fix: Complete linting cleanup

servers/legacy/mcp_base_server.py

@@ -17,7 +17,7 @@
 # MCP Protocol imports
 try:
     from mcp.server import Server
-    from mcp.types import Tool, TextContent, ImageContent, EmbeddedResource
+    from mcp.types import Tool, TextContent
     from mcp.server.stdio import stdio_server
     from mcp.server.models import InitializationOptions
 except ImportError:
@@ -261,7 +261,7 @@ async def _load_ml_model_lazy(self):
 
             # Step 4: Test prediction
             progress.update()
-            test_result = self.ml_model(f"Test message for {self.platform_name} initialization")
+            self.ml_model(f"Test message for {self.platform_name} initialization")
 
             progress.update()
             print(f"\n✅ [{self.platform_name.upper()}] Modello ML caricato e testato!")
@@ -371,7 +371,7 @@ async def _handle_search_memory(self, arguments: Dict[str, Any]) -> List[TextCon
     async def _handle_analyze_message(self, arguments: Dict[str, Any]) -> List[TextContent]:
         """Handle analyze_message tool call"""
         message = arguments.get("message", "")
-        platform_context = arguments.get("platform_context", {})
+        arguments.get("platform_context", {})
 
         if not message.strip():
             return [TextContent(type="text", text="❌ Cannot analyze empty message")]

servers/legacy/windsurf_mcp_server.py

@@ -200,7 +200,7 @@ async def _handle_save_code_snippet(self, arguments: Dict[str, Any]) -> List[Tex
         }
 
         # Save using base functionality
-        save_result = await self._handle_save_memory({
+        await self._handle_save_memory({
             "content": content,
             "context": context
         })

src/core/hybrid_trigger_system.py

@@ -203,7 +203,6 @@ async def _combine_predictions(
     ) -> HybridPrediction:
         """Combine predictions from both systems"""
 
-        reasoning = []
 
         # Mode-specific logic
         if self.mode == TriggerMode.DETERMINISTIC:

src/core/mcp_server_enhanced.py

@@ -329,7 +329,7 @@ async def _handle_register_conversation(self, arguments: Dict[str, Any]) -> List
         try:
             messages = arguments.get("messages", [])
             platform = arguments.get("platform", "unknown")
-            context = arguments.get("context", {})
+            arguments.get("context", {})
 
             # Store conversation in buffer
             self.conversation_buffer[platform] = messages

src/core/ml_trigger_system.py

@@ -15,20 +15,17 @@
 
 # ML imports
 try:
-    from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+    from sklearn.ensemble import GradientBoostingClassifier
     from sklearn.feature_extraction.text import TfidfVectorizer
     from sklearn.metrics.pairwise import cosine_similarity
     from sklearn.preprocessing import StandardScaler
-    from sklearn.neural_network import MLPClassifier
+
     HAS_SKLEARN = True
 except ImportError:
     HAS_SKLEARN = False
 
 try:
-    import torch
-    import torch.nn as nn
-    import torch.optim as optim
-    from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification, pipeline
+    from transformers import pipeline
     HAS_TORCH = True
 except ImportError:
     HAS_TORCH = False

src/core/simple_mcp_server.py

@@ -233,7 +233,7 @@ async def handle_call_tool(name: str, arguments: dict) -> List[types.TextContent
                     # Save memory
                     memory = await self.db.save_memory(content, importance, memory_type, metadata)
 
-                    result = {
+                    {
                         "success": True,
                         "memory_id": memory["id"],
                         "content": content,

src/services/health_service.py

@@ -37,7 +37,7 @@ async def check_all(self) -> bool:
     async def get_health_status(self) -> HealthCheckResponse:
         """Get comprehensive health status"""
         try:
-            start_time = time.time()
+            time.time()
 
             # Check individual services
             db_health = await database_service.health_check()

src/services/memory_service.py

@@ -141,24 +141,18 @@ async def auto_save_memory(
             importance = await self.embedding_service.analyze_memory_importance(content, context)
 
             # Determine memory type from context
-            memory_type = MemoryType.CONVERSATION
             trigger_type = "ml_trigger"
 
             if context:
                 if context.get("type") == "function_result":
-                    memory_type = MemoryType.FUNCTION
                     trigger_type = "function_result"
                 elif context.get("level") == "error":
-                    memory_type = MemoryType.ERROR
                     trigger_type = "error"
                 elif context.get("level") == "warning":
-                    memory_type = MemoryType.WARNING
                     trigger_type = "warning"
                 elif "decision" in content.lower():
-                    memory_type = MemoryType.DECISION
                     trigger_type = "decision"
                 elif any(keyword in content.lower() for keyword in ["knowledge", "fact", "information"]):
-                    memory_type = MemoryType.KNOWLEDGE
                     trigger_type = "knowledge"
 
             # Create memory

src/training/comprehensive_dataset_builder.py

@@ -11,9 +11,9 @@
 
 # ML imports
 try:
-    from datasets import Dataset, DatasetDict, load_dataset, concatenate_datasets
+    from datasets import Dataset, DatasetDict, load_dataset
     from sklearn.model_selection import train_test_split
-    from huggingface_hub import upload_file, create_repo, login
+    from huggingface_hub import create_repo, login
     HAS_DATASETS = True
 except ImportError:
     HAS_DATASETS = False

src/training/dataset_builder.py

@@ -15,7 +15,7 @@
 # ML imports
 try:
     from datasets import Dataset, DatasetDict, load_dataset
-    from transformers import AutoTokenizer
+
     from sklearn.model_selection import train_test_split
     HAS_DATASETS = True
 except ImportError:

src/training/ml_training_pipeline.py

@@ -16,8 +16,7 @@
     from sklearn.model_selection import train_test_split, cross_val_score
     from sklearn.metrics import classification_report, confusion_matrix
     from sklearn.ensemble import RandomForestClassifier
-    import matplotlib.pyplot as plt
-    import pandas as pd
+
     HAS_SKLEARN = True
 except ImportError:
     HAS_SKLEARN = False

tests/integration/test_full_workflow.py

@@ -143,7 +143,7 @@ async def test_auto_save_workflow(self, services):
         """Test auto-save workflow"""
         # Arrange
         memory_service = services["memory"]
-        embedding_service = services["embedding"]
+        services["embedding"]
 
         # Test content that should trigger auto-save
         test_content = """

tests/test_advanced_services.py

@@ -90,7 +90,7 @@ async def test_cache_service():
             "importance": 0.8
         }
 
-        cache_key = await cache_service.get_memory_cache_key("test_memory_123")
+        await cache_service.get_memory_cache_key("test_memory_123")
         await cache_service.cache_memory(memory_data)
         cached_memory = await cache_service.get_cached_memory("test_memory_123")
         print(f"✅ Memory cache: {cached_memory is not None}")