Wrap rl training env to make it a mcp-server.
Watch, diagnose and steer a live RL training run — from an AI agent, or from your shell.
Training a policy is a slow feedback loop. Launch, wait an hour, squint at TensorBoard, guess which reward weight was wrong, relaunch. rlmcp shortens it.
Attach it to a running job and you can ask the live process what the robot is doing, look at it, measure how jerky it is, change a reward weight, unlock harder terrain, and roll back if that made things worse. No restart.
Two backends ship: mjlab, which is what this is developed against, and IsaacLab. The simulator sits behind a small adapter, so a third is a couple of hundred lines rather than a fork.
⚠️ Experimental. The surface is real and tested, but it is still moving.
If you use a coding agent, give it one line:
(Say IsaacLab instead if that is what you train on — the docs cover both.)
It reads the docs here, adds the wrapper to your training script, and drives the run from there.
If you are driving yourself, jump to Install and Try it.
Almost all of that is an MCP tool too, so an agent can drive the whole loop, and it sees the screenshots and plots, not just their file paths. play is the exception, and stays CLI-only because it opens a window.
The CLI knows who is reading. A terminal gets aligned tables, and pictures open by themselves. A pipe gets JSON, and each command's JSON is a parsing contract that does not move — the shapes are written down and pinned by tests, in docs/tools.md.
From the project README.
Add the radar badge to your README — it shows your project was picked up by MCP Radar and links to this page:
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