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• Expand tool descriptions from 15 chars to at least 100-150 chars. Example: 'last_concept' should explain: What data structure is queried? What does the response contain? When should the LLM call this vs. a search/list tool? Is it read-only and safe to retry?
• Add parameter descriptions to 'add_concept'. For 'concept' param: specify valid values (enum of expense categories: Groceries, Rent, Utilities, Dining Out, Transportation, or free-form string?). For 'expense' param: add min/max bounds (e.g., 'expense amount in USD, range 0.01 - 1000000').
• Document the output schema explicitly. Define return value structure: Is it always a string? Can it return JSON with fields like {status: 'success', concept: '...', expense: ...}? This helps LLMs chain downstream calls.
• Add input validation and error handling. Reject negative expense values with message 'Invalid expense: must be >= 0.01 USD'. Validate concept string length. Return structured error responses: {error: 'Invalid input', detail: '...', suggestion: 'Try...'}.
• Rename 'add_concept' to 'add_expense' or 'add_bank_transaction' to match the semantic intent and the underlying table name (bank_data, not concept_bank).
• Add permission gates and audit logging. Log every tool invocation with timestamp, caller ID, parameters, and result. Gate destructive operations (add_concept is a write operation) behind permission checks.
• Remove or clearly mark this as a PoC/educational server, not for production. If intended as a PoC, document the security issues being demonstrated and never run this against real financial data.