ros-mcp-server
ROS-MCP-Server is a Python bridge that connects large language models (Claude, GPT, Gemini) to ROS/ROS2 robots via the Model Context Protocol, enabling AI systems to observe sensor data and control robot behavior without modifying existing robot code.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | robotmcp/ros-mcp-server |
| Owner | robotmcp |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 1.3k |
| Forks | 199 |
| Open issues | 24 |
| Latest release | v3.1.0 (2026-06-17) |
| Last updated | 2026-07-07 |
| Source | https://github.com/robotmcp/ros-mcp-server |
What ros-mcp-server is
A Python MCP server implementation that exposes ROS/ROS2 topic publish/subscribe, service calls, actions, and parameter management to LLM clients. It uses rosbridge to communicate with existing ROS systems and provides type discovery for custom message definitions, allowing LLMs to autonomously determine correct API usage.
Get the ros-mcp-server source
Clone the repository and explore it locally.
git clone https://github.com/robotmcp/ros-mcp-server.gitcd ros-mcp-server# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- ROS/ROS2 environment must be operational and rosbridge node deployed; network between MCP client and rosbridge must be stable and authenticated.
- Custom ROS message types are auto-discovered but complex nested types or external type dependencies may require manual type registration for LLM clarity.
- MCP client choice (Claude Desktop, Gemini CLI, Cursor, etc.) determines available LLM reasoning capability and inference cost; consider token overhead for large telemetry streams.
- Latency depends on rosbridge round-trip time and LLM inference—test end-to-end timing for your deployment before production use.
- Requires Python 3.10+, pip 23.0+; dev container provided but host ROS installation or Docker-based ROS setup needed for development/testing.
When to avoid it — and what to weigh
- Real-time, sub-millisecond control loops — MCP and LLM inference introduce latency unsuitable for high-frequency control (e.g., walking gait feedback at >100 Hz). Use for supervisory tasks, not low-level servo loops.
- Fully air-gapped or non-IP networks — Requires network connectivity between LLM client (cloud or local) and ROS system. Not viable for isolated systems without custom network bridging.
- Proprietary robot stacks without ROS middleware — Only works with systems already using ROS/ROS2. Direct integrations with manufacturer APIs (e.g., ABB Rapid, FANUC Karel) would require separate effort.
- Safety-critical applications without formal verification — LLM-generated commands lack hard guarantees and traceability. Regulatory domains (medical, aerospace) require additional safety layers, logging, and approval workflows.
License & commercial use
Licensed under Apache License 2.0, a permissive OSI-approved open-source license allowing commercial use, modification, and distribution with attribution and liability disclaimer.
Apache 2.0 permits commercial use, but the project provides no explicit warranty or liability limitation beyond the standard license text. Review the LICENSE file and consider consulting legal counsel for production deployments involving safety-critical robot control.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
MCP and rosbridge expose robot control surfaces to LLM clients over network; use firewall rules, mutual TLS, and network segmentation to prevent unauthorized access. LLM-generated commands are not intrinsically safe or auditable; add command validation, logging, and role-based access controls for safety-critical tasks. rosbridge itself has no built-in encryption—protect network path and consider VPN/SSH tunneling.
Alternatives to consider
Robot Operating System (ROS) with native Python/C++ nodes
Full control but requires writing custom integration code for each LLM provider; no abstraction layer like MCP. Better for deterministic, low-latency tasks.
Manufacturer robot APIs (ABB Connectivity Kit, FANUC RoboDialogue, etc.)
Tight integration with specific robot hardware but locked to one vendor and often proprietary. No multi-robot or LLM-agnostic approach.
Custom REST/gRPC layers on top of ROS
Offers flexibility and lower-level control but requires significant boilerplate, doesn't standardize on MCP, and no built-in LLM type discovery.
Build on ros-mcp-server with DEV.co software developers
Evaluate ROS-MCP-Server for your robotics project. Test with Claude, Gemini, or ChatGPT. Contact our team to discuss safety, security, and deployment strategy.
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ros-mcp-server FAQ
Do I need to modify my existing ROS code?
What LLM clients are supported?
Is this secure for production robots?
What is the latency?
Software developers & web developers for hire
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Ready to integrate AI with your robot?
Evaluate ROS-MCP-Server for your robotics project. Test with Claude, Gemini, or ChatGPT. Contact our team to discuss safety, security, and deployment strategy.