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LLMs sometimes hallucinate while stating one thing and outputting disconnected commands/code on the back end in many superficial/general models (Claude included) - does the MCP try to correct/account for that? Or is user oversight necessary to ensure that actions match the LLM output? Is the only way to stop aberrant operation by hitting a physical emergency stop or can the LLM interface be used to for that?

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Great question! I’m one of the collaborators on the project. Right now, the MCP server doesn’t “correct” hallucinations itself, but it enforces a strict tool interface: the LLM can only call valid ROS topics, services, or actions that actually exist and that are explicitly exposed as safe to use. This information is provided through the MCP, so if the model hallucinates a command, the call simply fails gracefully rather than executing something unintended.

For more advanced use cases, we’re also thinking about adding validation layers and safety constraints before execution — so the MCP acts not just as a bridge, but also as a safeguard.

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