feat(anthropic): Support JSON schema transformer and strict tool calls

pydantic_ai_slim/pydantic_ai/models/anthropic.py

@@ -835,14 +835,31 @@ async def _map_user_prompt(
                 else:
                     raise RuntimeError(f'Unsupported content type: {type(item)}')  # pragma: no cover
 
-    @staticmethod
-    def _map_tool_definition(f: ToolDefinition) -> BetaToolParam:
-        return {
+    def _map_tool_definition(self, f: ToolDefinition) -> BetaToolParam:
+        """Map tool definition to Anthropic format with strict mode support.
+
+        Follows the same pattern as OpenAI (line 768): checks if f.strict is truthy,
+        not just checking if it's not None (which was PR #3457's critical mistake).
+
+        Args:
+            f: Tool definition to map
+
+        Returns:
+            BetaToolParam with strict mode set if applicable
+        """
+        tool_param: BetaToolParam = {
             'name': f.name,
             'description': f.description or '',
             'input_schema': f.parameters_json_schema,
         }
 
+        # ✅ CRITICAL: Only add strict if explicitly True (not None, not False)
+        # This prevents the strict field from leaking into tools that don't explicitly set it
+        if f.strict is True:
+            tool_param['strict'] = True  # type: ignore[typeddict-item]
+
+        return tool_param
+
 
 def _map_usage(
     message: BetaMessage | BetaRawMessageStartEvent | BetaRawMessageDeltaEvent,

pydantic_ai_slim/pydantic_ai/profiles/anthropic.py

@@ -1,8 +1,92 @@
 from __future__ import annotations as _annotations
 
+from dataclasses import dataclass
+from typing import Any
+
+from .._json_schema import JsonSchema, JsonSchemaTransformer
 from . import ModelProfile
 
+try:
+    from anthropic.lib.tools import transform_schema as anthropic_transform_schema  # type: ignore[import-not-found]
+except ImportError:
+    anthropic_transform_schema = None
+
+
+@dataclass(init=False)
+class AnthropicJsonSchemaTransformer(JsonSchemaTransformer):
+    """Transform JSON schemas for Anthropic structured outputs.
+
+    Anthropic requires schemas to not include 'title', '$schema', or 'discriminator' at root level.
+
+    `additionalProperties` is set to false for objects to ensure strict mode compatibility.
+
+    optionally use Anthropic's transform_schema helper for validation
+
+    see: https://docs.claude.com/en/docs/build-with-claude/structured-outputs
+    """
+
+    def transform(self, schema: JsonSchema) -> JsonSchema:
+        """Apply Anthropic-specific schema transformations.
+
+        Removes 'title', '$schema', and 'discriminator' fields which are not supported by Anthropic API,
+        and sets `additionalProperties` to false for objects to ensure strict mode compatibility.
+
+        If available, also validates the schema using Anthropic's transform_schema helper from their SDK.
+
+        Args:
+            schema: The JSON schema to transform
+
+        Returns:
+            Transformed schema compatible with Anthropic's structured outputs API
+        """
+        # remove fields not supported by Anthropic
+        schema.pop('title', None)
+        schema.pop('$schema', None)
+        schema.pop('discriminator', None)
+
+        schema_type = schema.get('type')
+        if schema_type == 'object':
+            schema['additionalProperties'] = False
+            if self.strict is True:
+                if 'properties' not in schema:
+                    schema['properties'] = dict[str, Any]()
+                schema['required'] = list(schema['properties'].keys())
+            elif self.strict is None:
+                if schema.get('additionalProperties', None) not in (None, False):
+                    self.is_strict_compatible = False
+                else:
+                    schema['additionalProperties'] = False
+
+                if 'properties' not in schema or 'required' not in schema:
+                    self.is_strict_compatible = False
+                else:
+                    required = schema['required']
+                    for k in schema['properties'].keys():
+                        if k not in required:
+                            self.is_strict_compatible = False
+            else:
+                if 'additionalProperties' not in schema:
+                    schema['additionalProperties'] = False
+
+        if anthropic_transform_schema is not None:
+            try:
+                validated_schema = anthropic_transform_schema(schema)  # pyright: ignore[reportUnknownVariableType]
+                if isinstance(validated_schema, dict):
+                    schema = validated_schema
+            except Exception:
+                pass
+
+        if self.strict is True:
+            self.is_strict_compatible = True
+
+        return schema
+
 
-def anthropic_model_profile(model_name: str) -> ModelProfile | None:
+def anthropic_model_profile(model_name: str) -> ModelProfile:
     """Get the model profile for an Anthropic model."""
-    return ModelProfile(thinking_tags=('<thinking>', '</thinking>'))
+    return ModelProfile(
+        json_schema_transformer=AnthropicJsonSchemaTransformer,
+        supports_json_schema_output=True,
+        supports_json_object_output=True,
+        thinking_tags=('<thinking>', '</thinking>'),
+    )

tests/models/test_anthropic.py

@@ -545,6 +545,7 @@ def my_tool(value: str) -> str:  # pragma: no cover
                     'required': ['value'],
                     'type': 'object',
                 },
+                'strict': True,
                 'cache_control': {'type': 'ephemeral', 'ttl': '5m'},
             }
         ]
@@ -6516,3 +6517,101 @@ async def test_anthropic_bedrock_count_tokens_not_supported(env: TestEnv):
 
     with pytest.raises(UserError, match='AsyncAnthropicBedrock client does not support `count_tokens` api.'):
         await agent.run('hello', usage_limits=UsageLimits(input_tokens_limit=20, count_tokens_before_request=True))
+
+
+def test_anthropic_json_schema_transformer():
+    """
+    Test AnthropicJsonSchemaTransformer removes unsupported fields.
+    """
+    from pydantic_ai.profiles.anthropic import AnthropicJsonSchemaTransformer
+
+    schema = {
+        'type': 'object',
+        'title': 'testSchema',
+        '$schema': 'http://json-schema.org/draft-07/schema#',
+        'discriminator': 'some_value',
+        'properties': {'name': {'type': 'string'}, 'age': {'type': 'integer'}},
+        'required': ['name'],
+    }
+
+    transformer = AnthropicJsonSchemaTransformer(schema, strict=True)
+    result = transformer.walk()
+
+    assert 'title' not in result
+    assert '$schema' not in result
+    assert 'discriminator' not in result
+    assert result['type'] == 'object'
+    assert result['additionalProperties'] is False
+    assert result['properties'] == schema['properties']
+    # In strict mode, Anthropic requires ALL properties to be in required list
+    assert result['required'] == ['name', 'age']
+    assert transformer.is_strict_compatible is True
+
+    # test auto-detect mode (strict=False)
+    schema2 = {
+        'type': 'object',
+        'properties': {'id': {'type': 'integer'}},
+        'required': ['id'],
+    }
+    transformer2 = AnthropicJsonSchemaTransformer(schema2, strict=False)
+    result2 = transformer2.walk()
+    assert result2['additionalProperties'] is False
+    assert transformer2.is_strict_compatible is True
+
+
+def test_anthropic_strict_mode_tool_mapping():
+    """
+    Test AnthropicModel._map_tool_definition() supports strict mode.
+    """
+    from pydantic_ai.models.anthropic import AnthropicModel
+    from pydantic_ai.providers.anthropic import AnthropicProvider
+    from pydantic_ai.tools import ToolDefinition
+
+    # Use mock provider to avoid requiring real API key
+    model = AnthropicModel('claude-sonnet-4-5', provider=AnthropicProvider(api_key='test-key'))
+
+    tool_def_strict = ToolDefinition(
+        name='test_tool',
+        description='a test tool',
+        parameters_json_schema={'type': 'object', 'properties': {'x': {'type': 'integer'}}, 'required': ['x']},
+        strict=True,
+    )
+
+    result_strict = model._map_tool_definition(tool_def_strict)  # pyright: ignore[reportPrivateUsage]
+
+    assert result_strict['strict'] is True  # pyright: ignore[reportGeneralTypeIssues]
+    assert result_strict['name'] == 'test_tool'
+    assert result_strict.get('description') == 'a test tool'
+
+    tool_def_no_strict = ToolDefinition(
+        name='test_tool',
+        description='a test tool',
+        parameters_json_schema={'type': 'object', 'properties': {'x': {'type': 'integer'}}, 'required': ['x']},
+        strict=False,
+    )
+
+    result_no_strict = model._map_tool_definition(tool_def_no_strict)  # pyright: ignore[reportPrivateUsage]
+    assert 'strict' not in result_no_strict
+    assert result_no_strict['name'] == 'test_tool'
+    assert result_no_strict.get('description') == 'a test tool'
+
+
+async def test_anthropic_strucutred_output_with_test_model():
+    from pydantic import BaseModel
+
+    from pydantic_ai import Agent
+    from pydantic_ai.models.test import TestModel
+
+    class CityInfo(BaseModel):
+        city: str
+        country: str
+        population: int
+
+    test_model = TestModel(custom_output_args={'city': 'Tokyo', 'country': 'Japan', 'population': 14000000})
+    agent = Agent(test_model, output_type=CityInfo)
+
+    result = await agent.run('Tell me about Tokyo')
+    assert isinstance(result.output, CityInfo)
+    assert result.output.city == 'Tokyo'
+    assert result.output.country == 'Japan'
+    assert result.output.population == 14000000