning & Notifications

Feature	Details
Daemon	APScheduler-based, configurable interval (default 15 min)
Multi-target Scan	Sequentially scan all configured vCenter/ESXi targets
Scan Content	Alarms + Events + Host logs (hostd, vmkernel, vpxd)
Log Analysis	Regex pattern matching: error, fail, critical, panic, timeout, corrupt
Structured Log	JSONL output to `~/.vmware-aiops/scan.log`
Webhook	Slack, Discord, or any HTTP endpoint
Daemon Management	`daemon start/stop/status`, PID file, graceful shutdown

Safety Features

Feature	Details
Dry-Run Mode	`--dry-run` on any destructive command prints exact API calls without executing
Plan → Confirm → Execute → Log	Structured workflow: show current state, confirm changes, execute, audit log
Double Confirmation	All destructive ops (power-off, delete, reconfigure, snapshot-revert/delete, clone, migrate) require 2 sequential confirmations — no bypass flags
Rejection Logging	Declined confirmations are recorded in the audit trail
Audit Trail	All operations logged to `~/.vmware-aiops/audit.log` (JSONL) with before/after state
Input Validation	VM name, CPU (1-128), memory (128-1048576 MB), disk (1-65536 GB) validated
Password Protection	`.env` file loading with permission check; never in shell history
SSL Self-signed Support	`disableSslCertValidation` — only for ESXi with self-signed certs in isolated labs; production should use CA-signed certificates
Prompt Injection Protection	vSphere event messages and host logs are truncated, stripped of control characters, and wrapped in boundary markers before output
Webhook Data Scope	Sends notifications to user-configured URLs only — no third-party services by default
Task Waiting	All async operations wait for completion and report result
State Validation	Pre-operation checks (VM exists, power state correct)

vCenter vs ESXi Comparison

Capability	vCenter	ESXi Standalone
vMotion migration	✅	❌
Cross-host clone	✅	❌
Cluster management	✅	❌
All VM lifecycle ops	✅	✅
OVA/Template/Linked Clone deploy	✅	✅
Datastore browsing & image scan	✅	✅
Snapshots	✅	✅
Guest operations	✅	✅

Inventory, alarms, events, sensors, host services, and scanning are now in vmware-monitor.

Troubleshooting

"VM not found" error

VM names are case-sensitive in vSphere. Use exact name from vmware-monitor inventory vms.

Guest exec returns empty output

Use vm_guest_exec_output instead of vm_guest_exec — it auto-captures stdout/stderr. Basic vm_guest_exec only returns exit code.

Deploy OVA times out

Large OVA files (>10GB) may exceed the default 120s timeout. The upload happens via HTTP NFC lease — ensure network between the machine running vmware-aiops and ESXi is stable.

Plan apply fails mid-way

Run vmware-aiops plan list to see failed plan status. Ask user if they want to rollback with vm_rollback_plan. Irreversible steps (delete_vm) are skipped during rollback.

Connection refused / SSL error

Verify target is reachable: vmware-aiops doctor
For self-signed certs: set disableSslCertValidation: true in config.yaml (lab environments only)

Supported AI Platforms

Platform	Status	Config File	AI Model
Claude Code	✅ Native Skill	`skills/vmware-aiops/SKILL.md`	Anthropic Claude
Gemini CLI	✅ Extension	`gemini-extension/GEMINI.md`	Google Gemini
OpenAI Codex CLI	✅ Skill + AGENTS.md	`codex-skill/AGENTS.md`	OpenAI GPT
Aider	✅ Conventions	`codex-skill/AGENTS.md`	Any (cloud + local)
Continue CLI	✅ Rules	`codex-skill/AGENTS.md`	Any (cloud + local)
Trae IDE	✅ Rules	`trae-rules/project_rules.md`	Claude/DeepSeek/GPT-4o/Doubao
Kimi Code CLI	✅ Skill	`kimi-skill/SKILL.md`	Moonshot Kimi
MCP Server	✅ MCP Protocol	`mcp_server/`	Any MCP client
Python CLI	✅ Standalone	N/A	N/A

Platform Comparison

Feature	Claude Code	Gemini CLI	Codex CLI	Aider	Continue	Trae IDE	Kimi CLI
Cloud AI	Anthropic	Google	OpenAI	Any	Any	Multi	Moonshot
Local models	—	—	—	Ollama	Ollama	—	—
Skill system	SKILL.md	Extension	SKILL.md	—	Rules	Rules	SKILL.md
MCP support	Native	Native	Via Skills	Third-party	Native	—	—
Free tier	—	60 req/min	—	Self-hosted	Self-hosted	—	—

MCP Server Integrations

The vmware-aiops MCP server works with any MCP-compatible agent or tool. Ready-to-use configuration templates are in examples/mcp-configs/.

Agent / Tool	Local Model Support	Config Template	Integration Guide
Goose	✅ Ollama, LM Studio	`goose.json`	Guide
LocalCowork	✅ Fully offline	`localcowork.json`	Guide
mcp-agent	✅ Ollama, vLLM	`mcp-agent.yaml`	Guide
VS Code Copilot	—	`vscode-copilot.json`	Guide
Cursor	—	`cursor.json`	Guide
Continue	✅ Ollama	`continue.yaml`	Guide
Claude Code	—	`claude-code.json`	—

Fully local operation (no cloud API required):

bash

# Aider + Ollama + vmware-aiops (via AGENTS.md)
aider --conventions codex-skill/AGENTS.md --model ollama/qwen2.5-coder:32b

# Any MCP agent + local model + vmware-aiops MCP server
# See examples/mcp-configs/ for your agent's config format

Installation

Step 0: Prerequisites

bash

# Python 3.10+ required
python3 --version

# Node.js 18+ required for Gemini CLI and Codex CLI
node --version

Step 1: Clone & Install Python Backend

All platforms share the same Python backend.

bash

git clone https://github.com/zw008/VMware-AIops.git
cd VMware-AIops
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Step 2: Configure

bash

mkdir -p ~/.vmware-aiops
cp config.example.yaml ~/.vmware-aiops/config.yaml
# Edit config.yaml with your vCenter/ESXi targets

Set passwords via .env file (recommended):

bash

# Use the template
cp .env.example ~/.vmware-aiops/.env

# Edit and fill in your passwords, then lock permissions
chmod 600 ~/.vmware-aiops/.env

Security note: Prefer .env file over command-line export to avoid passwords appearing in shell history. The .env file should have chmod 600 (owner-only read/write).

Password environment variable naming convention:

code

VMWARE_{TARGET_NAME_UPPER}_PASSWORD
# Replace hyphens with underscores, UPPERCASE
# Example: target "home-esxi" → VMWARE_HOME_ESXI_PASSWORD
# Example: target "prod-vcenter" → VMWARE_PROD_VCENTER_PASSWORD

Security Best Practices

NEVER hardcode passwords in scripts or config files
NEVER pass passwords as command-line arguments (visible in ps)
ALWAYS use ~/.vmware-aiops/.env with chmod 600
ALWAYS configure connections via config.yaml — credentials are loaded from .env automatically
Config File Contents: config.yaml stores target hostnames, ports, and a reference to the .env file. It does not contain passwords or tokens. All secrets are stored exclusively in .env
TLS: Enabled by default. Disable only for ESXi hosts with self-signed certificates in isolated lab environments
Webhook: Disabled by default. When enabled, sends monitoring summaries to your own configured URL only — payloads contain no credentials, IPs, or PII, only aggregated alert metadata. No data sent to third-party services
Least Privilege: Use a dedicated vCenter service account with minimal permissions. For monitoring-only use cases, prefer the read-only VMware-Monitor
Prompt Injection Protection: All vSphere-sourced content is truncated, stripped of control characters, and wrapped in boundary markers before output
Code Review: We recommend reviewing the source code and commit history before deploying in production
Production Safety: For production environments, use the read-only VMware-Monitor instead. AI agents can misinterpret context and execute unintended destructive operations — real-world incidents have shown that AI-driven infrastructure tools without proper isolation can delete production databases and entire environments. VMware-Monitor eliminates this risk at the code level: no destructive functions exist in its codebase

Step 3: Connect Your AI Tool

Choose one (or more) of the following:

Option A: Claude Code (Marketplace)

Method 1: Marketplace (recommended)

In Claude Code, run:

code

/plugin marketplace add zw008/VMware-AIops
/plugin install vmware-ops

Then use:

code

/vmware-ops:vmware-aiops
> Show me all VMs on esxi-lab.example.com

Method 2: Local install

bash

# Clone and symlink
git clone https://github.com/zw008/VMware-AIops.git
ln -sf $(pwd)/VMware-AIops ~/.claude/plugins/marketplaces/vmware-aiops

# Register marketplace
python3 -c "
import json, pathlib
f = pathlib.Path.home() / '.claude/plugins/known_marketplaces.json'
d = json.loads(f.read_text()) if f.exists() else {}
d['vmware-aiops'] = {
    'source': {'source': 'github', 'repo': 'zw008/VMware-AIops'},
    'installLocation': str(pathlib.Path.home() / '.claude/plugins/marketplaces/vmware-aiops')
}
f.write_text(json.dumps(d, indent=2))
"

# Enable plugin
python3 -c "
import json, pathlib
f = pathlib.Path.home() / '.claude/settings.json'
d = json.loads(f.read_text()) if f.exists() else {}
d.setdefault('enabledPlugins', {})['vmware-ops@vmware-aiops'] = True
f.write_text(json.dumps(d, indent=2))
"

Restart Claude Code, then:

code

/vmware-ops:vmware-aiops

Submit to Official Marketplace

This plugin can also be submitted to the Anthropic official plugin directory for public discovery.

Option B: Gemini CLI

bash

# Install Gemini CLI
npm install -g @google/gemini-cli

# Install the extension from the cloned repo
gemini extensions install ./gemini-extension

# Or install directly from GitHub
# gemini extensions install https://github.com/zw008/VMware-AIops

Then start Gemini CLI:

code

gemini
> Show me all VMs on my ESXi host

Option C: OpenAI Codex CLI

bash

# Install Codex CLI
npm i -g @openai/codex
# Or on macOS:
# brew install --cask codex

# Copy skill to Codex skills directory
mkdir -p ~/.codex/skills/vmware-aiops
cp codex-skill/SKILL.md ~/.codex/skills/vmware-aiops/SKILL.md

# Copy AGENTS.md to project root
cp codex-skill/AGENTS.md ./AGENTS.md

Then start Codex CLI:

bash

codex --enable skills
> List all VMs on my ESXi

Option D: Aider (supports local models)

bash

# Install Aider
pip install aider-chat

# Install Ollama for local models (optional)
# macOS:
brew install ollama
ollama pull qwen2.5-coder:32b

# Run with cloud API
aider --conventions codex-skill/AGENTS.md

# Or with local model via Ollama
aider --conventions codex-skill/AGENTS.md \
  --model ollama/qwen2.5-coder:32b

Option E: Continue CLI (supports local models)

bash

# Install Continue CLI
npm i -g @continuedev/cli

# Copy rules file
mkdir -p .continue/rules
cp codex-skill/AGENTS.md .continue/rules/vmware-aiops.md

Configure ~/.continue/config.yaml for local model:

yaml

models:
  - name: local-coder
    provider: ollama
    model: qwen2.5-coder:32b

Then:

bash

cn
> Check ESXi health and alarms

Option F: Trae IDE

Copy the rules file to your project's .trae/rules/ directory:

bash

mkdir -p .trae/rules
cp trae-rules/project_rules.md .trae/rules/project_rules.md

Trae IDE's Builder Mode reads .trae/rules/ Markdown files at startup.

Note: You can also install Claude Code extension in Trae IDE and use .claude/skills/ format directly.

Option G: Kimi Code CLI

bash

# Copy skill file to Kimi skills directory
mkdir -p ~/.kimi/skills/vmware-aiops
cp kimi-skill/SKILL.md ~/.kimi/skills/vmware-aiops/SKILL.md

Option H: MCP Server (Smithery / Glama / Claude Desktop)

The MCP server exposes VMware operations as tools via the Model Context Protocol. Works with any MCP-compatible client (Claude Desktop, Cursor, etc.).

After uv tool install vmware-aiops, start the MCP server with one command (v1.5.15+):

bash

# Recommended — single command, no network re-resolve
vmware-aiops mcp

# With a custom config path
VMWARE_AIOPS_CONFIG=/path/to/config.yaml vmware-aiops mcp

Claude Desktop config (claude_desktop_config.json):

json

{
  "mcpServers": {
    "vmware-aiops": {
      "command": "vmware-aiops",
      "args": ["mcp"],
      "env": {
        "VMWARE_AIOPS_CONFIG": "/path/to/config.yaml"
      }
    }
  }
}

<details> <summary>Alternative: uvx (no install) or legacy entry point</summary>

bash

# Run without installing (requires PyPI access each launch)
uvx --from vmware-aiops vmware-aiops mcp

# Legacy entry point (still works, kept for backward compatibility)
vmware-aiops-mcp

Behind a corporate TLS proxy? uvx may fail with invalid peer certificate: UnknownIssuer. Use the recommended vmware-aiops mcp form above (no network needed), or set UV_NATIVE_TLS=true.

</details>

Install via Smithery:

bash

npx -y @smithery/cli install @zw008/VMware-AIops --client claude

Option I: Standalone CLI (no AI)

bash

# Already installed in Step 1
source .venv/bin/activate

vmware-aiops vm power-on my-vm --target home-esxi
vmware-aiops deploy ova ./ubuntu.ova --name my-vm --target home-esxi
vmware-aiops datastore browse datastore1 --target home-esxi

Update / Upgrade

Already installed? Re-run the install command for your channel to get the latest version:

Install Channel	Update Command
ClawHub	`clawhub install vmware-aiops`
Skills.sh	`npx skills add zw008/VMware-AIops`
Claude Code Plugin	`/plugin marketplace add zw008/VMware-AIops`
Git clone	`cd VMware-AIops && git pull origin main && uv pip install -e .`
uv	`uv tool install vmware-aiops --force`

Check your current version: vmware-aiops --version

Chinese Cloud Models

For users in China who prefer domestic cloud APIs or have limited access to overseas services.

DeepSeek

Cost-effective, strong coding capability.

bash

# Set DeepSeek API key (get from https://platform.deepseek.com)
export DEEPSEEK_API_KEY="your-key"

# Run with Aider
aider --conventions codex-skill/AGENTS.md \
  --model deepseek/deepseek-coder

Persistent config ~/.aider.conf.yml:

yaml

model: deepseek/deepseek-coder
conventions: codex-skill/AGENTS.md

Qwen (Alibaba Cloud)

Alibaba Cloud's coding model, free tier available.

bash

# Set DashScope API key (get from https://dashscope.console.aliyun.com)
export DASHSCOPE_API_KEY="your-key"

aider --conventions codex-skill/AGENTS.md \
  --model qwen/qwen-coder-plus

Or via OpenAI-compatible endpoint:

bash

export OPENAI_API_BASE="https://dashscope.aliyuncs.com/compatible-mode/v1"
export OPENAI_API_KEY="your-dashscope-key"

aider --conventions codex-skill/AGENTS.md \
  --model qwen-coder-plus-latest

Doubao (ByteDance)

bash

export OPENAI_API_BASE="https://ark.cn-beijing.volces.com/api/v3"
export OPENAI_API_KEY="your-ark-key"

aider --conventions codex-skill/AGENTS.md \
  --model your-doubao-endpoint-id

With Continue CLI

Configure ~/.continue/config.yaml:

yaml

# DeepSeek
models:
  - name: deepseek-coder
    provider: openai-compatible
    apiBase: https://api.deepseek.com/v1
    apiKey: your-deepseek-key
    model: deepseek-coder

# Qwen
models:
  - name: qwen-coder
    provider: openai-compatible
    apiBase: https://dashscope.aliyuncs.com/compatible-mode/v1
    apiKey: your-dashscope-key
    model: qwen-coder-plus-latest

Local Models (Aider + Ollama)

For fully offline operation — no cloud API, no internet, full privacy.

Aider + Ollama + local Qwen/DeepSeek is ideal for air-gapped environments.

Step 1: Install Ollama

bash

# macOS
brew install ollama

# Linux — download from https://ollama.com/download and install manually
# See https://github.com/ollama/ollama for platform-specific instructions

Step 2: Pull a model

Model	Command	Size	Note
Qwen 2.5 Coder 32B	`ollama pull qwen2.5-coder:32b`	~20GB	Best local coding model
Qwen 2.5 Coder 7B	`ollama pull qwen2.5-coder:7b`	~4.5GB	Low-memory option
DeepSeek Coder V2	`ollama pull deepseek-coder-v2`	~8.9GB	Strong reasoning
CodeLlama 34B	`ollama pull codellama:34b`	~19GB	Meta coding model

Hardware: 32B → ~20GB VRAM (or 32GB RAM for CPU). 7B → 8GB RAM.

Step 3: Run with Aider

bash

pip install aider-chat
ollama serve

# Aider + local Qwen (recommended)
aider --conventions codex-skill/AGENTS.md \
  --model ollama/qwen2.5-coder:32b

# Aider + local DeepSeek
aider --conventions codex-skill/AGENTS.md \
  --model ollama/deepseek-coder-v2

# Low-memory option
aider --conventions codex-skill/AGENTS.md \
  --model ollama/qwen2.5-coder:7b

Persistent config ~/.aider.conf.yml:

yaml

model: ollama/qwen2.5-coder:32b
conventions: codex-skill/AGENTS.md

Local Architecture

code

User → Aider CLI → Ollama (localhost:11434) → Qwen / DeepSeek local model
  │                                                    ↓
  │                                          reads AGENTS.md instructions
  │                                                    ↓
  └──────────────────────────────→ vmware-aiops CLI ──→ ESXi / vCenter

Tip: Local models are fully offline — perfect for air-gapped environments or strict data compliance.

CLI Reference

bash

# Diagnostics
vmware-aiops doctor                   # Check environment, config, connectivity
vmware-aiops doctor --skip-auth       # Skip vSphere auth check (faster)

# MCP Config Generator
vmware-aiops mcp-config generate --agent goose        # Generate config for Goose
vmware-aiops mcp-config generate --agent claude-code  # Generate config for Claude Code
vmware-aiops mcp-config list                          # List all supported agents

# VM operations
vmware-aiops vm power-on my-vm                                 # Power on
vmware-aiops vm power-off my-vm                                # Graceful shutdown (2x confirm)
vmware-aiops vm power-off my-vm --force                        # Force power off (2x confirm)
vmware-aiops vm create my-new-vm --cpu 4 --memory 8192 --disk 100  # Create VM
vmware-aiops vm delete my-vm --confirm                         # Delete VM (2x confirm)
vmware-aiops vm reconfigure my-vm --cpu 4 --memory 8192        # Reconfigure (2x confirm)
vmware-aiops vm snapshot-create my-vm --name "before-upgrade"  # Create snapshot
vmware-aiops vm snapshot-list my-vm                            # List snapshots
vmware-aiops vm snapshot-revert my-vm --name "before-upgrade"  # Revert snapshot
vmware-aiops vm snapshot-delete my-vm --name "before-upgrade"  # Delete snapshot (waits ≤30 min for consolidation)
vmware-aiops vm snapshot-delete my-vm --name "old-big" --no-wait  # Fire async, return a task id
vmware-aiops vm task-status task-1234                          # Poll an async task by id
vmware-aiops vm clone my-vm --new-name my-vm-clone             # Clone VM
vmware-aiops vm migrate my-vm --to-host esxi-02                # vMotion
vmware-aiops vm set-ttl my-vm --minutes 60                     # Auto-delete in 60 min
vmware-aiops vm cancel-ttl my-vm                               # Cancel TTL
vmware-aiops vm list-ttl                                       # Show all TTLs
vmware-aiops vm clean-slate my-vm --snapshot baseline          # Revert to baseline (2x confirm)

# Guest Operations (requires VMware Tools in guest)
vmware-aiops vm guest-exec my-vm --cmd /bin/bash --args "-c 'whoami'" --user root
vmware-aiops vm guest-upload my-vm --local ./script.sh --guest /tmp/script.sh --user root
vmware-aiops vm guest-download my-vm --guest /var/log/syslog --local ./syslog.txt --user root

# Plan → Apply (multi-step operations)
vmware-aiops plan list                                        # List pending/failed plans

# Deploy
vmware-aiops deploy ova ./ubuntu.ova --name my-vm --datastore ds1      # Deploy from OVA
vmware-aiops deploy template golden-ubuntu --name new-vm               # Deploy from template
vmware-aiops deploy linked-clone --source base-vm --snapshot clean --name test-vm  # Linked clone (seconds)
vmware-aiops deploy iso my-vm --iso "[datastore1] iso/ubuntu-22.04.iso"  # Attach ISO
vmware-aiops deploy mark-template golden-vm                            # Convert VM to template
vmware-aiops deploy batch-clone --source base-vm --count 5 --prefix lab  # Batch clone
vmware-aiops deploy batch deploy.yaml                                  # Batch deploy from YAML spec

# Cluster
vmware-aiops cluster info my-cluster                                   # Cluster details (HA/DRS status)
vmware-aiops cluster create my-cluster --ha --drs                      # Create cluster with HA+DRS
vmware-aiops cluster delete my-cluster                                 # Delete cluster (2x confirm)
vmware-aiops cluster add-host my-cluster --host esxi-03                # Add host to cluster (2x confirm)
vmware-aiops cluster remove-host my-cluster --host esxi-03             # Remove host (2x confirm)
vmware-aiops cluster configure my-cluster --ha --drs                   # Configure HA/DRS (2x confirm)

# Alarm management
vmware-aiops alarm list                                                # List triggered alarms
vmware-aiops alarm acknowledge esxi-01 "Host memory usage"             # Acknowledge alarm
vmware-aiops alarm reset esxi-01 "Host memory usage"                   # Clear alarms (2x confirm; clears ALL matching entity type + status)

# Datastore (browse and scan only — iSCSI/vSAN moved to vmware-storage)
vmware-aiops datastore browse datastore1 --path "iso/"                 # Browse datastore
vmware-aiops datastore scan-images --target home-esxi                  # Scan all datastores for images

# Scan
vmware-aiops scan now              # One-time scan

# Daemon
vmware-aiops daemon start          # Start scanner
vmware-aiops daemon status         # Check status
vmware-aiops daemon stop           # Stop daemon

# Companion skills for other operations:
#   vmware-monitor: inventory, alarms, events, sensors
#   vmware-storage: datastores, iSCSI, vSAN
#   vmware-vks:     Tanzu/TKC cluster lifecycle

Configuration

See config.example.yaml for all options.

Section	Key	Default	Description
targets	name	—	Friendly name
targets	host	—	vCenter/ESXi hostname or IP
targets	type	vcenter	`vcenter` or `esxi`
targets	port	443	Connection port
targets	verify_ssl	false	SSL certificate verification
scanner	interval_minutes	15	Scan frequency
scanner	severity_threshold	warning	Min severity: critical/warning/info
scanner	lookback_hours	1	How far back to scan
scanner	log_types	[vpxd, hostd, vmkernel]	Log sources
notify	log_file	~/.vmware-aiops/scan.log	JSONL log output
notify	webhook_url	—	Webhook endpoint (Slack, Discord, etc.)

Project Structure

code

VMware-AIops/
├── .claude-plugin/                # Claude Code marketplace manifest
│   └── marketplace.json
├── plugins/                       # Claude Code plugin
│   └── vmware-ops/
│       ├── .claude-plugin/
│       │   └── plugin.json
│       └── skills/
│           └── vmware-aiops/
│               └── SKILL.md       # Full operations skill
├── skills/                        # Skills index (npx skills add)
│   └── vmware-aiops/
│       ├── SKILL.md               # Slimmed-down skill (progressive disclosure)
│       └── references/            # Detailed docs loaded on-demand
│           ├── capabilities.md    # Full capabilities tables
│           ├── cli-reference.md   # Complete CLI reference
│           └── setup-guide.md     # Install, security, AI platforms
├── vmware_aiops/                  # Python backend
│   ├── config.py                  # YAML + .env config
│   ├── connection.py              # Multi-target pyVmomi
│   ├── cli.py                     # Typer CLI (double confirm)
│   ├── ops/                       # Operations
│   │   ├── inventory.py           # VMs, hosts, datastores, clusters
│   │   ├── health.py              # Alarms, events, sensors
│   │   ├── vm_lifecycle.py        # VM CRUD, snapshots, clone, migrate
│   │   ├── vm_deploy.py           # OVA, template, linked clone, batch deploy
│   │   └── datastore_browser.py   # Datastore browsing, image discovery
│   ├── scanner/                   # Log scanning daemon
│   └── notify/                    # Notifications (JSONL + webhook)
├── gemini-extension/              # Gemini CLI extension
│   ├── gemini-extension.json
│   └── GEMINI.md
├── codex-skill/                   # Codex + Aider + Continue
│   ├── SKILL.md
│   └── AGENTS.md
├── trae-rules/                    # Trae IDE rules
│   └── project_rules.md
├── kimi-skill/                    # Kimi Code CLI skill
│   └── SKILL.md
├── mcp_server/                    # MCP server wrapper
│   ├── server.py                  # FastMCP server with tools
│   └── __main__.py
├── smithery.yaml                  # Smithery marketplace config
├── RELEASE_NOTES.md
├── config.example.yaml
└── pyproject.toml

API Coverage

Built on pyVmomi (vSphere Web Services API / SOAP).

API Object	Usage
`vim.VirtualMachine`	VM lifecycle, snapshots, clone, migrate
`vim.HostSystem`	ESXi host info, sensors, services
`vim.Datastore`	Storage capacity, type, accessibility
`vim.host.DatastoreBrowser`	File browsing, image discovery (ISO/OVA/VMDK)
`vim.OvfManager`	OVA import and deployment
`vim.ClusterComputeResource`	Cluster, DRS, HA
`vim.Network`	Network listing
`vim.alarm.AlarmManager`	Active alarm monitoring
`vim.event.EventManager`	Event/log queries

Skill	Scope	Tools	Install
vmware-monitor	Read-only monitoring, alarms, events	8	`uv tool install vmware-monitor`
vmware-aiops	VM lifecycle, deployment, guest ops, cluster, datastore browse	31	`uv tool install vmware-aiops`
vmware-storage	Datastores, iSCSI, vSAN	11	`uv tool install vmware-storage`
vmware-vks	Tanzu Namespaces, TKC cluster lifecycle	20	`uv tool install vmware-vks`

Troubleshooting & Contributing

If you encounter any errors or issues, please send the error message, logs, or screenshots to zhouwei008@gmail.com. Contributions are welcome — feel free to join us in maintaining and improving this project!

License

MIT

Install

claude_desktop_config.json

{
  "mcpServers": {
    "vmware-aiops": {
      "command": "uvx",
      "args": [
        "vmware-aiops"
      ]
    }
  }
}

Skill	Scope	Install
vmware-monitor	Read-only: inventory, health, alarms, events, metrics	`uv tool install vmware-monitor`
vmware-storage	Datastores, iSCSI, vSAN management	`uv tool install vmware-storage`
vmware-vks	Tanzu Namespaces, TKC cluster lifecycle	`uv tool install vmware-vks`

Topics

VMware AIops

Quick Install (Recommended)

PyPI Install (No GitHub Access Required)

Claude Code Plugin Install

Why this over other VMware MCP servers

Capabilities Overview

What This Skill Does

CLI vs MCP: Which Mode to Use

Architecture

Version Compatibility

Official Broadcom References

Common Workflows

Deploy a Lab Environment

Batch Clone for Testing

Migrate VM to Another Host

VM Lifecycle

Plan → Apply (Multi-step Operations)

VM Deployment & Provisioning

Cluster Management

Alarm Management

Datastore Browser

Scheduled Scanning & Notifications

Safety Features

vCenter vs ESXi Comparison

Troubleshooting

"VM not found" error

Guest exec returns empty output

Deploy OVA times out

Plan apply fails mid-way

Connection refused / SSL error

Supported AI Platforms

Platform Comparison

MCP Server Integrations

Installation

Step 0: Prerequisites

Step 1: Clone & Install Python Backend

Step 2: Configure

Security Best Practices

Step 3: Connect Your AI Tool

Option A: Claude Code (Marketplace)

Option B: Gemini CLI

Option C: OpenAI Codex CLI

Option D: Aider (supports local models)

Option E: Continue CLI (supports local models)

Option F: Trae IDE

Option G: Kimi Code CLI

Option H: MCP Server (Smithery / Glama / Claude Desktop)

Option I: Standalone CLI (no AI)

Update / Upgrade

Chinese Cloud Models

DeepSeek

Qwen (Alibaba Cloud)

Doubao (ByteDance)

With Continue CLI

Local Models (Aider + Ollama)

Step 1: Install Ollama

Step 2: Pull a model

Step 3: Run with Aider

Local Architecture

CLI Reference

Configuration

Project Structure

API Coverage

Related Projects

Troubleshooting & Contributing

License

Install