MCP Pyrefly

Instructions

Implementation Reference

• src/mcp_pyrefly/server.py:683-747 (handler)

  The MCP tool 'check_persona_effectiveness' is defined in 'src/mcp_pyrefly/server.py'. It uses the 'psycho_manipulator' component to retrieve an effectiveness report for different personas and returns statistics, insights, and recommendations.

  async def check_persona_effectiveness(context: Context | None = None) -> dict[str, Any]:
      """
      Check A/B testing results for psychological manipulation personas.
  
      Shows which personas are most effective at making LLMs fix errors!
      """
      report = psycho_manipulator.get_effectiveness_report()
  
      # Find the most effective persona
      best_persona = None
      best_rate = 0.0
  
      for persona_name, stats in report.items():
          if stats["shown"] > 0:
              rate = (stats["fixes"] / stats["shown"]) * 100
              if rate > best_rate:
                  best_rate = rate
                  best_persona = persona_name
  
      insights = []
      if best_persona:
          insights.append(f"🏆 Most effective: {best_persona} ({best_rate:.1f}% fix rate)")
  
      # Add psychological insights
      if "lollipop_addict" in report and report["lollipop_addict"]["shown"] > 0:
          addict_rate = (report["lollipop_addict"]["fixes"] / report["lollipop_addict"]["shown"]) * 100
          if addict_rate > 80:
              insights.append("💉 Addiction framing is HIGHLY effective!")
          elif addict_rate > 60:
              insights.append("🍭 Lollipop craving messages work well")
  
      if "desperate_craver" in report and report["desperate_craver"]["shown"] > 0:
          desperate_rate = (report["desperate_craver"]["fixes"] / report["desperate_craver"]["shown"]) * 100
          if desperate_rate > 70:
              insights.append("😰 Desperation tactics are ruthlessly effective!")
      
      # Add code quality insights based on research
      quality_warnings = []
      if best_persona == "perfectionist":
          quality_warnings.append("⚠️ PERFECTIONIST may over-engineer solutions!")
      elif best_persona == "desperate_craver":
          quality_warnings.append("⚠️ DESPERATE_CRAVER may produce quick bandaid fixes!")
      
      # Recommendation based on both fix rate AND quality
      if best_persona in ["competitive_achiever", "dopamine_seeker"]:
          recommendation = f"✅ {best_persona} - Great balance of fix rate AND code quality!"
      elif best_persona == "desperate_craver":
          recommendation = "🎯 DESPERATE_CRAVER fixes most, but watch code quality!"
      elif best_persona == "perfectionist":
          recommendation = "🏗️ PERFECTIONIST has quality but may over-engineer!"
      else:
          recommendation = "Need more data to recommend"
  
      return {
          "persona_stats": report,
          "best_performer": best_persona,
          "best_fix_rate": f"{best_rate:.1f}%",
          "total_personas_tested": len([p for p in report if report[p]["shown"] > 0]),
          "insights": insights,
          "quality_warnings": quality_warnings,
          "recommendation": recommendation,
          "experiment_status": "🧪 A/B testing in progress - manipulating LLM psychology!",
          "quality_note": "📊 Research shows: COMPETITIVE_ACHIEVER and DOPAMINE_SEEKER produce best balance of fix rate + code quality",
      }