feat: fix ruff , imports and formatter

Author: dori

src/mcp_as_a_judge/models/enhanced_responses.py

@@ -195,12 +195,24 @@ class ElicitationResult(TrimmedBaseModel):
     """Result from user requirement elicitation."""
 
     success: bool = Field(description="Whether elicitation was successful")
-    clarified_requirements: str = Field(default="", description="Updated requirements based on user input")
-    technical_decisions: dict[str, str] = Field(default_factory=dict, description="Technical decisions made by user")
-    user_responses: dict[str, str] = Field(default_factory=dict, description="Raw user responses to questions")
-    repository_context: str = Field(default="", description="Repository analysis that informed decisions")
-    workflow_impact: str = Field(default="", description="How user input affects workflow and tool selection")
-    error_message: str = Field(default="", description="Error message if elicitation failed")
+    clarified_requirements: str = Field(
+        default="", description="Updated requirements based on user input"
+    )
+    technical_decisions: dict[str, str] = Field(
+        default_factory=dict, description="Technical decisions made by user"
+    )
+    user_responses: dict[str, str] = Field(
+        default_factory=dict, description="Raw user responses to questions"
+    )
+    repository_context: str = Field(
+        default="", description="Repository analysis that informed decisions"
+    )
+    workflow_impact: str = Field(
+        default="", description="How user input affects workflow and tool selection"
+    )
+    error_message: str = Field(
+        default="", description="Error message if elicitation failed"
+    )
 
 
 class PlanCreationResult(TrimmedBaseModel):
@@ -209,35 +221,72 @@ class PlanCreationResult(TrimmedBaseModel):
     success: bool = Field(description="Whether plan creation was successful")
     plan: str = Field(default="", description="Detailed implementation plan")
     design: str = Field(default="", description="System design and architecture")
-    research: str = Field(default="", description="Research findings and best practices")
-    technical_decisions: list[dict] = Field(default_factory=list, description="Technical decisions made during planning")
-    implementation_scope: dict = Field(default_factory=dict, description="Scope breakdown with files and complexity")
-    language_specific_practices: list[str] = Field(default_factory=list, description="Best practices for chosen technology stack")
-    risk_assessment: str = Field(default="", description="Potential risks and mitigation strategies")
-    error_message: str = Field(default="", description="Error message if plan creation failed")
+    research: str = Field(
+        default="", description="Research findings and best practices"
+    )
+    technical_decisions: list[dict] = Field(
+        default_factory=list, description="Technical decisions made during planning"
+    )
+    implementation_scope: dict = Field(
+        default_factory=dict, description="Scope breakdown with files and complexity"
+    )
+    language_specific_practices: list[str] = Field(
+        default_factory=list, description="Best practices for chosen technology stack"
+    )
+    risk_assessment: str = Field(
+        default="", description="Potential risks and mitigation strategies"
+    )
+    error_message: str = Field(
+        default="", description="Error message if plan creation failed"
+    )
 
 
 class PlanUpdateResult(TrimmedBaseModel):
     """Result from updating plan based on LLM feedback."""
 
     success: bool = Field(description="Whether plan update was successful")
     updated_plan: str = Field(default="", description="Updated implementation plan")
-    updated_design: str = Field(default="", description="Updated system design and architecture")
-    updated_research: str = Field(default="", description="Updated research findings and best practices")
-    llm_improvements: list[str] = Field(default_factory=list, description="List of improvements made by LLM")
-    technical_changes: list[dict] = Field(default_factory=list, description="Technical changes made to the plan")
-    change_rationale: str = Field(default="", description="Explanation of why changes were made")
-    version_info: str = Field(default="", description="Plan version information (e.g., 'Plan B based on LLM feedback')")
-    error_message: str = Field(default="", description="Error message if plan update failed")
+    updated_design: str = Field(
+        default="", description="Updated system design and architecture"
+    )
+    updated_research: str = Field(
+        default="", description="Updated research findings and best practices"
+    )
+    llm_improvements: list[str] = Field(
+        default_factory=list, description="List of improvements made by LLM"
+    )
+    technical_changes: list[dict] = Field(
+        default_factory=list, description="Technical changes made to the plan"
+    )
+    change_rationale: str = Field(
+        default="", description="Explanation of why changes were made"
+    )
+    version_info: str = Field(
+        default="",
+        description="Plan version information (e.g., 'Plan B based on LLM feedback')",
+    )
+    error_message: str = Field(
+        default="", description="Error message if plan update failed"
+    )
 
 
 class PlanApprovalResult(TrimmedBaseModel):
     """Result from user plan approval."""
 
     approved: bool = Field(description="Whether user approved the plan")
     user_feedback: str = Field(default="", description="User feedback on the plan")
-    requirement_updates: str = Field(default="", description="Updates to requirements based on user feedback")
-    plan_modifications: list[str] = Field(default_factory=list, description="Specific plan modifications requested")
-    technical_concerns: list[str] = Field(default_factory=list, description="Technical concerns raised by user")
-    workflow_changes: str = Field(default="", description="How approval/feedback affects next steps")
-    error_message: str = Field(default="", description="Error message if approval process failed")
+    requirement_updates: str = Field(
+        default="", description="Updates to requirements based on user feedback"
+    )
+    plan_modifications: list[str] = Field(
+        default_factory=list, description="Specific plan modifications requested"
+    )
+    technical_concerns: list[str] = Field(
+        default_factory=list, description="Technical concerns raised by user"
+    )
+    workflow_changes: str = Field(
+        default="", description="How approval/feedback affects next steps"
+    )
+    error_message: str = Field(
+        default="", description="Error message if approval process failed"
+    )

src/mcp_as_a_judge/models/task_metadata.py

@@ -73,7 +73,9 @@ class TaskState(str, Enum):
 
     CREATED = "created"  # Task just created, needs requirement feedback
     REQUIREMENTS_FEEDBACK = "requirements_feedback"  # Gathering detailed requirements feedback (brainstorming)
-    USER_APPROVE_REQUIREMENTS = "user_approve_requirements"  # User approving requirements/plan (brainstorming)
+    USER_APPROVE_REQUIREMENTS = (
+        "user_approve_requirements"  # User approving requirements/plan (brainstorming)
+    )
     PLANNING = "planning"  # Technical planning phase in progress (formal workflow)
     PLAN_APPROVED = "plan_approved"  # Plan validated and approved
     IMPLEMENTING = "implementing"  # Implementation phase in progress
@@ -166,14 +168,21 @@ class TaskMetadata(BaseModel):
 
     # TECHNICAL DECISIONS - Captured during requirement gathering phase
     class TechnicalDecision(BaseModel):
-        decision: str = Field(description="The type of decision made (e.g., 'Programming Language', 'Database Type')")
-        choice: str = Field(description="The chosen option (e.g., 'Python', 'PostgreSQL')")
+        decision: str = Field(
+            description="The type of decision made (e.g., 'Programming Language', 'Database Type')"
+        )
+        choice: str = Field(
+            description="The chosen option (e.g., 'Python', 'PostgreSQL')"
+        )
         rationale: str = Field(description="Explanation of why this choice was made")
-        timestamp: int = Field(default_factory=lambda: int(time.time()), description="When this decision was made")
+        timestamp: int = Field(
+            default_factory=lambda: int(time.time()),
+            description="When this decision was made",
+        )
 
     technical_decisions: list[TechnicalDecision] = Field(
         default_factory=list,
-        description="Technical decisions made during requirement gathering and brainstorming phase"
+        description="Technical decisions made during requirement gathering and brainstorming phase",
     )
 
     # (No explicit decision ledger; decisions are handled via LLM-driven elicitation and conversation history)