lerobot-context

name	lerobot-context
description	Pull authoritative, version-correct info from Context7 (library docs) and the Hugging Face MCP server (Hub + docs + datasets/models). Invoke whenever a question involves lerobot/huggingface APIs, policy configs, VLA/diffusion/flow-matching behavior, or any comparison across library versions (e.g. v0.4.x vs v0.5.x). Also invoke when resolving a dataset/model repo id on the Hub.

lerobot-context

This project depends on a fast-moving stack — lerobot, huggingface_hub, transformers, datasets, and VLA/diffusion policies (pi0, smolvla, etc.). Training-flag names, dataclass fields, module paths, and camera-key conventions change between minor tags. Do not answer from memory for these topics — use the MCP tools below first.

When to use

Trigger on:

• Questions about lerobot APIs, training flags, policy configs (pi0, smolvla, pi05, act, diffusion, rtc).
• Anything that references a specific lerobot version, release, or upgrade path.
• Questions about a Hugging Face dataset or model repo (lerobot/smolvla_base, ${HF_USER}/..., etc.).
• Dataset schema questions (feature keys like observation.images.*, action, observation.state).
• Compatibility questions between lerobot and its extras ([pi], [smolvla]).

How to use

Step 1 — check the installed lerobot version first, so the docs match the user's actual environment:

python -c "import lerobot; print(lerobot.__version__)"
# or, inside the vendored submodule:
git -C lerobot describe --tags --always

Step 2 — pull docs:

• context7.resolve-library-id → huggingface/lerobot (and transformers / huggingface_hub as needed), then context7.get-library-docs at the resolved id, filtered to the topic. Pass the version if Context7 supports it.
• huggingface.documentation_semantic_search for narrative questions ("how do I fine-tune smolvla with a custom camera layout?").
• huggingface.hub_repository_details for any repo_id the user mentions — confirm it exists, read the model/dataset card, check what camera keys or state dims it was trained on.
• huggingface.model_search / dataset_search when the user is fishing for a starting checkpoint.

Step 3 — verify against the local tree before recommending. Memories and docs can lag reality. If you'll recommend a symbol, Grep for it in the local lerobot checkout to confirm it exists at the user's version.

What lives where

Topic	Prefer
lerobot module paths, dataclass fields, CLI flags	Context7 (huggingface/lerobot) → then Grep the local checkout
Fine-tuning narrative / how-to	HF documentation_semantic_search
Model/dataset card, camera layout of a checkpoint	HF hub_repository_details
Cross-version breaking changes	Context7 with version pins + git diff vA..vB locally
transformers / huggingface_hub / peft / datasets APIs	Context7 (resolve → get-docs)

Auth — what the user must export

Both MCP servers are remote HTTP. .mcp.json references env vars; if either is missing the server will fail silently. Tell the user to export:

export CONTEXT7_API_KEY=...   # free tier at context7.com/dashboard; optional but raises rate limits
export HF_TOKEN=hf_...         # from https://huggingface.co/settings/tokens (Read scope is enough)

Persist them in ~/.bashrc / ~/.zshrc (or a project .envrc used with direnv). Do not write these into .mcp.json — that file is project-shared.

Anti-patterns

• Recommending a training flag without first confirming it exists at the installed lerobot.__version__.
• Quoting a camera-key convention from memory — always verify against the target checkpoint's model card (HF) and the factory code that loads input_features.
• Citing v0.4.x module paths when the user is on v0.5.x (or vice-versa). The so101_follower → so_follower and datasets.utils.hw_to_dataset_features → datasets.feature_utils.hw_to_dataset_features moves are the canonical gotchas here.