ROS MCP Server

The ROS MCP Server is a lightweight bridge that lets large language models (LLMs) such as Claude, GPT‑4, or Gemini speak directly to any ROS or ROS 2 robot without touching the robot’s source code. By running a single node and exposing an MCP‑compatible endpoint, the server turns every ROS topic, service, parameter, and custom message type into a first‑class API that an LLM can discover, query, and invoke with natural language. This eliminates the need for custom adapters or hand‑crafted command pipelines, allowing developers to prototype and iterate on robot behaviors in seconds rather than weeks.

For developers, the value lies in two‑way AI integration. An LLM can command a robot by translating human instructions into ROS messages or service calls, while simultaneously observing the robot’s state by subscribing to topics and reading parameters. The server automatically lists all available resources, provides full type definitions (including user‑defined messages), and supports publish/subscribe, service calls, and parameter manipulation. In ROS 1 or ROS 2 environments, the same MCP endpoint works unchanged, giving teams a single integration point across legacy and modern fleets.

Key capabilities include:

• Discovery – list topics, services, actions (future), and parameters with their full type schemas.
• Interaction – publish to topics, subscribe for live streams, call services (including custom ones), and get/set parameters.
• Visibility – real‑time telemetry feeds into the LLM, enabling context‑aware reasoning and diagnostics.
• Security hooks – upcoming permission controls allow fine‑grained access to sensitive topics or services.

Real‑world use cases span the robotics spectrum. In simulation, a user can command an NVIDIA Isaac Sim robot with natural language through Claude Desktop, watching the model drive motions in real time. On hardware, a human operator can instruct a Unitree Go quadruped to navigate obstacles while the LLM interprets camera feeds and adjusts control commands. In industrial settings, a technician can ask an LLM to list all ROS topics on a factory arm, inspect custom message types, and invoke debug services—all without writing any ROS code. These scenarios demonstrate how the server turns a robot into an AI‑friendly agent that can be taught, queried, and corrected on the fly.

By embedding ROS communication into the MCP framework, the server fits seamlessly into existing AI workflows. LLMs that already support MCP (Claude Desktop, Gemini, ChatGPT, etc.) can connect to the server with a single configuration change, gaining full access to robot data and control primitives. The result is a rapid prototyping loop where natural language becomes the primary interface to complex robotic systems, accelerating development cycles and lowering the barrier for non‑ROS experts to interact with robots.