Function _infer_tool_from_function

python/composio/core/models/custom_tool.py:264–357

Create a CustomTool by inferring metadata from a decorated function. - slug: from ``fn.__name__.upper()`` - name: from ``fn.__name__`` humanized - description: from ``fn.__doc__`` - input_params: from the first parameter with a BaseModel type annotation

(
    fn: t.Callable[..., t.Any],
    *,
    slug: t.Optional[str] = None,
    name: t.Optional[str] = None,
    description: t.Optional[str] = None,
    extends_toolkit: t.Optional[str] = None,
    output_params: t.Optional[t.Type[BaseModel]] = None,
    preload: t.Optional[bool] = None,
    annotation_locals: t.Optional[t.Mapping[str, t.Any]] = None,
)

def _infer_tool_from_function(
    fn: t.Callable[..., t.Any],
    *,
    slug: t.Optional[str] = None,
    name: t.Optional[str] = None,
    description: t.Optional[str] = None,
    extends_toolkit: t.Optional[str] = None,
    output_params: t.Optional[t.Type[BaseModel]] = None,
    preload: t.Optional[bool] = None,
    annotation_locals: t.Optional[t.Mapping[str, t.Any]] = None,
) -> CustomTool:
    """Create a CustomTool by inferring metadata from a decorated function.

    - slug: from ``fn.__name__.upper()``
    - name: from ``fn.__name__`` humanized
    - description: from ``fn.__doc__``
    - input_params: from the first parameter with a BaseModel type annotation
    """
    # Infer slug
    actual_slug = slug or fn.__name__.upper()
    actual_name = name or fn.__name__.replace("_", " ").title()
    actual_description = description or inspect.cleandoc(fn.__doc__ or "")

    if not actual_description:
        raise ValidationError(
            f"experimental.tool: description is required. "
            f'Add a docstring to "{fn.__name__}" or pass description=...'
        )

    # Validate and infer function signature.
    # Accepted shapes (consistent with TS CustomToolExecuteFn):
    #   (input: BaseModel)          — no session context needed
    #   (input: BaseModel, ctx)     — with session context
    # The first param MUST be annotated with a BaseModel subclass.
    # Reject async before wrapping (wrapper would hide it from _create_tool)
    if asyncio.iscoroutinefunction(fn):
        raise ValidationError(
            f'experimental.tool: "{fn.__name__}" is async. '
            f"The Composio Python SDK is synchronous — use a regular "
            f"'def {fn.__name__}(input, ctx)' instead of 'async def'."
        )

    sig = inspect.signature(fn)
    params = list(sig.parameters.values())
    resolved_annotations = _resolve_function_annotations(
        fn,
        localns=annotation_locals,
    )

    if not params:
        raise ValidationError(
            f'experimental.tool: "{fn.__name__}" must accept at least one parameter '
            f"annotated with a Pydantic BaseModel subclass, e.g. "
            f"def {fn.__name__}(input: MyInput, ctx): ..."
        )

    if len(params) > 2:
        raise ValidationError(