Fair enough! I've set a calendar reminder to come back here in 6 months and a year and see whether the article as a whole has aged like wine or milk.

I originally posted this article on an internal blog, and have reposted here on others' recommendation. Shortly after the initial post, Anthropic released a "Learning Mode" in Claude for Education - but I'll concede that they probably came up with the idea before me.

I see what you did there.

Thank you! This is a really helpful perspective.

Ultimately I'm looking at both persistence and DTL.

Neat use of Dynamic Dispatch! I was thinking something more like (note - this is very quick and dirty/WIP):

/// Generate a set of ObjectScript classes corresponding to Pydantic models defined in a given Python module.
/// 
/// Args:
///     sourceModule: Path to the Python module containing Pydantic models.
///     targetPackage: Target package for generated ObjectScript classes.
///     baseClass: Base class for generated ObjectScript classes.
///
/// Significant contributions by Windsurf / Claude 3.7 Sonnet (Thinking)
/// That is to say, if it doesn't work, it's the AI's fault. (Plus mine for being bad at Python.)
ClassMethod Generate(sourceModule = "mcp", targetPackage = "pkg.isc.mcp.types.test", baseClass = "pkg.isc.mcp.types.BaseModel") [ Language = python ]
{
    import importlib
    import inspect
    import traceback
    import sys
    from pydantic import BaseModel
    import iris
    import datetime
    from typing import Union, Literal
    from types import NoneType, UnionType
    from logging import getLogger

    # Map complex type expressions to ObjectScript types
    complex_type_map = {
        'dict[str, typing.Any]': '%DynamicObject',
        'list[typing.Any]': '%DynamicArray'
    }

    # Other complex expressions that should be flagged as required properties
    complex_required_type_map = {
    }
    
    # Map Pydantic field types to ObjectScript types
    type_map = {
        'str': '%String',
        'int': '%Integer',
        'float': '%Float',
        'bool': '%Boolean',
        'datetime.datetime': '%TimeStamp',
        'datetime.date': '%Date',
        'dict': '%DynamicObject',
        'list': '%DynamicArray'
    }

    def get_all_models(module_name):
        models = []
        processed_models = set()  # Keep track of models we've seen to avoid duplicates
        
        def find_models(module_name):
            module = importlib.import_module(module_name)
            discovered = []
            
            # Find all top-level models in this module
            for name, obj in inspect.getmembers(module):
                if inspect.isclass(obj) and issubclass(obj, BaseModel) and obj != BaseModel:
                    if obj.__name__ not in processed_models:
                        discovered.append(obj)
                        processed_models.add(obj.__name__)
            
            return discovered
        
        # First find all top-level models in the specified module
        module = importlib.import_module(module_name)
        top_models = []
        for name, obj in inspect.getmembers(module):
            if inspect.isclass(obj) and issubclass(obj, BaseModel) and obj != BaseModel:
                top_models.append(obj)
                processed_models.add(obj.__name__)
        
        models.extend(top_models)
        
        # Now recursively find all referenced models
        i = 0
        while i < len(models):
            current_model = models[i]
            i += 1
            
            # Check each field for model references
            for field_name, field_info in current_model.__fields__.items():
                annotation = field_info.annotation
                referenced_models = find_referenced_models(annotation)
                
                for model in referenced_models:
                    if model.__name__ not in processed_models:
                        models.append(model)
                        processed_models.add(model.__name__)
                        print(f"Added referenced model: {model.__name__}")
        
        return models

    def process_model(targetPackage, model):
        # Format class name with package prefix
        class_name = f"{targetPackage}.{model.__name__}"
        
        # Check if class already exists
        cls_def = iris.cls('%Dictionary.ClassDefinition')._OpenId(class_name)
        if cls_def != "":
            print(f"Updating existing class: {class_name}")
        else:
            # Create new class definition
            cls_def = iris.cls('%Dictionary.ClassDefinition')._New()
            cls_def.Name = class_name
            print(f"Creating new class: {class_name}")
        
        cls_def.Super = baseClass
        cls_def.ProcedureBlock = 1
        
        # Add parameter to indicate this is an auto-generated class
        cls_def.Parameters.Clear()
        auto_gen_param = iris.cls('%Dictionary.ParameterDefinition')._New()
        auto_gen_param.Name = "AUTOGENERATED"
        auto_gen_param.Default = "1"
        auto_gen_param.parent = cls_def

        # Clear existing properties - always start from a clean slate
        cls_def.Properties.Clear()
        
        # Process model fields to create properties
        for field_name, field_info in model.__fields__.items():
            # Skip fields that start with underscore
            if field_name.startswith('_'):
                continue
            
            # Simplify property checking - create it fresh
            # The _Save() call will handle merging if it's already defined
            prop = iris.cls('%Dictionary.PropertyDefinition')._New()
            prop.Name = field_name
            prop.parent = cls_def

            print(f"Processing field: {field_name}: {field_info.annotation}")
            
            annotation = field_info.annotation
            (os_type, collection_type, required) = process_annotation(annotation)
            print(f"\tType: {os_type}, Collection type: {collection_type}")
            prop.Type = os_type
            prop.Collection = collection_type
            prop.Required = 1 if required else 0
        
        # Save the class definition
        sc = cls_def._Save()
        if not iris.cls('%SYSTEM.Status').IsOK(sc):
            print(f"Error saving class {class_name}: {iris.cls('%SYSTEM.Status').GetErrorText(sc)}")

    def process_annotation(annotation, topLevel = True):
        # Set up logger once
        logger = getLogger("Generator")
        logger.setLevel("DEBUG")
        
        os_type = ''
        collection_type = ''
        required = True
        
        logger.debug(f"Processing annotation: {annotation}")

        if complex_type_map.__contains__(str(annotation)):
            os_type = complex_type_map[str(annotation)]
            return (os_type, collection_type, False)

        if complex_required_type_map.__contains__(str(annotation)):
            os_type = complex_required_type_map[str(annotation)]
            return (os_type, collection_type, True)
        
        # Check if it's a Union type (Python 3.10+ pipe syntax)
        if isinstance(annotation, UnionType):
            union_types = annotation.__args__
            logger.debug(f"Native union type with args: {union_types}")
            
            # Check if it's an Optional (Union with NoneType)
            if (type(None) in union_types) or (NoneType in union_types):
                # Get the actual type (filter out None)
                actual_type = next(arg for arg in union_types if arg is not type(None) and arg is not NoneType)
                logger.debug(f"Optional type detected: {actual_type}")
                (os_type, collection_type, required) = process_annotation(actual_type)
                required = False
            else:
                # For regular union types, use a strategy that picks the most flexible type
                logger.debug(f"Processing union with multiple types")
                # Default to using the last type in the union
                for type_arg in union_types:
                    (os_type, collection_type, required) = process_annotation(type_arg, False)
        
        # Handle typing.Union
        elif hasattr(annotation, "__origin__") and annotation.__origin__ is Union:
            union_types = annotation.__args__
            logger.debug(f"typing.Union with args: {union_types}")
            
            # Check if it's an Optional (Union with NoneType)
            if (type(None) in union_types) or (NoneType in union_types):
                # Get the actual type (filter out None)
                actual_type = next(arg for arg in union_types if arg is not type(None) and arg is not NoneType)
                logger.debug(f"Optional type detected: {actual_type}")
                (os_type, collection_type, required) = process_annotation(actual_type)
                required = False
            else:
                # For regular union types, use the same strategy as above
                logger.debug(f"Processing union with multiple types")
                for type_arg in union_types:
                    (os_type, collection_type, required) = process_annotation(type_arg, False)
        
        # Handle container types (List, Dict, etc.)
        elif hasattr(annotation, "__origin__"):
            container_type = annotation.__origin__
            
            # Handle Literal separately
            if container_type is Literal:
                logger.debug(f"Literal type: {annotation}")
                os_type = '%String'
            elif topLevel == False:
                # For nested complex types, just fall back to %DynamicArray/%DynamicObject
                os_type = type_map.get(annotation.__name__, '%DynamicObject')
            else:
                type_args = annotation.__args__
                logger.debug(f"Container type: {container_type} with args: {type_args}")
                
                # For List[str], type_args would be (str,)
                # For Dict[str, int], type_args would be (str, int)
                if len(type_args) == 1:
                    # For a single type, it's a collection
                    (os_type, collection_type, required) = process_annotation(type_args[0], False)
                    collection_type = "list"
                    logger.debug(f"List type with element type: {os_type}")
                elif len(type_args) == 2:
                    # For a key-value pair, it's a dictionary
                    (os_type, collection_type, required) = process_annotation(type_args[1], False)
                    collection_type = "array"
                    logger.debug(f"Dictionary type with value type: {os_type}")
        
        # Handle types with a __name__ attribute (basic types)
        elif hasattr(annotation, "__name__"):
            type_name = annotation.__name__
            os_type = type_map.get(type_name, '%String')
            logger.debug(f"Named type: {type_name} -> {os_type}")
        
        # Handle any other types
        else:
            os_type = type_map.get(str(annotation), '%String')
            logger.debug(f"Other type: {annotation} -> {os_type}")
        
        logger.debug(f"Final mapping: {os_type}, collection: {collection_type}, required: {required}")
        return (os_type, collection_type, required)

    def find_referenced_models(annotation):
        """Find all Pydantic models referenced in this type annotation."""
        result = []
        
        # Direct model reference
        if inspect.isclass(annotation) and issubclass(annotation, BaseModel) and annotation != BaseModel:
            result.append(annotation)
        
        # Check for container types (Union, List, etc.)
        elif hasattr(annotation, "__origin__"):
            # For Union types, check each argument
            if annotation.__origin__ is Union:
                for arg in annotation.__args__:
                    result.extend(find_referenced_models(arg))
            
            # For container types like List, Dict
            elif hasattr(annotation, "__args__"):
                for arg in annotation.__args__:
                    result.extend(find_referenced_models(arg))
        
        return result

    try:
        # Find all Pydantic models in the module
        models = get_all_models(sourceModule)
        
        # Add referenced classes to type_map
        for model in models:
            # Format class name with package prefix
            class_name = f"{targetPackage}.{model.__name__}"
            type_map[model.__name__] = class_name

        print(models);

        # Process each model
        for model in models:
            print(f"\r\n")
            process_model(targetPackage, model)
        
        # Compile the whole package
        status = iris.cls('%SYSTEM.OBJ').CompilePackage(targetPackage, 'ck')
        if not iris.cls('%SYSTEM.Status').IsOK(status):
            print(f"Error compiling package {targetPackage}: {iris.cls('%SYSTEM.Status').GetErrorText(status)}")
        
        # Return success
        return 1
        
    except Exception as e:
        exc_type, exc_value, exc_traceback = sys.exc_info()
        lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
        print("Exception caught in Generator.Generate:")
        print(''.join(lines))
        print(f"Error details: {str(e)}")
        return 0
}

There's still a TON of nuances to deal with here, but it's a start at least...

Jani, thanks for asking - December 16 is the plan.