KaggleRun has 8 tools with complete input schemas and descriptions, but quality is uneven. Tool names follow verb_noun convention (execute_python, get_gpu_info, list_files, read_file, save_file, download_file, test_connection). Descriptions are present but brief (avg ~80 chars, below the 194-char baseline). Parameters lack descriptions in several tools (get_gpu_info, get_system_info, test_connection have empty properties). Output schemas are not documented, LLMs cannot predict response structure. Error handling is minimal; no recovery guidance or categorization. The execute_python tool is high-risk (IRREVERSIBLE) but lacks confirmation/dry-run pattern. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk levels.

• Document output schemas for all 8 tools. Include field names, types, and examples. E.g., execute_python returns {output_text: string, errors: string[], execution_status: 'success'|'timeout'|'error'}.
• Add descriptions to all parameters, even empty ones. E.g., get_gpu_info inputSchema.properties should document what fields are returned (not input params, but clarify in description: 'No parameters required. Returns GPU model, memory, driver version.').
• Implement confirmation pattern for execute_python: add optional 'dry_run' parameter or require explicit 'confirm: true' flag. Return execution plan before running irreversible code.
• Add tool annotations to MCP Tool definitions: set readOnlyHint=true for get_*, list_*, read_*, download_*; set destructiveHint=true for execute_python, save_file; set idempotentHint=true for read-only tools.
• Expand descriptions to 100-200 chars with WHEN/WHY context. E.g., 'Get GPU model, memory, and driver version. Call this first to verify GPU availability before execute_python.' Include dependency hints.