Cortex has 7 tools with clear naming (verb-first: get_, query_, explore_, find_, write_, list_) and reasonable descriptions (avg ~150 chars). However, critical gaps undermine production readiness: (1) Input schemas are present but sparse, most parameters lack type constraints and validation guidance; (2) Output schemas are entirely undocumented, LLMs cannot predict response structure for chaining; (3) Error handling is minimal, no recovery guidance or actionable error messages; (4) write_system_design_node and write_agent_instructions lack confirmation/dry-run patterns for destructive operations; (5) Parameter descriptions are generic ('e.g. @File:auth.py:f89a2') rather than prescriptive constraints. The tool set is coherent (graph exploration + design documentation), but implementation quality is below production baseline.

• Document output schema for every tool. Specify field names, types, and structure. E.g. get_graph_timeline returns [{label, slot, timestamp, message}]. This enables LLM chaining.
• Replace example values in parameter descriptions with formal constraints. E.g. 'node_id: string matching pattern @<Type>:<Name>:<Hash>' or 'depth: integer, 1-2 (default 1)'.
• Add dry-run or confirmation step to write_system_design_node and write_agent_instructions. Return a preview of changes and require explicit confirmation before committing.
• Implement actionable error messages. E.g. 'Node @File:auth.py:f89a2 not found. Available nodes: @File:auth.py:f89a1, @File:auth.py:f89a3' instead of generic 404.
• Document write_agent_instructions input schema. Currently empty ({}). Clarify what parameters control the output (e.g. agent_role, instructions_format, output_path).