Server has 10 tools with basic schemas and descriptions, but significant gaps prevent higher scores. Naming is verb-noun compliant (domains_list, devices_action_mobile). Descriptions exist but are terse (10-50 chars, below the 50-200 char baseline for LLM optimization). Input schemas are present but sparse: most parameters lack descriptions. Output schemas are not documented. Error handling is minimal, no recovery guidance or actionable error messages. The server registers tools via a register() function with inputSchema objects, but parameter-level descriptions are almost entirely absent.

• Add descriptions to every parameter. For 'query' in devices_list_mobile, write: 'Search query (e.g. "model:Pixel"). Supports partial matches. Optional.' For 'maxResults', write: 'Number of results to return (1-100, default 50). Larger values may slow response.'
• Document output schemas for all tools. E.g., domains_list should state: 'Returns array of {domainName: string, isPrimary: boolean, verified: boolean}.' This enables LLMs to extract and chain data correctly.
• Expand tool descriptions to 50-200 chars. E.g., 'Get the Workspace customer (org) info.' → 'Retrieve organization-level metadata (name, domain, language, alternate email). Call this first to verify org identity before listing users or devices.'
• Add error recovery guidance. E.g., devices_action_mobile should document: 'If device not found, call devices_list_mobile with a query to locate it. If action fails due to permissions, contact your admin.'
• Implement explicit confirmation for destructive operations. Instead of optional 'confirm' flag, split into two tools: devices_action_mobile_preview (dry-run) and devices_action_mobile_execute (requires confirmation from prior preview). Or return a confirmation token that must be passed back.