mcp-remote-macos-use: The only general AI agent that does NOT requires extra API key, giving you full control on your local

by baryhuang · MCP Server · ★ 491

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 79/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

claude-desktopcomputer-usegeneral-agentmacosmacos-usemcp-server

Quick Facts

Stars491
Forks55
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score79/100
Open Issues2
Last Updated2025-06-10
Created2025-03-21
Platformsclaude-code, mcp, python
Est. Tokens~18k

About mcp-remote-macos-use

MCP Server - Remote MacOs Use The first open-source MCP server that enables AI to fully control remote macOS systems. A direct alternative to OpenAI Operator, optimized specifically for autonomous AI agents with complete desktop capabilities, requiring no additional software installation.

Compatible Skills

These tools work well together with mcp-remote-macos-use for enhanced workflows:

  • os-ai-computer-use — semantic(0.29)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • open-computer-use — semantic(0.26)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
  • mac_computer_use — semantic(0.22)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • claude-computer-use-macos — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (57%)
  • AIlice — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)

mcp-remote-macos-use alternative? Top 6 similar tools

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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