feat(bias): implement ToolBiasEngine and integrate into ai_client orchestration loop

src/tool_bias.py

@@ -0,0 +1,55 @@
+from typing import List, Dict, Any, Optional
+from src.models import Tool, ToolPreset, BiasProfile
+
+class ToolBiasEngine:
+ def apply_semantic_nudges(self, tool_definitions: List[Dict[str, Any]], preset: ToolPreset) -> List[Dict[str, Any]]:
+  weight_map = {
+   5: "[HIGH PRIORITY] ",
+   4: "[PREFERRED] ",
+   2: "[NOT RECOMMENDED] ",
+   1: "[LOW PRIORITY] "
+  }
+  
+  preset_tools: Dict[str, Tool] = {}
+  for cat_tools in preset.categories.values():
+   for t in cat_tools:
+    if isinstance(t, Tool):
+     preset_tools[t.name] = t
+  
+  for defn in tool_definitions:
+   name = defn.get("name")
+   if name in preset_tools:
+    tool = preset_tools[name]
+    prefix = weight_map.get(tool.weight, "")
+    if prefix:
+     defn["description"] = prefix + defn.get("description", "")
+    
+    if tool.parameter_bias:
+     params = defn.get("parameters") or defn.get("input_schema")
+     if params and "properties" in params:
+      props = params["properties"]
+      for p_name, bias in tool.parameter_bias.items():
+       if p_name in props:
+        p_desc = props[p_name].get("description", "")
+        props[p_name]["description"] = f"[{bias}] {p_desc}".strip()
+  
+  return tool_definitions
+
+ def generate_tooling_strategy(self, preset: ToolPreset, global_bias: BiasProfile) -> str:
+  lines = ["### Tooling Strategy"]
+  
+  preferred = []
+  for cat_tools in preset.categories.values():
+   for t in cat_tools:
+    if isinstance(t, Tool) and t.weight >= 4:
+     preferred.append(t.name)
+  
+  if preferred:
+   lines.append(f"Preferred tools: {', '.join(preferred)}.")
+  
+  if global_bias.category_multipliers:
+   lines.append("Category focus multipliers:")
+   for cat, mult in global_bias.category_multipliers.items():
+    lines.append(f"- {cat}: {mult}x")
+    
+  return "\n\n".join(lines)