Update mcp_servers.py

mcp_servers.py

@@ -1,4 +1,4 @@
-# mcp_servers.py (FIXED: Robust 'str' handling)
+# mcp_servers.py (FIXED: Robust 'str' handling & Enhanced Extraction)
 import asyncio
 import json
 import re
@@ -15,15 +15,19 @@ EVALUATION_PROMPT_TEMPLATE = load_prompt(config.PROMPT_FILES["evaluator"])
 def extract_json(text: str) -> dict:
     """Robustly extracts JSON from text."""
     try:
+        # 1. Try simple block extraction first
         clean_text = text.strip()
         if "```json" in clean_text:
              clean_text = clean_text.split("```json")[1].split("```")[0].strip()
         elif "```" in clean_text:
              clean_text = clean_text.split("```")[1].split("```")[0].strip()
         return json.loads(clean_text)
-    except json.JSONDecodeError:
+    except (json.JSONDecodeError, IndexError):
+        # 2. Fallback: Regex to find the outermost JSON object
         try:
-            match = re.search(r'(\{.*\})', text, re.DOTALL)
+            # Looks for { ... } but matching closest brackets is hard with regex.
+            # This regex grabs from the first { to the last }
+            match = re.search(r'(\{[\s\S]*\})', text)
             if match:
                 return json.loads(match.group(1))
         except:
@@ -52,8 +56,7 @@ class BusinessSolutionEvaluator:
             
             v_fitness = extract_json(response.text)
             
-            # --- FIX 1: Strict Type Checking ---
-            # If LLM returns a string (e.g. "Error") instead of JSON object, raise error to trigger fallback.
+            # Strict Type Checking
             if not isinstance(v_fitness, (dict, list)):
                 raise ValueError(f"Judge returned invalid type: {type(v_fitness)}")
             
@@ -62,7 +65,6 @@ class BusinessSolutionEvaluator:
         
         except Exception as e:
             print(f"ERROR: BusinessSolutionEvaluator failed: {e}")
-            # Return a safe fallback dictionary so the agent doesn't crash
             return {
                 "Novelty": {"score": 1, "justification": f"Error: {str(e)}"},
                 "Usefulness_Feasibility": {"score": 1, "justification": f"Error: {str(e)}"},
@@ -120,22 +122,17 @@ class AgentCalibrator:
                         continue
                     metric = role_metrics[role]
                     
-                    # --- FIX 2: Robust Dict Access ---
-                    # Safely get the score object
+                    # Robust Dict Access
                     raw_score_data = res.get("score", {})
                     
-                    # If it's not a container (e.g. it's a string), reset it
                     if not isinstance(raw_score_data, (dict, list)):
                         raw_score_data = {}
 
-                    # If it's a list, unwrap it
                     if isinstance(raw_score_data, list):
                         raw_score_data = raw_score_data[0] if len(raw_score_data) > 0 else {}
                     
-                    # Now get the specific metric
                     metric_data = raw_score_data.get(metric, {})
 
-                    # Repeat safety checks
                     if not isinstance(metric_data, (dict, list)):
                         metric_data = {}
 
@@ -143,7 +140,6 @@ class AgentCalibrator:
                         metric_data = metric_data[0] if len(metric_data) > 0 else {}
                             
                     score = metric_data.get("score", 0)
-                    # ---------------------------------
 
                     if score > best_score:
                         best_score = score