ssh-alias-mcp

# ssh-alias-mcp

English | 中文  ·  SKILL  ·  REFERENCE  ·  CLI  ·  INSTALL

AI-driven server operations tool. Configure servers in YAML, execute commands, deploy scripts, and manage files — all through a unified interface for AI Agents and CLI.

✨ Highlights

• One config, three shells — bash, cmd, powershell auto-adapted via command templates

• Alias system — One YAML line = one AI skill, auto-exposed as MCP tools

• MCP + CLI — Same config, same connection pool, shared by AI and humans

Related MCP server: cygnus-ssh-mcp

Quick Start

# AI Agent (MCP)
claude mcp add ssh-alias-mcp python <path>/mcp_server.py

# CLI
python cli.py my-server run "uptime"
python cli.py my-server alias deploy

Documentation

• SKILL.md — AI Agent skill definition (MCP tools, quick reference)

• REFERENCE.md — Server configuration, aliases, security, file transfer

• CLI_USAGE.md — CLI commands reference

• INSTALL.md — AI Agent installation guide

Agent Setup in One Sentence (Copy It To Your Agents)

Read https://github.com/leehom0123/ssh-alias-mcp/blob/main/INSTALL.md — set up the SKILL and install the MCP service as described.

Real-World Scenarios

Scenario 1: Deploy to 5 servers in parallel

# _shared/common.yml
aliases:
  - name: deploy
    script: deploy.sh
    sudo: true

AI workflow: You say "deploy to all prod servers" → AI reads server list → calls ssh_run, ssh_run_alias, or a dynamic alias tool such as ssh_alias.prod-01.deploy on each server in parallel → reports result. No SSH boilerplate, no password prompts.

Scenario 2: AI analyzes crash cause

aliases:
  - name: crash-check
    inline: "journalctl -xe --since '1 hour ago' && dmesg -T | tail -100 && free -h && df -h /"

Before: SSH into server → manually check logs → search for kernel panic → analyze core dump → hours later After: Tell AI "server crashed, check why" → AI calls ssh_alias.prod-01.crash-check → analyzes logs → identifies OOM killer → suggests fix

Scenario 3: Emergency troubleshooting

aliases:
  - name: check
    inline: "docker logs --tail 50 my-app && df -h / && free -h"

Before: Open terminal → SSH → type commands → copy output → analyze AI workflow: You say "my app is slow, check it" → AI uses ssh_run, ssh_run_alias, or a dynamic alias tool to grab logs and metrics → identifies bottleneck → suggests fix

Scenario 4: Cross-platform deployment

# Linux server
server:
  host: "192.168.1.100"
  shell: bash

# Windows server
server:
  host: "10.0.0.50"
  shell: powershell

AI workflow: You say "deploy to both Linux and Windows servers" → AI reads server configs → uses ssh_run, ssh_run_alias, or dynamic alias tools on each → tool auto-adapts bash/powershell commands → reports unified result

Links

• GitHub