fix(schema): infer array/object type from items/properties when 'type' is absent (closes #7668)

[DanubiaM]

Closes #7668

Summary

• The JSON Schema spec does not require a "type" key on every node — a schema with an "items" key can only describe an array, and one with "properties" can only describe an object, even without an explicit "type". This is legal, spec-compliant JSON Schema that some MCP servers rely on (observed in practice with Atlassian's remote MCP server, but the gap is generic: it affects any MCP/A2A source that omits "type" this way, not just one integration).
• _json_schema_to_pydantic_type (pydantic_schema_utils.py) had no inference for this: when type_ was None it fell straight through to return Any, even when items/properties made the real shape obvious. That silently drops validation for the field (anything is accepted) and, worse, when the schema round-trips back out via generate_model_description/model.model_json_schema(), an Any-typed union member serializes as an empty {} — which the pre-existing schema == {} branch in ensure_type_in_schemas then relabels {"type": "object"}, even when the original node was really an array. The tool schema shown to the LLM ends up wrong, not just permissive.
• ensure_type_in_schemas itself only handled the fully-empty-dict case in anyOf/oneOf; a non-empty entry missing type (e.g. {"items": {...}}) was left untouched, which would still trip a strict-mode provider 400 ("schema must have a 'type' key") on any code path that reaches it with a raw, un-round-tripped schema.

What changed

• lib/crewai/src/crewai/utilities/pydantic_schema_utils.py:
  • _json_schema_to_pydantic_type: when type_ is None, infer "array" from a sibling "items" key or "object" from a sibling "properties" key before the rest of the type-dispatch logic runs. Falls back to Any only when neither hint is present.
  • ensure_type_in_schemas: added _infer_type_from_structure (same items→array / properties→object rule) and applied it to non-empty anyOf/oneOf members missing "type", in addition to the existing empty-{}→"object" handling.

Both functions are shared, generic schema-conversion utilities used for every MCP tool and A2A schema the framework ingests — this is not special-cased to any one server.

Test plan

• lib/crewai/tests/utilities/test_pydantic_schema_utils.py:
  • TestEnsureTypeInSchemas: new cases for an anyOf entry with items and no type (→ gets "type": "array"), one with properties and no type (→ gets "type": "object"), and a negative case confirming a genuinely ambiguous non-empty schema (no items/properties) is left untyped rather than guessed.
  • New TestUntypedStructuralSchemas class: builds a model from a realistic untyped items-in-anyOf schema and asserts it accepts a list of strings, accepts None, and rejects a non-list/non-null value — confirming the field is no longer silently typed as Any. Same pattern for the properties-without-type case.
• Ran pytest tests/utilities/test_pydantic_schema_utils.py -v: 94 passed (88 previously existing + 6 new), no regressions.

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📝 Walkthrough

Walkthrough

The schema utilities infer array and object types from items and properties when type is omitted. Ambiguous schemas remain untyped. Pydantic model creation preserves the inferred types and validates values accordingly.

Changes

Schema type inference

Layer / File(s)	Summary
Structural normalization lib/crewai/src/crewai/utilities/pydantic_schema_utils.py, lib/crewai/tests/utilities/test_pydantic_schema_utils.py	ensure_type_in_schemas infers array or object for untyped structural branches. Branches with both items and properties, or without structural hints, remain untyped.
Pydantic type conversion lib/crewai/src/crewai/utilities/pydantic_schema_utils.py, lib/crewai/tests/utilities/test_pydantic_schema_utils.py	_json_schema_to_pydantic_type uses the same inference and falls back to Any for ambiguous schemas. Tests verify valid arrays and objects, None handling, and rejection of incorrect types.

Priority: ➖ Normal

Severity of issue fixed: Medium

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 53.85% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 13 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely describes the main change: inferring missing array and object types from JSON Schema structure.
Description check	✅ Passed	The description includes the linked issue, a detailed summary, implementation details, and test results. It uses a "Test plan" section instead of the template's "Verification" heading and does not inc…
Linked Issues check	✅ Passed	The changes satisfy issue #7668. _infer_type_from_structure infers array from items and object from properties. _json_schema_to_pydantic_type uses this inference, so structural branches re…
Out of Scope Changes check	✅ Passed	The changes are limited to the shared schema conversion utility and its regression tests. The helper centralizes the required inference and supports generic MCP and A2A schema handling. The pull reque…

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Inline comments:
In `@lib/crewai/src/crewai/utilities/pydantic_schema_utils.py`:
- Around line 280-281: Update _infer_type_from_structure to return None when a
schema contains both “items” and “properties”, before the individual array or
object checks. In _json_schema_to_pydantic_type, use
_infer_type_from_structure(json_schema) for type inference so mixed structural
schemas remain untyped and retain their object constraints.

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