feat(bias): implement data models and storage for tool weighting and bias profiles

tests/test_tool_presets_execution.py

@@ -3,14 +3,14 @@ import asyncio
 from src import ai_client
 from src import mcp_client
 from src import models
-from src.models import ToolPreset
+from src.models import ToolPreset, Tool
 from unittest.mock import MagicMock, patch
 
 @pytest.mark.asyncio
 async def test_tool_auto_approval():
  # Setup a preset with read_file as auto
  preset = ToolPreset(name="AutoTest", categories={
-  "General": {"read_file": "auto"}
+  "General": [Tool(name="read_file", approval="auto")]
  })
  
  with patch("src.tool_presets.ToolPresetManager.load_all", return_value={"AutoTest": preset}):
@@ -39,7 +39,7 @@ async def test_tool_auto_approval():
 async def test_tool_ask_approval():
  # Setup a preset with run_powershell as ask
  preset = ToolPreset(name="AskTest", categories={
-  "General": {"run_powershell": "ask"}
+  "General": [Tool(name="run_powershell", approval="ask")]
  })
  
  with patch("src.tool_presets.ToolPresetManager.load_all", return_value={"AskTest": preset}):
@@ -65,7 +65,7 @@ async def test_tool_ask_approval():
 async def test_tool_rejection():
  # Setup a preset with run_powershell as ask
  preset = ToolPreset(name="AskTest", categories={
-  "General": {"run_powershell": "ask"}
+  "General": [Tool(name="run_powershell", approval="ask")]
  })
  
  with patch("src.tool_presets.ToolPresetManager.load_all", return_value={"AskTest": preset}):