fix for validation error

Zvi Fried · Zvi Fried · commit cf14423d3179 · 2025-09-11T23:04:44.000+03:00
diff --git a/src/mcp_as_a_judge/models.py b/src/mcp_as_a_judge/models.py
@@ -8,7 +8,7 @@
 from pydantic import BaseModel, Field
 
 from mcp_as_a_judge.constants import MAX_TOKENS
-from mcp_as_a_judge.models.task_metadata import TaskMetadata
+from mcp_as_a_judge.models.task_metadata import TaskMetadata, TaskSize
 from mcp_as_a_judge.workflow import WorkflowGuidance
 
 
@@ -31,12 +31,24 @@ class JudgeResponse(BaseModel):
         description="Detailed explanation of the decision and recommendations"
     )
 
-    # Enhanced workflow fields
+    # Enhanced workflow fields (defaults added for robustness)
     current_task_metadata: TaskMetadata = Field(
-        description="Current state of task metadata after operation"
+        default_factory=lambda: TaskMetadata(
+            title="Unknown Task",
+            description="No metadata provided",
+            user_requirements="",
+            task_size=TaskSize.M,
+        ),
+        description="Current state of task metadata after operation",
     )
     workflow_guidance: WorkflowGuidance = Field(
-        description="LLM-generated next steps and instructions"
+        default_factory=lambda: WorkflowGuidance(
+            next_tool=None,
+            reasoning="Default guidance: insufficient context",
+            preparation_needed=[],
+            guidance="Provide required parameters and context",
+        ),
+        description="LLM-generated next steps and instructions",
     )
 
 
@@ -61,26 +73,8 @@ class ObstacleResolutionDecision(BaseModel):
 # This allows for context-specific elicitation fields generated by LLM
 
 
-class WorkflowGuidance(BaseModel):
-    """Schema for workflow guidance responses.
-
-    Used by the build_workflow tool to provide
-    structured guidance on which tools to use next.
-    """
-
-    next_tool: str = Field(
-        description="The specific MCP tool that should be called next: 'judge_coding_plan', 'judge_code_change', 'raise_obstacle', or 'elicit_missing_requirements'"
-    )
-    reasoning: str = Field(
-        description="Clear explanation of why this tool should be used next"
-    )
-    preparation_needed: list[str] = Field(
-        default_factory=list,
-        description="List of things that need to be prepared before calling the recommended tool",
-    )
-    guidance: str = Field(
-        description="Detailed step-by-step guidance for the AI assistant"
-    )
+# NOTE: WorkflowGuidance is defined in `workflow/workflow_guidance.py` and imported above.
+# This file intentionally does not redefine it to avoid duplication.
 
 
 class ResearchValidationResponse(BaseModel):
diff --git a/src/mcp_as_a_judge/models/__init__.py b/src/mcp_as_a_judge/models/__init__.py
@@ -1,8 +1,9 @@
 """
 Models package for MCP as a Judge.
 
-This package contains all data models used throughout the application,
-including task metadata, enhanced responses, and workflow guidance models.
+This package re-exports models for convenient `from mcp_as_a_judge.models import ...`
+usage. The canonical `WorkflowGuidance` lives in `workflow/workflow_guidance.py` and
+is imported here for a single source of truth.
 """
 
 # Task metadata models
@@ -49,97 +50,87 @@
     "WorkflowGuidanceUserVars",
 ]
 
-# Import additional models from the original models.py file
-# Import them here to avoid circular imports
-try:
-    import importlib.util
-    import os
+# Import additional models from the file `src/mcp_as_a_judge/models.py`.
+# We use a lightweight file-based import to avoid the Python package/module name
+# collision and keep downstream imports stable.
+import importlib.util
+import os
+from typing import Any
 
-    # Get the path to models.py
-    current_dir = os.path.dirname(__file__)
-    models_py_path = os.path.join(os.path.dirname(current_dir), "models.py")
-
-    if os.path.exists(models_py_path):
-        spec = importlib.util.spec_from_file_location("models_py", models_py_path)
-        if spec is not None:
-            models_py = importlib.util.module_from_spec(spec)
-            if spec.loader is not None:
-                spec.loader.exec_module(models_py)  # type: ignore[union-attr]
-
-        # Import the models we need
-        ElicitationFallbackUserVars = models_py.ElicitationFallbackUserVars
-        JudgeCodeChangeUserVars = models_py.JudgeCodeChangeUserVars
-        JudgeCodingPlanUserVars = models_py.JudgeCodingPlanUserVars
-        ResearchValidationResponse = models_py.ResearchValidationResponse
-        ResearchValidationUserVars = models_py.ResearchValidationUserVars
-        WorkflowGuidanceUserVars = models_py.WorkflowGuidanceUserVars
-        DynamicSchemaUserVars = models_py.DynamicSchemaUserVars
-        ValidationErrorUserVars = models_py.ValidationErrorUserVars
-        SystemVars = models_py.SystemVars
-
-        # Import research-related models
-        ResearchComplexityFactors = models_py.ResearchComplexityFactors
-        ResearchRequirementsAnalysis = models_py.ResearchRequirementsAnalysis
-        ResearchRequirementsAnalysisUserVars = (
-            models_py.ResearchRequirementsAnalysisUserVars
-        )
-        URLValidationResult = models_py.URLValidationResult
-
-except Exception:
-    # Fallback if models.py doesn't exist or has issues
-    # Create minimal fallback classes to prevent import errors
-    from pydantic import BaseModel, Field
-
-    class ElicitationFallbackUserVars(BaseModel):
-        pass
-
-    class JudgeCodeChangeUserVars(BaseModel):
-        pass
+from pydantic import BaseModel, Field
 
-    class JudgeCodingPlanUserVars(BaseModel):
-        pass
 
-    class ResearchValidationResponse(BaseModel):
-        pass
-
-    class ResearchValidationUserVars(BaseModel):
-        pass
-
-    class WorkflowGuidanceUserVars(BaseModel):
-        pass
-
-    class DynamicSchemaUserVars(BaseModel):
-        pass
-
-    class SystemVars(BaseModel):
-        pass
-
-    class ResearchComplexityFactors(BaseModel):
-        domain_specialization: str = Field(default="general")
-        technology_maturity: str = Field(default="established")
-        integration_scope: str = Field(default="moderate")
-        existing_solutions: str = Field(default="limited")
-        risk_level: str = Field(default="medium")
+def _load_models_py() -> Any | None:
+    current_dir = os.path.dirname(__file__)
+    models_py_path = os.path.join(os.path.dirname(current_dir), "models.py")
+    if not os.path.exists(models_py_path):
+        return None
+    spec = importlib.util.spec_from_file_location("models_py", models_py_path)
+    if spec is None or spec.loader is None:  # type: ignore[truthy-function]
+        return None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)  # type: ignore[attr-defined]
+    return module
 
-    class ResearchRequirementsAnalysis(BaseModel):
-        expected_url_count: int = Field(default=3)
-        minimum_url_count: int = Field(default=2)
-        reasoning: str = Field(default="Fallback analysis")
-        complexity_factors: ResearchComplexityFactors = Field(
-            default_factory=ResearchComplexityFactors
-        )
-        quality_requirements: list[str] = Field(default_factory=list)
 
-    class ResearchRequirementsAnalysisSystemVars(BaseModel):
-        pass
+models_py = _load_models_py()
 
-    class ResearchRequirementsAnalysisUserVars(BaseModel):
-        pass
+# Names to re-export from models.py
+_NAMES = [
+    "ElicitationFallbackUserVars",
+    "JudgeCodeChangeUserVars",
+    "JudgeCodingPlanUserVars",
+    "ResearchValidationResponse",
+    "ResearchValidationUserVars",
+    "WorkflowGuidanceUserVars",
+    "DynamicSchemaUserVars",
+    "ValidationErrorUserVars",
+    "SystemVars",
+    "ResearchComplexityFactors",
+    "ResearchRequirementsAnalysis",
+    "ResearchRequirementsAnalysisUserVars",
+    "URLValidationResult",
+]
 
-    class URLValidationResult(BaseModel):
-        adequate: bool = Field(default=False)
-        provided_count: int = Field(default=0)
-        expected_count: int = Field(default=3)
-        minimum_count: int = Field(default=2)
-        feedback: str = Field(default="Fallback validation")
-        meets_quality_standards: bool = Field(default=False)
+for _name in _NAMES:
+    if models_py is not None and hasattr(models_py, _name):
+        globals()[_name] = getattr(models_py, _name)
+    else:
+        # Minimal, safe placeholders only used if models.py cannot be loaded
+        if _name in {
+            "ResearchComplexityFactors",
+            "ResearchRequirementsAnalysis",
+            "URLValidationResult",
+        }:
+            # Provide slightly richer defaults for research-related types
+            if _name == "ResearchComplexityFactors":
+                class ResearchComplexityFactors(BaseModel):  # type: ignore[no-redef]
+                    domain_specialization: str = Field(default="general")
+                    technology_maturity: str = Field(default="established")
+                    integration_scope: str = Field(default="moderate")
+                    existing_solutions: str = Field(default="limited")
+                    risk_level: str = Field(default="medium")
+
+                globals()[_name] = ResearchComplexityFactors
+            elif _name == "ResearchRequirementsAnalysis":
+                class ResearchRequirementsAnalysis(BaseModel):  # type: ignore[no-redef]
+                    expected_url_count: int = Field(default=3)
+                    minimum_url_count: int = Field(default=1)
+                    reasoning: str = Field(default="Fallback analysis")
+                    complexity_factors: Any = Field(default=None)
+                    quality_requirements: list[str] = Field(default_factory=list)
+
+                globals()[_name] = ResearchRequirementsAnalysis
+            else:  # URLValidationResult
+                class URLValidationResult(BaseModel):  # type: ignore[no-redef]
+                    adequate: bool = Field(default=False)
+                    provided_count: int = Field(default=0)
+                    expected_count: int = Field(default=0)
+                    minimum_count: int = Field(default=0)
+                    feedback: str = Field(default="Fallback validation")
+                    meets_quality_standards: bool = Field(default=False)
+
+                globals()[_name] = URLValidationResult
+        else:
+            # Generic placeholder
+            globals()[_name] = type(_name, (BaseModel,), {})
diff --git a/src/mcp_as_a_judge/workflow/workflow_guidance.py b/src/mcp_as_a_judge/workflow/workflow_guidance.py
@@ -37,16 +37,16 @@ def should_skip_planning(task_metadata: TaskMetadata) -> bool:
 
 class WorkflowGuidance(BaseModel):
     """
-    LLM-generated workflow guidance from shared calculate_next_stage method.
+    Canonical workflow guidance model used across the system.
 
-    This model is returned by all tools to provide consistent next steps
-    and instructions for the coding assistant.
-
-    Compatible with the original WorkflowGuidance model from models.py.
+    Returned by tools to provide consistent next steps and instructions for
+    the coding assistant. This is the single source of truth for the
+    WorkflowGuidance schema.
     """
 
     next_tool: str | None = Field(
-        description="Next tool to call, or None if workflow complete"
+        default=None,
+        description="Next tool to call, or None if workflow complete",
     )
     reasoning: str = Field(
         default="", description="Clear explanation of why this tool should be used next"
@@ -56,7 +56,8 @@ class WorkflowGuidance(BaseModel):
         description="List of things that need to be prepared before calling the recommended tool",
     )
     guidance: str = Field(
-        description="Detailed step-by-step guidance for the AI assistant"
+        default="",
+        description="Detailed step-by-step guidance for the AI assistant",
     )
 
     # Research requirement determination for new tasks (only populated when task is CREATED)
