#!/usr/bin/env python3
"""
Analyze only recent transcript entries from current session after MCP fix.
Focus on actual tool usage patterns, not fabricated data.
"""
import json
from pathlib import Path
from datetime import datetime, timedelta
from collections import defaultdict
def analyze_recent_transcript(transcript_path: Path, hours_back: int = 2):
"""Analyze recent entries in transcript file."""
# Get cutoff time
cutoff_time = datetime.utcnow() - timedelta(hours=hours_back)
tool_usage = defaultdict(int)
mcp_calls = []
grep_calls = []
read_calls = []
tool_sequences = []
current_sequence = []
with open(transcript_path, 'r') as f:
for line in f:
try:
entry = json.loads(line.strip())
# Skip if too old
timestamp_str = entry.get('timestamp', '')
if timestamp_str:
try:
timestamp = datetime.fromisoformat(timestamp_str.replace('Z', '+00:00'))
if timestamp < cutoff_time:
continue
except:
continue
# Look for assistant messages with tool calls
if entry.get('type') == 'assistant' and entry.get('message'):
message = entry['message']
content = message.get('content', [])
for item in content:
if isinstance(item, dict) and item.get('type') == 'tool_use':
tool_name = item.get('name', '')
tool_input = item.get('input', {})
tool_usage[tool_name] += 1
current_sequence.append(tool_name)
# Track specific tools
if tool_name.startswith('mcp__'):
mcp_calls.append({
'tool': tool_name,
'params': tool_input,
'timestamp': timestamp_str
})
elif tool_name == 'Grep':
grep_calls.append({
'pattern': tool_input.get('pattern', ''),
'path': tool_input.get('path', ''),
'timestamp': timestamp_str
})
elif tool_name == 'Read':
read_calls.append({
'file': tool_input.get('file_path', ''),
'offset': tool_input.get('offset', 0),
'limit': tool_input.get('limit', None),
'timestamp': timestamp_str
})
# New user message starts new sequence
if entry.get('type') == 'user' and current_sequence:
if len(current_sequence) > 1:
tool_sequences.append(current_sequence)
current_sequence = []
except json.JSONDecodeError:
continue
return {
'tool_usage': dict(tool_usage),
'mcp_calls': mcp_calls,
'grep_calls': grep_calls,
'read_calls': read_calls,
'sequences': tool_sequences
}
def main():
"""Analyze recent transcript from current session."""
# Current session transcript
transcript_path = Path.home() / ".claude/projects/-workspaces-Code-Index-MCP/2ffb60c4-3fc8-41b4-b5da-5100ddb55f35.jsonl"
print("Analyzing Recent MCP Usage (Last 2 Hours)")
print("=" * 80)
result = analyze_recent_transcript(transcript_path, hours_back=2)
# Summary
print(f"\nTool Usage Summary:")
for tool, count in sorted(result['tool_usage'].items(), key=lambda x: x[1], reverse=True):
print(f" {tool}: {count}")
# MCP Usage
print(f"\nMCP Tool Calls: {len(result['mcp_calls'])}")
for mcp in result['mcp_calls'][:5]:
print(f" - {mcp['tool']} at {mcp['timestamp']}")
# Grep vs Read patterns
print(f"\nGrep Calls: {len(result['grep_calls'])}")
print(f"Read Calls: {len(result['read_calls'])}")
# Read with limits
reads_with_limit = [r for r in result['read_calls'] if r['limit'] is not None]
if reads_with_limit:
avg_limit = sum(r['limit'] for r in reads_with_limit) / len(reads_with_limit)
print(f"\nReads with limit: {len(reads_with_limit)} ({len(reads_with_limit)/len(result['read_calls'])*100:.1f}%)")
print(f"Average limit: {avg_limit:.0f} lines")
# Common sequences
if result['sequences']:
print(f"\nCommon Tool Sequences:")
seq_counts = defaultdict(int)
for seq in result['sequences']:
pattern = ' -> '.join(seq)
seq_counts[pattern] += 1
for pattern, count in sorted(seq_counts.items(), key=lambda x: x[1], reverse=True)[:5]:
print(f" {pattern}: {count}")
# MCP adoption rate
total_search_tools = (result['tool_usage'].get('Grep', 0) +
result['tool_usage'].get('Glob', 0) +
sum(1 for t in result['tool_usage'] if t.startswith('mcp__')))
if total_search_tools > 0:
mcp_rate = len(result['mcp_calls']) / total_search_tools * 100
print(f"\nMCP Adoption Rate: {mcp_rate:.1f}%")
if __name__ == "__main__":
main()
