Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"vmware-aiops": {
"env": {
"VMWARE_AIOPS_CONFIG": "/path/to/config.yaml"
},
"args": [
"--from",
"vmware-aiops",
"vmware-aiops-mcp"
],
"command": "uvx"
}
}
}Are you the author?
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AI-powered VMware vCenter/ESXi VM lifecycle and deployment tool — 31 tools across 6 categories.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y '@zw008/VMware-AIops' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked @zw008/VMware-AIops against OSV.dev.
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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-monitorvmware-storage Datastores, iSCSI, vSAN management uv tool install vmware-storagevmware-vks Tanzu Namespaces, TKC cluster lifecycle uv tool install vmware-vksNeed read-only monitoring only? Use VMware-Monitor — zero destructive code in the codebase.
Works with Claude Code, Cursor, Codex, Gemini CLI, Trae, and 30+ AI agents:
# Via Skills.sh
npx skills add zw008/VMware-AIops
# Via ClawHub
clawhub install vmware-aiops
# 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
# Add marketplace
/plugin marketplace add zw008/VMware-AIops
# Install plugin
/plugin install vmware-ops
# Use the skill
/vmware-ops:vmware-aiops
| 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 |
| 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.
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
| vSphere / VCF Version | Support | N