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lpigeon/ros-mcp-server

by Various

Connect AI models like Claude & GPT with robots using MCP and ROS.

L

MCP

lpigeon/ros-mcp-server

Added 1 June 2026

#mcp #mcp-server #modelcontextprotocol #ros #ros-mcp-server #ros2 #ros2-mcp-server

Overview

A Python server that bridges AI models (Claude, GPT) with robots using the Model Context Protocol (MCP) and Robot Operating System (ROS). It translates natural language requests into ROS commands, enabling AI-driven robot control and monitoring.

Best for

Best for
Developers integrating large language models with ROS-based robotic systems

Use cases

  • Command a robot arm to pick objects using natural language via Claude
  • Query sensor data from a ROS-enabled robot through GPT
  • Automate multi-step robot tasks by chaining AI model calls

How to use

Tested with

Claude Desktop, Claude Code, Cursor, ChatGPT

Notes

A Python server that bridges AI models (Claude, GPT) with robots using the Model Context Protocol (MCP) and Robot Operating System (ROS). It translates natural language requests into ROS commands, enabling AI-driven robot control and monitoring.

1,257 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

  • Command a robot arm to pick objects using natural language via Claude
  • Query sensor data from a ROS-enabled robot through GPT
  • Automate multi-step robot tasks by chaining AI model calls

Pros

  • Leverages existing ROS infrastructure for real robot control
  • Supports multiple AI models through the MCP standard
  • Active open-source project with over 1,200 stars

Cons

  • Requires a running ROS environment, limiting use to ROS-compatible robots
  • Dependent on external AI model APIs for natural language processing
  • No built-in simulation or safety validation for generated commands

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Leverages existing ROS infrastructure for real robot control
  • Supports multiple AI models through the MCP standard
  • Active open-source project with over 1,200 stars

Cons

  • Requires a running ROS environment, limiting use to ROS-compatible robots
  • Dependent on external AI model APIs for natural language processing
  • No built-in simulation or safety validation for generated commands
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