Strong tool definitions with clear naming, good descriptions, and complete input schemas. All four tools follow verb-first naming convention (generate_, iterate_, list_, get_). Descriptions range from 120 - 180 chars, within the production baseline (p10=34, p90=392). All parameters have type definitions and descriptions. However, output schemas are not documented, no specification of what fields get_screen or list_screens return, which limits LLM planning. Tool compositions are sound: generate_screen → iterate_screen → get_screen forms a logical chain. No tool annotations (readOnlyHint/destructiveHint) present, which is a missed signal for agent reasoning.

• Add output schema documentation to all four tools. Specify what fields generate_screen returns (e.g., screen_id, png_url, html_source, design_system_used, created_at). Document list_screens response structure (screens: [], total_count, next_cursor). Document get_screen fields (screen_id, name, project, html, png_url, created_at, iterations).
• Annotate tools with destructiveHint and readOnlyHint. Mark generate_screen and iterate_screen with 'destructiveHint: true' (they commit designs to storage). Mark list_screens and get_screen with readOnlyHint: true.
• Add pagination to list_screens: accept 'limit' (1 - 100, default 20) and 'offset' (default 0). Return response with { screens: [], total_count: N, has_more: bool }.
• Enhance error handling to return structured error responses: { error: string, code: 'invalid_input|not_found|service_error|timeout', suggestion: string }. E.g., 'Screen not found (code: not_found). Call list_screens() to see available screens.'
• Constrain the design_system parameter: either accept an enum of predefined systems ('apple-hig', 'material-design', 'custom') or provide examples and format guidelines in the description.
• Consider adding confirmation support for generate_screen. Large generation operations benefit from a dry-run or confirmation step to prevent accidental asset creation.