uiautomator2-mcp-server: A MCP server that provides tools for controlling and interacting with Android devices using uiautomator2

by tanbro · MCP Server · ★ 53

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

How to install uiautomator2-mcp-server

uvx uiautomator2-mcp-server stdio
uv tool install uiautomator2-mcp-server

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 71/100

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. Full audit →

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.

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Quick Facts

Stars53
Forks11
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score71/100
Open Issues5
Last Updated2026-10-22
Created2026-01-01
Platformsmcp, python
Est. Tokens~20k

About uiautomator2-mcp-server

uiautomator2-mcp-server - 🇨🇳 中文说明 Code of Conduct: Please follow our Code of Conduct when participating in this project. An MCP server that provides tools for controlling Android devices using uiautomator2. Use AI to automate your Android device: take screenshots, tap/swipe, manage apps, send text, and more. 📺 Demo Conversational Androi

Compatible Skills

These tools work well together with uiautomator2-mcp-server for enhanced workflows:

  • LockKnife — semantic(0.21)+complementary+rare_topics+same_lang+shared_platform (52%)
  • adb-mcp — semantic(0.53)+rare_topics+similar_pop+shared_platform (48%)

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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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