Five tools with minimal descriptions (10-50 chars) and no visible input schemas in the provided code. Tool names follow verb_noun convention (route_query, get_constraints, search_knowledge, read_chapter, list_skills), which is good. However, descriptions are generic and lack context for LLM selection. No parameter descriptions visible beyond the schema snippet shown. No output schemas documented. No error handling guidance. The code excerpt cuts off mid-class, so full schema validation is incomplete, but what is visible shows significant gaps against production baselines (94% of A+ tools have docstrings; 100% have param descriptions).

• Expand each tool description to 100 - 200 characters. Include: (1) what the tool does, (2) when to call it instead of similar tools, (3) what it returns. Example: 'Search the e-commerce knowledge index by entity name or keyword. Returns matching chapters with IDs suitable for read_chapter(). Use this to discover relevant guidance before reading full chapters.'
• Document output schemas for all tools. Specify field names, types, and which fields are required for chaining (e.g., search_knowledge must return chapter_id for read_chapter). Use JSON Schema format in tool metadata.
• Add pagination to search_knowledge and get_constraints. Accept offset/limit or cursor parameters. Return total_count and next_cursor so agents can iterate large result sets without context explosion.
• Add parameter descriptions for 'limit' (e.g., 'Maximum results to return (1 - 100, default 20)') and 'platform' (e.g., 'Platform identifier: amazon, shopify, ebay, etc.'). Enums for platform values prevent hallucinated platform names.
• Add error recovery hints to tool descriptions. E.g., read_chapter: 'If chapter_id is invalid, call search_knowledge() to find the correct ID.' This guides LLM self-correction on failures.