Strong schema definitions with Zod validation and consistent tool registration. All 12 tools have descriptions (avg 95 chars) and input schemas with type constraints. Tool names follow verb_noun pattern (list_, get_, start_). Annotations (readOnlyHint/destructiveHint) present for all tools. Key gaps: output schemas not documented in code; parameter descriptions are minimal (avg 40 chars, below 72-char baseline); no error recovery guidance; no pagination metadata (total_count, next_cursor) in list tools despite accepting limit/offset.

• Document output schemas for all tools. E.g., 'Returns: {datasets: [{id: uuid, slug: string, versions: [{number: int, created_at: iso8601}]}, total_count: int}'.
• Enhance parameter descriptions to 50-100 chars with format hints. E.g., 'Organization slug (2-32 lowercase alphanumeric, e.g., acme-corp)' instead of 'Organization slug'.
• Add pagination metadata to list tool responses: total_count, has_more, or next_cursor. Document in tool description: 'Returns up to 50 results; use offset to paginate.'
• Implement error classification in failure() function. Return {code, message, retryable: bool, user_fixable: bool, suggestion: string} so LLM knows next action.
• Add optional dry_run parameter to start_training, start_evaluation, verify_deployment, run_workflow. Return estimated cost/impact without committing resources.