hcloud-mcp-server
An MCP server that exposes the Huawei Cloud CLI as a single tool for AI assistants to discover and execute commands interactively via --help navigation, with built-in safety for destructive operations.
README
hcloud-mcp-server
An MCP (Model Context Protocol) server that exposes the Huawei Cloud CLI (hcloud) as a single tool that AI assistants can navigate interactively — just like a human would.
Usage Options
- MCP server: It maps the main hcloud functions as MCP functions and instructs for the use of --help in order to get the other functions, it uses hcloud cli in the background and as such, hcloud cli must be set up beforehand with
hcloud configure initand configure obs as well. It works faster than the CLI + Skill vertion - CLI + Skill: As stated by Microsoft: Modern coding agents increasingly favor CLI–based workflows exposed as SKILLs over MCP because CLI invocations are more token-efficient: they avoid loading large tool schemas and verbose accessibility trees into the model context, allowing agents to act through concise, purpose-built commands.
It is recommended to uses the CLI + Skill option, you'll only need to copy the skills folder to your tool's skills folder.
.opencode/skills or .claude/skills
How It Works
The server registers one tool: hcloud_cli. The AI navigates hcloud using --help at each level:
hcloud_cli("--help") → list all services
hcloud_cli("IAM --help") → list IAM operations
hcloud_cli("IAM ListCustomPolicies --help") → show parameters
hcloud_cli("IAM ListCustomPolicies") → execute
This mirrors how a human discovers and uses the CLI, requires only 1 tool definition in the LLM context (~300 tokens), and works within any model's context window.
Features
- Single tool — minimal context footprint, works with any model size
- Interactive discovery — AI navigates
--helpmenus naturally - Safety layer — destructive operations (Delete, Detach, Revoke, etc.) require
confirm=true; default is dry-run mode - Structured output — JSON output forced automatically (
--cli-output=json) - Configurable — timeout, binary path, safety settings
Requirements
- Python 3.10+
- hcloud CLI installed and configured with authentication
Installation
pip install hcloud-mcp-server
Or from source:
git clone <repo-url>
cd hcloud-mcp-server
pip install -e .
Verify:
hcloud-mcp --help
Running
# Start the MCP server (stdio transport)
hcloud-mcp
# With a config file
hcloud-mcp --config /path/to/config.json
# With debug logging
hcloud-mcp --debug
Configuration
Config file
Create a JSON config file (see config.example.json):
{
"execution": {
"timeout_seconds": 30,
"cli_output": "json",
"hcloud_binary": "hcloud"
}
}
Environment variables
| Variable | Description |
|---|---|
HCLOUD_MCP_CONFIG |
Path to config JSON file |
Tool Reference
hcloud_cli
| Parameter | Type | Required | Description |
|---|---|---|---|
command |
string | Yes | hcloud CLI arguments (everything after hcloud) |
confirm |
boolean | No | Set true to execute destructive operations (default: false = dry-run) |
Navigation flow:
command="--help"— list all available servicescommand="<Service> --help"— list operations for a servicecommand="<Service> <Operation> --help"— show parameters and descriptionscommand="<Service> <Operation> --param1=value1"— execute the operation
Parameter format: Always use --param=value (equals sign required by hcloud).
Safety: Operations containing Delete, Remove, Revoke, Detach, Disassociate, Cancel, or Force are classified as destructive. Without confirm=true, they run with --dryrun first.
Adding to AI Tools
The hcloud MCP server uses standard stdio transport, compatible with any MCP client.
opencode
Add to ~/.opencode/opencode.json:
{
"mcp": {
"hcloud": {
"type": "local",
"command": ["hcloud-mcp"],
"enabled": true
}
}
}
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"hcloud": {
"command": "hcloud-mcp",
"args": []
}
}
}
Cursor
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"hcloud": {
"command": "hcloud-mcp",
"args": []
}
}
}
VS Code (GitHub Copilot)
Add to .vscode/mcp.json:
{
"servers": {
"hcloud": {
"type": "stdio",
"command": "hcloud-mcp",
"args": []
}
}
}
Windsurf
Add to ~/.windsurf/mcp.json:
{
"mcpServers": {
"hcloud": {
"command": "hcloud-mcp",
"args": []
}
}
}
Goose
Add to ~/.config/goose/config.yaml:
extensions:
hcloud:
type: stdio
command: hcloud-mcp
args: []
Any MCP Client (generic)
| Setting | Value |
|---|---|
| Command | hcloud-mcp |
| Args | [] |
| Transport | stdio |
Example AI Session
User: How many custom IAM policies do I have?
AI: Let me check your IAM policies.
→ hcloud_cli(command="--help")
[sees IAM in the service list]
→ hcloud_cli(command="IAM --help")
[sees ListCustomPolicies in the operations list]
→ hcloud_cli(command="IAM ListCustomPolicies --help")
[sees no required parameters]
→ hcloud_cli(command="IAM ListCustomPolicies")
{ "total_number": 2, "roles": [...] }
AI: You have 2 custom IAM policies.
User: Delete the policy with ID abc123
AI: This is a destructive operation. Let me run it in dry-run mode first.
→ hcloud_cli(command="IAM DeleteCustomPolicy --role_id=abc123")
⚠️ DRY RUN — no changes made.
Set confirm=true to execute for real.
→ hcloud_cli(command="IAM DeleteCustomPolicy --role_id=abc123", confirm=true)
✅ Executed (confirmed destructive operation):
{ "message": "Delete success" }
AI: The policy has been deleted.
Development
# Install in editable mode
pip install -e .
# Run tests
pytest tests/ -v
# Run with debug logging
hcloud-mcp --debug
Troubleshooting
Server fails to start:
- Ensure
hcloudis installed and in PATH:which hcloud - Verify auth:
hcloud IAM ListCustomPolicies
Tool calls return errors:
- Check hcloud profile:
hcloud configure list - Enable debug logging:
hcloud-mcp --debug
AI doesn't know how to use the tool:
- The tool description includes navigation instructions
- Remind the AI: "Use hcloud_cli with --help to discover operations"
Recommended Servers
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.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.