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feat(toolbox-core): Add support for optional parameters #290
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kurtisvg
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kurtisvg
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Background
The previous implementation of
toolbox-core
treated all tool parameters as implicitly required. This limited the flexibility of tool definitions and did not align with common use cases where tools have optional arguments with default behaviors.This PR introduces full support for optional parameters, corresponding to the feature added to the Toolbox server (#617), enabling developers to create more versatile and powerful tools. The changes ensure that optionality is correctly reflected and handled at every stage: from the function signature and type hinting down to the final payload sent to the server.
Proposed Changes
This PR introduces a series of coordinated changes across the parameter handling and tool invocation pipeline:
1.
ParameterSchema
EnhancementParameterSchema
of the Toolbox protocol, allowing tool manifest authors to explicitly mark parameters as optional.__get_type()
method now correctly wraps the type intyping.Optional
when a parameter hasrequired=False
.to_param()
method now constructs aninspect.Parameter
withdefault=None
for optional parameters.2. Correct Signature Generation
ToolboxTool.__init__
method, the list of tool parameters is now sorted to place all required parameters before optional ones.ValueError: non-default argument follows default argument
that could occur if a manifest defined an optional parameter before a required one.3. Aligned Pydantic Validation
The
params_to_pydantic_model
utility function has been updated to be aware of therequired
flag. It now generates Pydantic Field definitions withdefault=None
for optional fields, ensuring runtime validation is consistent with the function signature.4. Cleaner API Payloads
null
value.Impact
my_tool(required_arg: str, optional_arg: Optional[str] = "Some Value")). Standard tools like
help()` and editor autocompletion will work as expected.