dune-mcp

Instructions

Detect outliers (Z-score > 3) and trends in query results.

Implementation Reference

• src/main.py:265-301 (handler)

  The handler and registration for the 'analyze_results' MCP tool. This function checks the job status, fetches analysis from dune_service.analyze_result, and returns a formatted summary of numeric columns including means, standard deviations, outliers (Z-score >3), and trend heuristics.

  @mcp.tool()
  def analyze_results(job_id: str) -> str:
      """
      Detect outliers (Z-score > 3) and trends in query results.
      """
      try:
          # Check status first
          status_data = dune_service.get_status(job_id)
          state = status_data.get("state", "UNKNOWN")
          
          if state != "QUERY_STATE_COMPLETED" and state != "COMPLETED":
              return f"Job is not complete (Status: {state}). Cannot analyze."
  
          analysis = dune_service.analyze_result(job_id, data_processor)
          
          if "error" in analysis:
              return f"Analysis Failed: {analysis['error']}"
              
          summary = [f"Analysis for Job {job_id} ({analysis['row_count']} rows):"]
          
          for col, stats in analysis.get("numeric_analysis", {}).items():
              summary.append(f"\nColumn: {col}")
              summary.append(f"  Mean: {stats.get('mean'):.2f} | StdDev: {stats.get('std_dev'):.2f}")
              
              outliers = stats.get("outlier_count", 0)
              if outliers > 0:
                  summary.append(f"  Outliers detected: {outliers} values (>3 sigma)")
                  summary.append(f"  Sample outliers: {stats.get('top_outliers')}")
              else:
                  summary.append("  No significant outliers.")
                  
              if "trend_heuristic" in stats:
                  summary.append(f"  Trend: {stats['trend_heuristic']}")
                  
          return "\n".join(summary)
      except Exception as e:
          return f"Error analyzing results: {str(e)}"