SimulateInput defines 18 tools with explicit schemas and basic descriptions. All tools have input schemas with proper JSON Schema structure (type, properties, required, additionalProperties). However, descriptions are uniformly terse (10-30 chars), parameter descriptions are entirely absent, and output schemas are not documented. No error handling guidance, no parameter constraints (enums, ranges), and no indication of which operations are destructive vs. safe. Tool names follow verb_noun convention (start_session, list_windows, click_text) but lack context about side effects. The server is functional but falls short of production-grade quality, descriptions do not guide LLM selection, parameters lack validation hints, and error recovery is opaque.

• Add 50-150 character descriptions to every tool explaining WHAT it does, WHEN to use it (vs. similar tools), and what it returns. E.g., 'find_text: Locate visible text in the window using OCR. Returns coordinates and text snippets. Use when you need to find UI elements by their displayed text.'
• Document every parameter with type, format, constraints, and purpose. E.g., 'session_id (string, required): Unique session identifier returned by start_session. Used to route all subsequent commands to the correct automation context.'
• Define output schemas for all tools. Specify returned fields, types, and whether results are paginated. E.g., find_text returns [{text, x, y, width, height, confidence}]. This enables LLMs to chain tools correctly.
• Add parameter constraints: enums for 'key' (valid key names), ranges for 'threshold' (0.0-1.0), 'duration_ms' (1-10000), 'max_results' (1-100). Use JSON Schema minimum/maximum/enum.
• Implement dry_run for all destructive operations (click, type_text, drag, press_key, hotkey, clear_text). Return what WOULD happen without executing. Add error recovery guidance: 'If click fails, try find_text() first to verify the target exists.'