unitree-go2-mcp-server: The Unitree Go2 MCP Server is a server built on the MCP that enables users to control the Unitree Go2 robot

by lpigeon · MCP Server · ★ 87

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

How to install unitree-go2-mcp-server

git clone https://github.com/lpigeon/unitree-go2-mcp-server.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 75/100

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.

go2go2-mcp-servermcpmcp-servermodelcontextprotocolros2unitreeunitree-go2unitree-go2-mcp-server

Quick Facts

Stars87
Forks13
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score75/100
Open Issues1
Last Updated2026-06-10
Created2025-05-09
Platformsmcp, python
Est. Tokens~5606k

About unitree-go2-mcp-server

Unitree Go2 MCP Server The Unitree Go2 MCP Server is a server built on the Model Context Protocol (MCP) that enables users to control the Unitree Go2 robot using natural language commands interpreted by a Large Language Model (LLM). These commands are translated into ROS2 instructions, allowing the robot to perform corresponding actions.

Compatible Skills

These tools work well together with unitree-go2-mcp-server for enhanced workflows:

  • scrapegraph-mcp — semantic(0.55)+same_lang+similar_pop+shared_platform (54%)

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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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