Single tool 'abliterate' has a well-structured schema with 8 parameters, all typed and described. Description is comprehensive (280+ chars) and explains the action, prerequisites, and output. However, the tool performs an irreversible, high-risk operation (model modification) with no confirmation step, dry-run option, or explicit error recovery guidance. Output schema is not formally documented in the code. Parameter descriptions are good but lack explicit constraint details (e.g., what constitutes a valid HF model ID, what happens if optimization fails). No per-parameter validation rules stated.

• Add a 'dry_run' boolean parameter (default false) to report achievable metrics without writing the model. This enables agents to preview results before committing to a long optimization run.
• Document the return schema explicitly: {baseline_refusals: int, remaining_refusals: int, kl_divergence: float, selected_trial: int, output_path: str, estimated_runtime_seconds: int}.
• Enhance 'model' parameter description: 'Hugging Face model ID (e.g., meta-llama/Llama-2-7b) or absolute local path. Model must be a transformer-based LLM. If not found, tool will raise ModelNotFoundError with available alternatives.'
• Add explicit error handling guidance in docstring: 'Raises: ModelNotFoundError (suggest search_huggingface), OutOfMemoryError (suggest reducing batch_size or n_trials), TimeoutError (retryable after 5 min).'
• Constrain 'n_trials' to range 1 - 500 with description: 'Higher values improve search quality but increase runtime (roughly 2 - 5 min per trial on A100). Default 100 is suitable for most models.'

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry