Norviq's chatbot example exposes 6 tools with basic naming and minimal descriptions. All tools have input schemas with type and description fields, meeting the baseline. However, descriptions are extremely brief (10-50 chars), falling well below the 194-char production average and the 50-200 char LLM-optimized range. No output schemas are documented. Critical security issue: execute_sql and delete_record are exposed as direct agent tools without confirmation patterns or permission gates, violating pattern:permission-gate and pattern:confirmation-request. The tool set lacks error handling guidance, pagination support, and composition patterns. Naming is verb-first (good), but descriptions do not explain WHEN to use each tool or what happens on failure.

• Expand all tool descriptions to 100-200 characters. Include WHAT the tool does, WHEN to use it (vs similar tools), and what it returns. Example: 'search_kb: Search the knowledge base for product and policy information. Returns matching articles with titles, summaries, and relevance scores. Use this before get_customer to find relevant policies.'
• Document output schemas for all tools. Specify field names, types, and whether results are paginated. Example: search_kb returns {results: [{id, title, summary, relevance_score}], total_count, next_cursor}.
• Add confirmation pattern to delete_record and execute_sql. Require a separate confirm_delete(record_id, confirmation_token) or confirm_query(query_hash) step before execution. This prevents accidental data loss.
• Add permission gate checks. Document required scopes (e.g., 'Requires write:database scope') and validate caller permissions before executing write/delete operations.
• Add error handling guidance to all tools. Document common failure modes and recovery steps. Example: 'If search_kb returns no results, try a broader query or use get_customer to find related information.'