Skip to main content

Connect AI Language Models with Robots on ROS using MCP

Project description

ROS MCP Server 🧠⇄🤖

Static Badge Static Badge Static Badge Python Dev Container GitHub Repo stars GitHub last commit

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 rosbridge node.
  • True two-way communication → LLMs can both control robots and observe everything happening in ROS (sensors, topics, parameters).
  • ROS1 & ROS2 support → works with both versions out of the box.
  • MCP-compatible → integrates with any MCP-enabled LLM (Claude Desktop, Gemini, ChatGPT, and beyond).

🎥 Examples in Action

🖥️ Example - Controlling the MOCA mobile manipulator in NVIDIA Isaac Sim
Commands are entered into Claude Desktop, which uses the MCP server to directly drive the simulated robot.


🐕 Example - Controlling Unitree Go with natural language (video)
The MCP server enables the Claude to interpret images from the robot's cameras, and then command the robot based on human natural language commands.


🏭 Example - Debugging an industrial robot (Video)

  • Connecting to an industrial robot enables the LLM to browse all ROS topics and services to assess the robot state.
  • With no predefined context, the MCP server enables the LLM to query details about custom topic and service types and their syntax (00:28).
  • Using only natural language, the operator calls the custom services to test and debug the robot(01:42).

Testing and debugging an industrial robot


⚙️ Features of the ROS MCP Server

  • List topics, services, and message types → explore everything available in your robot’s ROS environment.
  • View type definitions (incl. custom) → understand the structure of any message.
  • Publish/subscribe to topics → send commands or stream robot data in real time.
  • Call services (incl. custom) → trigger robot functions directly.
  • Get/set parameters → read or adjust robot settings on the fly.
  • 🔜 Action support → upcoming support for ROS Actions.
  • 🔜 Permission controls → manage access for safer deployments.

🛠 Getting Started

The MCP server is version-agnostic (ROS1 or ROS2) and works with any MCP-compatible LLM.

Installation

Follow the installation guide for step-by-step instructions:

  1. Clone the repository
  2. Install uv and rosbridge
  3. Install Claude Desktop (or any MCP-enabled client)
  4. Configure your client to connect to the ROS MCP Server
  5. Start rosbridge on the target robot

📚 More Examples & Tutorials

Browse our examples to see the server in action.
We welcome community PRs with new examples and integrations!


🤝 Contributing

We love contributions of all kinds:

  • Bug fixes and documentation updates
  • New features (e.g., Action support, permissions)
  • Additional examples and tutorials

Check out the contributing guidelines and see issues tagged good first issue to get started.


📜 License

This project is licensed under the Apache License 2.0.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ros_mcp-2.1.5.tar.gz (22.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ros_mcp-2.1.5-py3-none-any.whl (24.5 kB view details)

Uploaded Python 3

File details

Details for the file ros_mcp-2.1.5.tar.gz.

File metadata

  • Download URL: ros_mcp-2.1.5.tar.gz
  • Upload date:
  • Size: 22.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ros_mcp-2.1.5.tar.gz
Algorithm Hash digest
SHA256 140b26a7a8f708427f6f00fdb86773511237e3f2c6a09d8b34c3ad310b90c0c5
MD5 67ad85a67cddab525811bbd68d8079b7
BLAKE2b-256 08433dadc7252624d282233608d70e638b4c6df16593ccf4d9c2b941e40de172

See more details on using hashes here.

Provenance

The following attestation bundles were made for ros_mcp-2.1.5.tar.gz:

Publisher: publish.yml on robotmcp/ros-mcp-server

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file ros_mcp-2.1.5-py3-none-any.whl.

File metadata

  • Download URL: ros_mcp-2.1.5-py3-none-any.whl
  • Upload date:
  • Size: 24.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ros_mcp-2.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 b7491efa511115ec499fefc9385e8118e513b448e92a065f36972e345572386d
MD5 a0832705e2b5bb92c7aa5a76654e51c4
BLAKE2b-256 71992b4ced0da22ef28096c321343ccb7743a3c40d6253a93a10cc977c3a09bb

See more details on using hashes here.

Provenance

The following attestation bundles were made for ros_mcp-2.1.5-py3-none-any.whl:

Publisher: publish.yml on robotmcp/ros-mcp-server

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page