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AI Development Guidelines MCP Server

analyze_feedback.py3.28 kB
#!/usr/bin/env python3 """Analyze user feedback and usage patterns.""" import json from pathlib import Path from datetime import datetime from collections import Counter, defaultdict def analyze_feedback(): """Analyze collected feedback data.""" print("Analyzing feedback and usage patterns...") feedback_dir = Path("feedback") if not feedback_dir.exists(): print("No feedback data found. Creating sample analytics...") feedback_dir.mkdir(exist_ok=True) sample_data = { "message": "No feedback data collected yet", "recommendations": [ "Start collecting MCP call metrics", "Track tool usage patterns", "Monitor token consumption", "Collect user ratings and comments" ] } analytics_dir = Path("analytics") analytics_dir.mkdir(exist_ok=True) with open(analytics_dir / "feedback_analysis.json", 'w') as f: json.dump(sample_data, f, indent=2) print("✓ Sample analytics created") return feedback_files = list(feedback_dir.glob("*.json")) if not feedback_files: print("No feedback files found") return tool_usage = Counter() token_usage = defaultdict(list) response_times = defaultdict(list) for file in feedback_files: with open(file, 'r') as f: data = json.load(f) if "tool_name" in data: tool_usage[data["tool_name"]] += 1 if "tokens_used" in data: tool = data.get("tool_name", "unknown") token_usage[tool].append(data["tokens_used"]) if "response_time_ms" in data: tool = data.get("tool_name", "unknown") response_times[tool].append(data["response_time_ms"]) analytics = { "analysis_time": datetime.now().isoformat(), "total_calls": sum(tool_usage.values()), "tool_usage": dict(tool_usage), "token_stats": { tool: { "total": sum(tokens), "avg": sum(tokens) / len(tokens) if tokens else 0, "max": max(tokens) if tokens else 0, "min": min(tokens) if tokens else 0 } for tool, tokens in token_usage.items() }, "response_time_stats": { tool: { "avg_ms": sum(times) / len(times) if times else 0, "max_ms": max(times) if times else 0, "min_ms": min(times) if times else 0 } for tool, times in response_times.items() } } analytics_dir = Path("analytics") analytics_dir.mkdir(exist_ok=True) output_file = analytics_dir / f"feedback_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" with open(output_file, 'w') as f: json.dump(analytics, f, indent=2) print(f"✓ Analytics saved to {output_file}") print(f" Total calls analyzed: {analytics['total_calls']}") print(f" Most used tool: {tool_usage.most_common(1)[0] if tool_usage else 'N/A'}") if __name__ == "__main__": analyze_feedback()

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