Focus on efficient memory usage and algorithm selection while preserving functionality. Apply these optimization techniques: **Memory optimization:** - Use conservative default values for memory-intensive constants (prefer ~5k over 50k for buffers) - Pre-allocate collections when the final size is known: `Vec::with_capacity(known_size)` - Avoid unnecessary intermediate collections by using direct operations **Algorithm efficiency:** - Use direct slicing instead of collecting intermediate vectors: ```rust // Instead of: lines.iter().rev().take(100).rev().copied().collect() let start = lines.len().saturating_sub(100); let result = lines[start..].join("\n"); ``` - Prefer efficient data structure operations like HashMap membership tests over string prefix matching: ```rust // Instead of: tool_name.starts_with(PREFIX) // Use: self.tool_map.contains_key(tool_name) ``` Remember that correctness takes priority over performance optimizations. Avoid truncating or modifying data in ways that could break downstream functionality, even if it improves performance metrics.