Optimize memory and algorithms

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:

  // 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:

  // 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.