Add pydantic model tests for MXCP integration

Author: bgaidioz

src/mxcp/sdk/executor/plugins/python.py

@@ -46,6 +46,7 @@
 import hashlib
 import inspect
 import logging
+import sys
 import tempfile
 from collections.abc import Callable
 from pathlib import Path
@@ -555,12 +556,25 @@ async def _execute_function(
         try:
             from ..context import reset_execution_context, set_execution_context
 
+            # Convert parameters based on function signature
+            converted_params = self._convert_parameters(func, params)
+
+            # Handle validation errors
+            if isinstance(converted_params, dict) and "__validation_errors" in converted_params:
+                # Convert structured errors to string for backward compatibility
+                # TODO: In future, return structured errors directly for better UI
+                error_parts = []
+                for param_name, param_errors in converted_params["__validation_errors"].items():
+                    error_details = [f"{err['field']}: {err['message']}" for err in param_errors]
+                    error_parts.append(f"Invalid {param_name}: {', '.join(error_details)}")
+                return "; ".join(error_parts)
+
             # Check if function is async
             if asyncio.iscoroutinefunction(func):
                 # For async functions, set context and let contextvars propagate it
                 context_token = set_execution_context(context)
                 try:
-                    result = await func(**params)
+                    result = await func(**converted_params)
                 finally:
                     reset_execution_context(context_token)
             else:
@@ -570,7 +584,7 @@ def sync_function_wrapper() -> Any:
 
                     thread_token = set_execution_context(context)
                     try:
-                        return func(**params)
+                        return func(**converted_params)
                     finally:
                         reset_execution_context(thread_token)
 
@@ -586,3 +600,155 @@ def sync_function_wrapper() -> Any:
         except Exception as e:
             logger.error(f"Function execution failed: {e}")
             raise
+
+    def _convert_parameters(
+        self, func: Callable[..., Any], params: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Convert parameters based on function signature with comprehensive type support.
+
+        Features:
+        - Uses TypeAdapter for robust type conversion and validation
+        - Supports forward references with proper module namespace context
+        - Collects structured validation errors per parameter for better UI
+        - Uses signature binding for proper parameter handling
+        - Handles **kwargs functions specially to maintain compatibility
+
+        Args:
+            func: The function to call
+            params: Raw parameter dictionary
+
+        Returns:
+            Converted parameters dictionary, or dict with __validation_errors on failure.
+            The __validation_errors contains structured per-parameter error details.
+        """
+        import typing
+
+        from pydantic import ValidationError
+
+        # Get function signature
+        sig = inspect.signature(func)
+
+        # Resolve type hints with proper module context for forward references
+        try:
+            globalns = getattr(func, "__globals__", {})
+            # Use the defining module's namespace for localns (handles more forward-ref edge cases)
+            mod = sys.modules.get(getattr(func, "__module__", ""), None)
+            localns = vars(mod) if mod else globalns
+            type_hints = typing.get_type_hints(
+                func, globalns=globalns, localns=localns, include_extras=True
+            )
+        except (NameError, AttributeError, TypeError) as e:
+            logger.debug(f"Failed to resolve type hints for {func.__name__}: {e}")
+            type_hints = {}
+
+        # Use signature binding for proper parameter mapping, but handle **kwargs specially
+        has_var_keyword = any(
+            p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
+        )
+
+        if has_var_keyword:
+            # For functions with **kwargs, use direct parameter mapping to maintain compatibility
+            bound_params = params
+        else:
+            # For regular functions, use bind_partial for proper parameter handling
+            try:
+                bound_args = sig.bind_partial(**params)
+                bound_args.apply_defaults()
+                bound_params = bound_args.arguments
+            except TypeError as e:
+                # If binding fails, fall back to direct parameter mapping
+                logger.debug(f"Parameter binding failed for {func.__name__}: {e}")
+                bound_params = params
+
+        converted_params = {}
+        validation_errors = {}  # Dict to store per-parameter error details
+
+        for param_name, param_value in bound_params.items():
+            try:
+                # Get resolved type hint, fall back to raw annotation if available
+                param_type = type_hints.get(param_name)
+                if param_type is None and param_name in sig.parameters:
+                    param_type = sig.parameters[param_name].annotation
+
+                # Skip if no type annotation or annotation is Any/object
+                if (
+                    param_type is None
+                    or param_type == inspect.Parameter.empty
+                    or param_type in (typing.Any, object)
+                ):
+                    converted_params[param_name] = param_value
+                    continue
+
+                # Convert the parameter value based on its type
+                converted_value = self._convert_parameter_value(param_name, param_value, param_type)
+                converted_params[param_name] = converted_value
+
+            except ValidationError as ve:
+                # Collect structured validation errors for this parameter
+                param_errors = []
+                for error in ve.errors():
+                    param_errors.append(
+                        {
+                            "field": (
+                                ".".join(str(loc) for loc in error["loc"])
+                                if error["loc"]
+                                else param_name
+                            ),
+                            "message": error["msg"],
+                            "type": error["type"],
+                        }
+                    )
+                validation_errors[param_name] = param_errors
+
+            except Exception as e:
+                # Log unexpected errors with stack trace but continue processing other parameters
+                logger.exception(f"Unexpected error converting parameter '{param_name}': {e}")
+                converted_params[param_name] = param_value
+
+        # Return structured validation errors if any occurred
+        if validation_errors:
+            logger.error(
+                f"Parameter validation failed for parameters: {list(validation_errors.keys())}"
+            )
+            return {"__validation_errors": validation_errors}
+
+        return converted_params
+
+    def _convert_parameter_value(self, param_name: str, param_value: Any, param_type: Any) -> Any:
+        """Convert a single parameter value based on its type annotation.
+
+        Uses TypeAdapter to handle all type conversions uniformly:
+        - Direct models: User
+        - Optional models: Optional[User], User | None
+        - Container types: list[User], dict[str, User], etc.
+        - Complex nested types: dict[str, list[User]]
+        - Non-model types: TypedDicts, dataclasses, primitives
+
+        Args:
+            param_name: Parameter name (for logging/errors)
+            param_value: Raw parameter value
+            param_type: Resolved type annotation
+
+        Returns:
+            Converted parameter value
+
+        Raises:
+            ValidationError: If pydantic validation fails
+        """
+        from pydantic import BaseModel, TypeAdapter
+
+        # Fast path: if value is already a BaseModel instance, keep it as-is
+        if isinstance(param_value, BaseModel):
+            logger.debug(f"Parameter '{param_name}' is already a BaseModel instance")
+            return param_value
+
+        # Try to create TypeAdapter for the parameter type
+        try:
+            adapter = TypeAdapter(param_type)
+            logger.debug(f"Converting parameter '{param_name}' using TypeAdapter for {param_type}")
+            return adapter.validate_python(param_value)
+        except (TypeError, ValueError) as e:
+            # TypeAdapter couldn't be created or type doesn't need validation
+            # This happens for types that don't require special handling
+            logger.debug(f"No TypeAdapter needed for parameter '{param_name}': {e}")
+            return param_value

src/mxcp/sdk/validator/converters.py

@@ -323,8 +323,26 @@ def validate_output(value: Any, schema: TypeSchema) -> None:
                         raise ValidationError(f"Array item {i}: {str(e)}") from e
 
         elif return_type == "object":
+            # Handle pydantic models by converting them to dict first
             if not isinstance(value, dict):
-                raise ValidationError(f"Expected object, got {type(value).__name__}")
+                if hasattr(value, "model_dump"):
+                    # Pydantic v2
+                    try:
+                        value = value.model_dump()
+                    except Exception:
+                        raise ValidationError(
+                            f"Expected object, got {type(value).__name__}"
+                        ) from None
+                elif hasattr(value, "dict"):
+                    # Pydantic v1
+                    try:
+                        value = value.dict()
+                    except Exception:
+                        raise ValidationError(
+                            f"Expected object, got {type(value).__name__}"
+                        ) from None
+                else:
+                    raise ValidationError(f"Expected object, got {type(value).__name__}")
 
             properties = schema.properties or {}
             required = schema.required or []
@@ -366,6 +384,20 @@ def serialize_for_output(obj: Any) -> Any:
         elif hasattr(obj, "isoformat"):
             # Handle any other datetime-like objects
             return obj.isoformat()
+        elif hasattr(obj, "model_dump"):
+            # Handle Pydantic v2 models
+            try:
+                serialized = obj.model_dump()
+                return TypeConverter.serialize_for_output(serialized)
+            except Exception:
+                return obj
+        elif hasattr(obj, "dict"):
+            # Handle Pydantic v1 models
+            try:
+                serialized = obj.dict()
+                return TypeConverter.serialize_for_output(serialized)
+            except Exception:
+                return obj
         else:
             return obj
 

tests/server/fixtures/integration/python/test_container_types.py

@@ -0,0 +1,84 @@
+from typing import List, Dict, Optional
+from pydantic import BaseModel
+
+
+class User(BaseModel):
+    """Simple user model for testing container types."""
+
+    name: str
+    age: int
+    email: str
+
+
+def process_user_list(users: List[User]) -> dict:
+    """Test function that takes a list of pydantic models."""
+    total_age = sum(user.age for user in users)
+    avg_age = total_age / len(users) if users else 0
+    names = [user.name for user in users]
+
+    return {
+        "user_count": len(users),
+        "average_age": avg_age,
+        "names": names,
+        "total_age": total_age,
+    }
+
+
+def process_user_dict(user_map: Dict[str, User]) -> dict:
+    """Test function that takes a dict of pydantic models."""
+    user_count = len(user_map)
+    names = list(user_map.keys())
+    ages = [user.age for user in user_map.values()]
+    avg_age = sum(ages) / len(ages) if ages else 0
+
+    return {
+        "user_count": user_count,
+        "user_keys": sorted(names),
+        "average_age": avg_age,
+        "oldest_user": max(user_map.values(), key=lambda u: u.age).name if user_map else None,
+    }
+
+
+def process_optional_user(user: Optional[User] = None) -> dict:
+    """Test function with optional pydantic model."""
+    if user is None:
+        return {"has_user": False, "message": "No user provided"}
+
+    return {
+        "has_user": True,
+        "user_name": user.name,
+        "user_age": user.age,
+        "user_email": user.email,
+    }
+
+
+# Test functions using built-in container types (Python 3.9+)
+def process_builtin_user_list(users: list[User]) -> dict:
+    """Test function using built-in list[User] (Python 3.9+)."""
+    total_age = sum(user.age for user in users)
+    avg_age = total_age / len(users) if users else 0
+    names = [user.name for user in users]
+
+    return {
+        "user_count": len(users),
+        "average_age": avg_age,
+        "names": names,
+        "total_age": total_age,
+        "type_used": "builtin_list",
+    }
+
+
+def process_builtin_user_dict(user_map: dict[str, User]) -> dict:
+    """Test function using built-in dict[str, User] (Python 3.9+)."""
+    user_count = len(user_map)
+    names = list(user_map.keys())
+    ages = [user.age for user in user_map.values()]
+    avg_age = sum(ages) / len(ages) if ages else 0
+
+    return {
+        "user_count": user_count,
+        "user_keys": sorted(names),
+        "average_age": avg_age,
+        "oldest_user": max(user_map.values(), key=lambda u: u.age).name if user_map else None,
+        "type_used": "builtin_dict",
+    }

tests/server/fixtures/integration/python/test_endpoints.py

@@ -1,5 +1,6 @@
 from typing import Dict, Any
 from mxcp.runtime import config, on_init
+from pydantic import BaseModel
 
 global_var = None
 
@@ -121,3 +122,53 @@ def process_user_data(user_data: Dict[str, Any]) -> Dict[str, Any]:
     }
 
     return {"original_data": user_data, "analysis": analysis, "processing_status": "success"}
+
+
+# Pydantic models for testing
+class UserProfile(BaseModel):
+    """User profile model."""
+
+    name: str
+    age: int
+    email: str
+    is_premium: bool = False
+
+
+class UserStats(BaseModel):
+    """User statistics model."""
+
+    total_users: int
+    active_users: int
+    premium_users: int
+    average_age: float
+
+
+def validate_user_profile(profile: UserProfile) -> str:
+    """Take a pydantic model as parameter and return a primitive result."""
+    # Validate and process the user profile
+    if profile.age < 0:
+        return "Invalid age: must be non-negative"
+
+    if not profile.email or "@" not in profile.email:
+        return "Invalid email format"
+
+    status = "premium" if profile.is_premium else "regular"
+    return f"User {profile.name} ({profile.age} years old, {profile.email}) is a {status} user - validation passed"
+
+
+def get_user_stats(user_count: int) -> UserStats:
+    """Take a primitive argument and return a pydantic model."""
+    # Generate some mock statistics based on the user count
+    active_ratio = 0.8
+    premium_ratio = 0.3
+
+    active_users = int(user_count * active_ratio)
+    premium_users = int(user_count * premium_ratio)
+    average_age = 32.5  # Mock average age
+
+    return UserStats(
+        total_users=user_count,
+        active_users=active_users,
+        premium_users=premium_users,
+        average_age=average_age,
+    )