All 4 tools have identical, minimal definitions. Each accepts a single 'goal' string parameter with no type validation, enum constraints, or format guidance. Descriptions are generic delegation statements (18-24 chars) that fail to explain WHEN to use each tool, what distinguishes them, or what output format to expect. No output schemas documented. Tools appear to be agent-to-agent delegation wrappers, but the interface treats them as black boxes, LLMs cannot reason about which specialist to invoke or how to structure the goal. No error handling guidance, no pagination, no idempotency markers. Schema definitions are minimal (only 'goal' with description, no type constraints). This is a delegation pattern that should be transparent to the LLM, but the tool definitions provide no transparency.

• Expand each tool description to 100-150 chars explaining the specialist's domain, when to delegate to them, and what kind of goals they handle. E.g., 'Delegate inventory-related questions (stock levels, warehouse locations, availability) to the inventory specialist. Returns a plain-text response with inventory details or recommendations.'
• Add explicit type: 'string' to the 'goal' parameter schema. Consider adding minLength, maxLength, or pattern constraints to guide LLMs on goal format (e.g., minLength: 10, maxLength: 500).
• Document the output schema: specify that the tool returns a string (plain text), and note any expected structure (e.g., 'Returns a plain-text response; may include structured data like 'Stock: 150 units, Location: Warehouse A').
• Rename tools to use clearer verb_noun patterns: 'invoke_inventory_specialist', 'query_supplier_specialist', 'delegate_logistics_query', 'get_pricing_specialist_response'. This makes the action explicit.
• Add error handling guidance in descriptions: 'If the specialist is unavailable, the tool returns an error message. Retry after a brief delay, or escalate to a human operator if the specialist remains unreachable.'