No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by robotmcp · MCP Server · ★ 1.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ros-mcp-server safe to install? View the security audit →
ROS MCP Server 🧠⇄🤖 ROS-MCP-Server connects large language models (such as Claude, GPT, and Gemini) with existing robots giving them bidirectional AI integration. With no changes to existing robot source code, this enables: 🗣 Commanding the robot in natural language → instructions are translated into ROS/ROS2 commands. 👀 Giving AI full visibility → subscribe to topics, call services, read sensor data, and monitor robot state in real time. ✅ Key Benefits No robot code changes → only requires adding the node. True two-way communication → LLMs can both control robots and observe everything happening in ROS (sensors, topics, parameters). ROS1 & ROS2 support → works with bo
| Stars | 1,471 |
| Forks | 212 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 61.9365284459048/100 |
| Open Issues | 62 |
| Last Updated | 2026-09-22 |
| Created | 2025-04-11 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~14k |
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ros-mcp-server is Connect AI models like Claude & GPT with robots using MCP and ROS.. It is categorized as a MCP Server with 1.5k GitHub stars.
ros-mcp-server is primarily written in Python. It covers topics such as mcp, mcp-server, modelcontextprotocol.
You can find installation instructions and usage details in the ros-mcp-server GitHub repository at github.com/robotmcp/ros-mcp-server. The project has 1.5k stars and 212 forks, indicating an active community.
ros-mcp-server is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to ros-mcp-server on Agent Skills Hub include bagel, csharp-sdk, MCP-Chinese-Getting-Started-Guide. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: