Server has 5 tools with complete input schemas and descriptions, but quality is inconsistent. Tool names follow verb_noun convention (get_, generate_, calculate_, sync_). Descriptions are present but generic (avg ~100 chars, below 194-char baseline). Parameters have types and descriptions, but lack enums for constrained inputs (e.g., subject_segment, cognitive_dimension accept free-form strings). Output schemas are JSON-serialized strings rather than structured objects, forcing LLM parsing. Error handling is minimal, most tools return JSON error objects without recovery guidance. No tool annotations (readOnlyHint/destructiveHint) despite clear risk profiles. Two WRITE tools (sync_ai_platform_state, sync_lms_onboarding_state) lack confirmation patterns or dry-run support.

• Add enum constraints to subject_segment ('robotics'|'security'|'seo') and cognitive_dimension ('apply'|'analyze'|'evaluate'|'create') to prevent hallucinated values.
• Document output schemas explicitly. Define the structure of each tool's return object (e.g., lesson_plan has title and objective fields) so LLMs know what to extract.
• Add readOnlyHint and destructiveHint annotations to tool definitions. Mark get_progressive_syllabus and calculate_spaced_repetition as readOnly; mark sync_* tools as destructive.
• Implement a dry-run or confirmation pattern for WRITE tools. Add an optional 'dry_run' parameter to sync_ai_platform_state and sync_lms_onboarding_state, returning the proposed state change without committing.
• Enhance error messages with recovery guidance. Instead of silently clamping grade_1_5 to [0,5], return 'Invalid grade: got 7, must be 0-5. Retrying with grade=5.' to inform the LLM of the correction.