[Hardik K (hardikkaurani)]

Description

When instantiating KernelParameterMetadata, the parameter type can be provided either via type_="..." or via its declared alias type="..." (or defaulted to "str").

However, form_schema runs as a mode="before" model validator prior to alias resolution and default population in Pydantic. Inside form_schema:

type_ = data.get("type_", None)

This caused two issues:

1. When callers initialize metadata with the alias type="..." (e.g., KernelParameterMetadata(name="query", type="str")), data contains "type", so data.get("type_") evaluates to None. As a result, infer_schema returns None and metadata.schema_data remains None.
2. When callers initialize metadata without specifying a type (relying on the default type_: str | None = Field(default="str", alias="type")), type_ is also absent from data during mode="before", leaving metadata.schema_data as None instead of inferring {"type": "string"}.

When tool-calling or function-choice schemas are constructed across connectors (OpenAI, Anthropic, Bedrock, etc.), missing schema_data serializes parameter properties as null instead of their proper JSON schema.

Fix

• In KernelParameterMetadata.form_schema, check data.get("type_") or data.get("type").
• If neither was provided and no type_object was specified, fall back to "str" matching the field default.
• Added regression tests verifying schema inference when using the type alias and when using the default type.

Verification

• Added regression tests in python/tests/unit/functions/test_kernel_parameter_metadata.py.
• All unit tests in python/tests/unit/functions/ pass (208 passed).
• Ruff check and formatting checks pass cleanly.

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[Hardik K (hardikkaurani)]

Hi Eduard van Valkenburg (@eavanvalkenburg), could you please take a look at this PR when you have a moment? It fixes schema_data inference in KernelParameterMetadata when using the 'type' alias or when relying on the default 'str' type. Tests and ruff checks pass cleanly. Thank you!