SAVILE exposes 7 tools with basic schemas and descriptions, but critical gaps prevent production use. Tool naming is inconsistent (agent_* is dynamic and vague; board_* lacks verb clarity). Descriptions exist but are generic and lack actionable context for LLM selection. Parameters have types but many lack descriptions (e.g., task_id in board_update_task has no description). Output schemas are completely undocumented, LLMs cannot plan downstream calls. Error handling is absent. The dynamic agent_* tools are inferred from filesystem state, not explicitly registered, capping their individual scores at 50. Overall, this is a D-grade server with significant structural issues.

• Document output schemas for all tools. Example: list_logic_modules should return {modules: [{category: string, name: string, description: string}]}. board_get_tasks should return {tasks: [{id: string, title: string, status: string, assignee_persona: string}], total: number}.
• Rename board_* tools to verb_noun format: get_board_tasks, add_board_task, update_board_task. This clarifies intent and follows production baselines (90% of A+ tools start with action verbs).
• Add enum constraint to board_update_task status parameter: {type: string, enum: [todo, in_progress, review, done], description: 'Task status. Must be one of: todo, in_progress, review, done.'}
• Add descriptions to all undescribed parameters: task_id in board_update_task ('The unique ID of the task to update'), notes in board_update_task ('Optional findings, commit hashes, or handoff notes to append').
• Implement error handling with recovery guidance. Example: if read_logic_module fails, return 'File not found: realist.md. Available personas: [list]. Call list_logic_modules() to discover valid modules.' This enables LLM self-correction.