47 tools with severe definition gaps. Tool names follow verb_noun pattern (good), but descriptions are inconsistent, many lack critical details about parameters, return values, and error handling. Input schemas visible in test file show only 'account' parameter with minimal type info; most tools lack complete parameter documentation. No output schemas documented. Error handling guidance absent. Descriptions range 20-200 chars but many omit prerequisites, side effects, and recovery paths. This server exposes raw API complexity rather than abstracting it for agent reasoning.

• Expand all tool descriptions to 100-200 chars. Include: what the tool does, when to use it (vs similar tools), what parameters it requires, and what it returns. Example: 'Search Gmail messages by query (e.g., from:[email protected] subject:urgent). Returns up to 20 matching message IDs and snippets. Requires account label or email. Call gws.mail.read_message to fetch full content.'
• Document complete input schemas for all 47 tools. For each tool, list all required and optional parameters with type, description, constraints, and examples. E.g., gws.mail.search needs: query (string, required), maxResults (integer, 1-100, default 20), pageToken (string, optional).
• Add output schemas showing field names, types, and descriptions. E.g., gws.accounts.list returns [{label: string, email: string, isDefault: boolean}]. This enables LLMs to chain tools and extract the right data.
• Add error handling guidance to destructive tools (delete, remove, send, clear). E.g., 'If account not found, call gws.accounts.list to see available accounts. If permission denied, verify the account has the required Google Workspace scope.'
• Standardize parameter naming: use account_label and account_email as separate optional params, or document that 'account' accepts both formats and will resolve ambiguity. Add validation examples: 'account must be a label (e.g., "work") or email (e.g., "[email protected]").' This prevents LLM confusion and reduces failed calls.