diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
index 5ca449145..42ea06d7d 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -33,6 +33,8 @@
     title: Using the Dataset Tools
   - local: language_and_recipes
     title: Language Columns and Recipes
+  - local: tools
+    title: Tools
   - local: streaming_video_encoding
     title: Streaming Video Encoding
   title: "Datasets"
diff --git a/docs/source/tools.mdx b/docs/source/tools.mdx
new file mode 100644
index 000000000..47d556a2e
--- /dev/null
+++ b/docs/source/tools.mdx
@@ -0,0 +1,198 @@
+# Tools
+
+LeRobot v3.1 supports **tool calls** in policies — assistant messages can
+emit structured invocations like `say(text="OK, starting now")` that the
+runtime dispatches to a real implementation (TTS, controller, logger, …).
+
+This page covers:
+
+1. Where the tool catalog lives (PR 1).
+2. How the annotation pipeline produces tool-call atoms (PR 2).
+3. How to add your own tool (PR 3).
+
+## Where tools are declared
+
+Two layers.
+
+**The catalog** — a list of OpenAI-style function schemas — lives at
+`meta/info.json["tools"]` on each dataset. Example:
+
+```json
+{
+  "features": { "...": "..." },
+  "tools": [
+    {
+      "type": "function",
+      "function": {
+        "name": "say",
+        "description": "Speak a short utterance to the user via the TTS executor.",
+        "parameters": {
+          "type": "object",
+          "properties": {
+            "text": { "type": "string", "description": "The verbatim text to speak." }
+          },
+          "required": ["text"]
+        }
+      }
+    }
+  ]
+}
+```
+
+Read it via the dataset metadata accessor:
+
+```python
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+
+meta = LeRobotDatasetMetadata(repo_id="pepijn/super_poulain_final_annotations")
+tools = meta.tools     # list[dict] — OpenAI tool schemas
+```
+
+If the dataset's `info.json` doesn't declare any tools, `meta.tools`
+returns `DEFAULT_TOOLS` from `lerobot.datasets.language` — currently a
+single-entry list with the canonical `say` schema. So unannotated
+datasets and chat-template consumers keep working without any
+configuration:
+
+```python
+prompt_str = tokenizer.apply_chat_template(
+    sample["messages"],
+    tools=meta.tools,                 # works either way
+    add_generation_prompt=False,
+    tokenize=False,
+)
+```
+
+**The implementations** — runnable Python — live under
+`src/lerobot/tools/`, one file per tool. The `say` implementation
+arrives in PR 3 and wraps Kyutai's pocket-tts model.
+
+## Per-row tool *invocations*
+
+The catalog above describes *what can be called*. The actual *call* — the
+function name plus the argument values — is stored per-row, on the
+assistant atoms in `language_events`:
+
+```python
+{
+  "role": "assistant",
+  "content": null,
+  "style": null,
+  "timestamp": 12.4,
+  "camera": null,
+  "tool_calls": [
+    { "type": "function",
+      "function": { "name": "say", "arguments": { "text": "On it." } } }
+  ]
+}
+```
+
+Recipes splice these into rendered messages via `tool_calls_from`:
+
+```yaml
+user_interjection_response:
+  bindings:
+    speech: "emitted_at(t, role=assistant, tool_name=say)"
+  messages:
+    - { role: user,      content: "${task}",         stream: high_level }
+    - { role: assistant, content: "${current_plan}", stream: high_level,
+        target: true, tool_calls_from: speech }
+```
+
+The model's training target is one assistant turn that carries both the
+plan text *and* the `say` tool call. At inference, the runtime parses
+the generated text back into structured `tool_calls` and dispatches to
+the matching implementation.
+
+## How to add your own tool
+
+Three steps. Concrete example: a `record_observation` tool the policy
+can call to capture an extra observation outside the regular control
+loop.
+
+### Step 1 — declare the schema
+
+Add an entry under `meta/info.json["tools"]`. Either edit the file
+directly on disk *before* running the annotation pipeline (it'll be
+preserved) or hand it to `lerobot-annotate` via a config flag (PR 2 —
+exact CLI lands with the pipeline change).
+
+```json
+{
+  "tools": [
+    { "type": "function", "function": { "name": "say", "...": "..." } },
+    {
+      "type": "function",
+      "function": {
+        "name": "record_observation",
+        "description": "Capture a high-resolution still image for the user.",
+        "parameters": {
+          "type": "object",
+          "properties": {
+            "label": { "type": "string", "description": "Short label for the saved image." }
+          },
+          "required": ["label"]
+        }
+      }
+    }
+  ]
+}
+```
+
+The schema follows OpenAI's function-calling convention exactly, so the
+chat template can render it natively.
+
+### Step 2 — implement the call
+
+Create `src/lerobot/tools/record_observation.py`:
+
+```python
+from .base import Tool
+from typing import Any
+
+RECORD_OBSERVATION_SCHEMA: dict[str, Any] = { "...": "..." }   # mirrors the JSON above
+
+
+class RecordObservationTool:
+    name = "record_observation"
+    schema = RECORD_OBSERVATION_SCHEMA
+
+    def __init__(self, schema: dict | None = None, output_dir: str = "."):
+        self.output_dir = output_dir
+
+    def call(self, arguments: dict) -> str:
+        label = arguments["label"]
+        # ... save the latest camera frame to <output_dir>/<label>.png ...
+        return f"saved {label}.png"
+```
+
+One file per tool keeps dependencies isolated — `record_observation`
+might pull `pillow`, while `say` (PR 3) pulls `pocket-tts`. Users
+installing only the tools they need avoid heavy transitive deps.
+
+### Step 3 — register it
+
+Add to `src/lerobot/tools/registry.py` (PR 3):
+
+```python
+from .record_observation import RecordObservationTool
+
+TOOL_REGISTRY["record_observation"] = RecordObservationTool
+```
+
+That's it. At runtime `get_tools(meta)` looks up each schema in
+`meta.tools`, instantiates the matching registered class, and returns
+a name → instance dict the dispatcher can route into.
+
+## Where this fits in the three-PR stack
+
+| Layer | PR | What lands |
+|---|---|---|
+| Catalog storage in `meta/info.json` + `meta.tools` accessor | PR 1 | This page; `SAY_TOOL_SCHEMA`, `DEFAULT_TOOLS` constants in `lerobot.datasets.language`; `LeRobotDatasetMetadata.tools` property |
+| Annotation pipeline writes `tools` to meta after a run; honors anything users pre-populated | PR 2 | `lerobot-annotate` ensures `meta/info.json["tools"]` includes the canonical `say` and merges any user-declared tools |
+| Runnable implementations under `src/lerobot/tools/`; runtime dispatcher; `say.py` wired to Kyutai's pocket-tts | PR 3 | One file per tool; `Tool` protocol; `TOOL_REGISTRY`; optional `[tools]` extra in `pyproject.toml` |
+
+If you want to use a tool *without* writing an implementation (e.g. for
+training-time chat-template formatting only), step 1 alone is enough —
+the model still learns to *generate* the call. Steps 2 and 3 are only
+needed to actually *execute* it at inference.
diff --git a/src/lerobot/datasets/dataset_metadata.py b/src/lerobot/datasets/dataset_metadata.py
index 981526c95..959018dd3 100644
--- a/src/lerobot/datasets/dataset_metadata.py
+++ b/src/lerobot/datasets/dataset_metadata.py
@@ -318,6 +318,28 @@ class LeRobotDatasetMetadata:
         """Keys to access visual modalities (regardless of their storage method)."""
         return [key for key, ft in self.features.items() if ft["dtype"] in ["video", "image"]]
 
+    @property
+    def tools(self) -> list[dict]:
+        """OpenAI-style tool schemas declared by this dataset.
+
+        Read from ``meta/info.json["tools"]``. Returns a copy, so callers
+        can mutate the result safely. Falls back to
+        :data:`lerobot.datasets.language.DEFAULT_TOOLS` (the canonical
+        ``say`` schema) when the dataset doesn't declare any — that way
+        unannotated datasets and chat-template consumers
+        (``apply_chat_template(messages, tools=meta.tools)``) keep
+        working out of the box.
+
+        Implementations live under :mod:`lerobot.tools` (one file per
+        tool); see ``docs/source/tools.mdx`` for the authoring guide.
+        """
+        from .language import DEFAULT_TOOLS  # noqa: PLC0415  (avoid circular import)
+
+        declared = self.info.get("tools")
+        if isinstance(declared, list) and declared:
+            return [dict(t) for t in declared]
+        return [dict(t) for t in DEFAULT_TOOLS]
+
     @property
     def names(self) -> dict[str, list | dict]:
         """Names of the various dimensions of vector modalities."""
diff --git a/src/lerobot/datasets/language.py b/src/lerobot/datasets/language.py
index bce1b33aa..8ab4e006e 100644
--- a/src/lerobot/datasets/language.py
+++ b/src/lerobot/datasets/language.py
@@ -183,6 +183,43 @@ def validate_camera_field(style: str | None, camera: str | None) -> None:
         )
 
 
+# --- Tool registry --------------------------------------------------------
+# Tools declared on a dataset live in ``meta/info.json["tools"]`` as a list
+# of OpenAI-style function schemas. The runtime / training stack reads them
+# through :class:`LeRobotDatasetMetadata.tools` (with these constants as
+# fallback when the dataset doesn't declare any). Implementations live
+# under :mod:`lerobot.tools` (one file per tool); see
+# ``docs/source/tools.mdx`` for the authoring guide.
+
+SAY_TOOL_SCHEMA: dict = {
+    "type": "function",
+    "function": {
+        "name": "say",
+        "description": "Speak a short utterance to the user via the TTS executor.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "text": {
+                    "type": "string",
+                    "description": "The verbatim text to speak.",
+                }
+            },
+            "required": ["text"],
+        },
+    },
+}
+"""Canonical schema for the ``say`` tool emitted by the steerable
+annotation pipeline (PR 2 Module 2). Single source of truth — PR 2's
+writer, PR 3's runtime tool registry, and the dataset visualizer all
+import this constant rather than duplicating the dict."""
+
+DEFAULT_TOOLS: list[dict] = [SAY_TOOL_SCHEMA]
+"""Fallback tools list. Returned by ``LeRobotDatasetMetadata.tools``
+when ``meta/info.json["tools"]`` is unset, so unannotated datasets and
+chat-template consumers (``apply_chat_template(messages, tools=...)``)
+keep working out of the box."""
+
+
 def column_for_style(style: str | None) -> LanguageColumn:
     """Map a language style to the column where rows of that style are stored.