diff --git a/tests/integrations/test_pydantic.py b/tests/integrations/test_pydantic.py
index 8a50be5..9322420 100644
--- a/tests/integrations/test_pydantic.py
+++ b/tests/integrations/test_pydantic.py
@@ -1,10 +1,10 @@
 from typing import Literal
+
 import pytest
 from pydantic import BaseModel, ValidationError
 
-from typeid.integrations.pydantic import TypeIDField
 from typeid import TypeID
-
+from typeid.integrations.pydantic import TypeIDField
 
 USER_TYPEID_STR = str(TypeID("user"))
 ORDER_TYPEID_STR = str(TypeID("order"))
@@ -34,3 +34,53 @@ def test_json_serializes_as_string():
     m = M(id=USER_TYPEID_STR)
     data = m.model_dump_json()
     assert '"id":"' in data
+
+
+def test_json_schema_generation():
+    """Test that model_json_schema() works and produces correct schema."""
+    schema = M.model_json_schema()
+
+    # Verify the schema structure exists
+    assert "properties" in schema
+    assert "id" in schema["properties"]
+
+    id_schema = schema["properties"]["id"]
+
+    # Verify it matches the documented expectations
+    assert id_schema["type"] == "string"
+    assert id_schema["format"] == "typeid"
+    assert "user" in id_schema.get("description", "")
+    assert "examples" in id_schema
+
+
+def test_json_schema_with_generate_definitions():
+    """
+    Test that TypeIDField works with Pydantic's generate_definitions().
+
+    This mimics how FastAPI generates OpenAPI schemas and would catch
+    the bug where __get_pydantic_json_schema__ was passing the wrong
+    schema to the handler, causing FastAPI's OpenAPI generation to fail.
+    """
+    from pydantic.json_schema import GenerateJsonSchema
+
+    # Use the same method FastAPI uses internally
+    generator = GenerateJsonSchema()
+    _, definitions = generator.generate_definitions(
+        inputs=[(M, "validation", M.__pydantic_core_schema__)],
+    )
+
+    # Verify M is in definitions
+    assert "M" in definitions
+
+    # Check the id field schema
+    m_schema = definitions["M"]
+    assert "properties" in m_schema
+    assert "id" in m_schema["properties"]
+
+    id_schema = m_schema["properties"]["id"]
+
+    # Verify TypeID-specific schema properties (same as test_json_schema_generation)
+    assert id_schema["type"] == "string"
+    assert id_schema["format"] == "typeid"
+    assert "user" in id_schema.get("description", "")
+    assert "examples" in id_schema
diff --git a/typeid/integrations/pydantic/v2.py b/typeid/integrations/pydantic/v2.py
index 552d146..638cea6 100644
--- a/typeid/integrations/pydantic/v2.py
+++ b/typeid/integrations/pydantic/v2.py
@@ -65,6 +65,7 @@ def __get_pydantic_core_schema__(cls, source_type: Any, handler: Any) -> core_sc
         # Using a plain validator keeps it simple and fast.
         return core_schema.no_info_plain_validator_function(
             cls._validate,
+            json_schema_input_schema=core_schema.str_schema(),
             serialization=core_schema.plain_serializer_function_ser_schema(
                 lambda v: str(v),
                 when_used="json",
@@ -73,7 +74,9 @@ def __get_pydantic_core_schema__(cls, source_type: Any, handler: Any) -> core_sc
 
     @classmethod
     def __get_pydantic_json_schema__(cls, core_schema_: core_schema.CoreSchema, handler: Any) -> JsonSchemaValue:
-        schema = handler(core_schema_)
+        # Pass the json_schema_input_schema to the handler instead of the validator function schema
+        # This allows Pydantic to generate a proper JSON schema from the string type
+        schema = handler(core_schema_.get("json_schema_input_schema", core_schema_))
 
         # Ensure JSON schema is "string"
         schema.update(