MCP Standards

# bottleneck detect Analyze performance bottlenecks in swarm operations and suggest optimizations. ## Usage ```bash npx claude-flow bottleneck detect [options] ``` ## Options - `--swarm-id, -s <id>` - Analyze specific swarm (default: current) - `--time-range, -t <range>` - Analysis period: 1h, 24h, 7d, all (default: 1h) - `--threshold <percent>` - Bottleneck threshold percentage (default: 20) - `--export, -e <file>` - Export analysis to file - `--fix` - Apply automatic optimizations ## Examples ### Basic bottleneck detection ```bash npx claude-flow bottleneck detect ``` ### Analyze specific swarm ```bash npx claude-flow bottleneck detect --swarm-id swarm-123 ``` ### Last 24 hours with export ```bash npx claude-flow bottleneck detect -t 24h -e bottlenecks.json ``` ### Auto-fix detected issues ```bash npx claude-flow bottleneck detect --fix --threshold 15 ``` ## Metrics Analyzed ### Communication Bottlenecks - Message queue delays - Agent response times - Coordination overhead - Memory access patterns ### Processing Bottlenecks - Task completion times - Agent utilization rates - Parallel execution efficiency - Resource contention ### Memory Bottlenecks - Cache hit rates - Memory access patterns - Storage I/O performance - Neural pattern loading ### Network Bottlenecks - API call latency - MCP communication delays - External service timeouts - Concurrent request limits ## Output Format ``` 🔍 Bottleneck Analysis Report ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 Summary ├── Time Range: Last 1 hour ├── Agents Analyzed: 6 ├── Tasks Processed: 42 └── Critical Issues: 2 🚨 Critical Bottlenecks 1. Agent Communication (35% impact) └── coordinator → coder-1 messages delayed by 2.3s avg 2. Memory Access (28% impact) └── Neural pattern loading taking 1.8s per access ⚠️ Warning Bottlenecks 1. Task Queue (18% impact) └── 5 tasks waiting > 10s for assignment 💡 Recommendations 1. Switch to hierarchical topology (est. 40% improvement) 2. Enable memory caching (est. 25% improvement) 3. Increase agent concurrency to 8 (est. 20% improvement) ✅ Quick Fixes Available Run with --fix to apply: - Enable smart caching - Optimize message routing - Adjust agent priorities ``` ## Automatic Fixes When using `--fix`, the following optimizations may be applied: 1. **Topology Optimization** - Switch to more efficient topology - Adjust communication patterns - Reduce coordination overhead 2. **Caching Enhancement** - Enable memory caching - Optimize cache strategies - Preload common patterns 3. **Concurrency Tuning** - Adjust agent counts - Optimize parallel execution - Balance workload distribution 4. **Priority Adjustment** - Reorder task queues - Prioritize critical paths - Reduce wait times ## Performance Impact Typical improvements after bottleneck resolution: - **Communication**: 30-50% faster message delivery - **Processing**: 20-40% reduced task completion time - **Memory**: 40-60% fewer cache misses - **Overall**: 25-45% performance improvement ## Integration with Claude Code ```javascript // Check for bottlenecks in Claude Code mcp__claude-flow__bottleneck_detect { timeRange: "1h", threshold: 20, autoFix: false } ``` ## See Also - `performance report` - Detailed performance analysis - `token usage` - Token optimization analysis - `swarm monitor` - Real-time monitoring - `cache manage` - Cache optimization