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docs(tools): drop follow-up-PR references

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Reword the two callouts in `tools.mdx` to describe the runtime layer
in present tense ("not part of the catalog layer shipped today",
"those modules don't yet exist in the tree") instead of pointing at a
specific follow-up PR. Keeps the doc honest about what works now
without coupling it to a particular release order.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
2026-05-06 20:29:42 +02:00
parent e7e5fca5de
commit 85576acc29
1 changed files with 8 additions and 8 deletions
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@@ -68,9 +68,9 @@ prompt_str = tokenizer.apply_chat_template(
**The implementations** — runnable Python — will live under **The implementations** — runnable Python — will live under
`src/lerobot/tools/`, one file per tool. The runtime dispatcher and `src/lerobot/tools/`, one file per tool. The runtime dispatcher and
the canonical `say` implementation (wrapping Kyutai's pocket-tts) land the canonical `say` implementation (wrapping Kyutai's pocket-tts) are
in a follow-up PR; this PR ships only the catalog storage and not part of the catalog layer described here; today this layer ships
fallback constant. only the schema storage and the `DEFAULT_TOOLS` fallback constant.
## Per-row tool _invocations_ ## Per-row tool _invocations_
@@ -118,11 +118,11 @@ the matching implementation.
> **Note:** Steps 2 and 3 below describe the runtime layer > **Note:** Steps 2 and 3 below describe the runtime layer
> (`src/lerobot/tools/`, the `Tool` protocol, `TOOL_REGISTRY`, > (`src/lerobot/tools/`, the `Tool` protocol, `TOOL_REGISTRY`,
> `get_tools(meta)`) which lands in a follow-up PR. Today (this PR > `get_tools(meta)`) which is not part of the catalog layer shipped
> only), Step 1 is enough to make the tool visible to the chat > today — those modules don't yet exist in the tree. Step 1 alone is
> template via `meta.tools` so the model can learn to _generate_ the > enough to make the tool visible to the chat template via
> call. Executing the call at inference is what the follow-up PR > `meta.tools` so the model can learn to _generate_ the call;
> wires up. > executing the call at inference requires the runtime layer.
Three steps. Concrete example: a `record_observation` tool the policy Three steps. Concrete example: a `record_observation` tool the policy
can call to capture an extra observation outside the regular control can call to capture an extra observation outside the regular control