fix: Add support for list-type JSON Schema fields in modeling.py #37
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added 2 commits
May 1, 2025 23:35
Problem:
- JSON Schema allows specifying multiple types using array notation: {"type": ["string", "number"]}
- This is valid per JSON Schema specification, and common in real-world schemas
- The mcpadapt modeling.py module failed with "unhashable type: 'list'" when processing such schemas
- Error occurs in get_field_type() when attempting to use a list as a dictionary key
Solution:
- Enhanced get_field_type() to properly handle list-type JSON Schema types
- Added special case to detect when json_type is a list
- Implemented conversion of list-type to Python Union types
- For single-item lists, extract and use the single type
- For multi-item lists, create a Union of all mapped types
- Preserves original behavior for all other schema types
This fix ensures compatibility with JSON Schema that use the array notation for
specifying multiple allowed types for a field, which is a common pattern in the
JSON Schema ecosystem. The fix is backwards compatible and follows the expected
behavior of properly converting JSON Schema types to their Python equivalents.
This commit adds a comprehensive test suite for validating the fix for JSON Schema array notation in type fields (e.g., "type": ["string", "number"]). Key additions: - New test file tests/test_modeling.py with multiple test scenarios: - Direct test against modeling.py to verify handling of list-type JSON Schema fields - Tests for array-type fields with multiple primitive types - Specific tests for handling of null types in array notation - Inspection utility to examine actual schema structure in MCP tools The tests are designed to: 1. Verify the fix works correctly for all edge cases 2. Provide clear diagnostics when the bug is present 3. Demonstrate proper handling of various JSON Schema type patterns 4. Ensure consistent behavior with the existing anyOf implementation The test handles both the "happy path" (with fix) and failure path (without fix), making it valuable for preventing regressions. It also improves null type handling to be consistent with how the codebase already handles nulls in anyOf constructs. This testing approach validates that our implementation correctly supports the JSON Schema specification, which allows multiple types to be specified either via array notation or anyOf constructs.
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Hey @sakthikannan25 thanks for the PR I will review soon! |
grll
requested changes
May 7, 2025
| assert "Name: None" in result_null | ||
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| def test_json_schema_array_type_with_null(json_schema_array_type_server_script): |
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this is already tested above or maybe we could just add one assert above with the case name=None?
| assert "Name: None" in result_with_null | ||
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| def test_json_schema_inspection(json_schema_array_type_server_script): |
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I don't think we need those tests for now I prefer to focus on "E2E" tests from the MCP with somewhat real use scenario to avoid growing the code base / test bases unnecessarly
| print(f"Error during schema inspection: {e}", file=sys.stderr) | ||
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| def test_direct_modeling_with_list_type(): |
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this test could be nice but I would remove the verbose try / except explaining the type error and just keep it to the essential
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I would keep only 2 tests 1 more end to end as you have and move it to test_langchain_adapter.py the other one that simply test the modeling with a schema and test the output ok to keep here but I would move the file in tests/utils/test_modeling.py.
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Problem:
Solution:
This fix ensures compatibility with JSON Schema that use the array notation for specifying multiple allowed types for a field, which is a common pattern in the JSON Schema ecosystem. The fix is backwards compatible and follows the expected behavior of properly converting JSON Schema types to their Python equivalents.