Three tools with minimal documentation. All have descriptions (10-20 chars), but lack parameter descriptions entirely. No input schemas visible in source, only inferred from function signatures. No output schemas documented. Tools return unstructured strings (raw yfinance data dumps) rather than structured objects. No error handling, validation, or recovery guidance. Naming is verb-noun but generic ('stock_price', 'stock_info', 'income_statement'). No distinction between similar tools (stock_info vs income_statement both return company data). Descriptions lack WHEN/WHY context and example values are embedded in docstrings (anti-pattern).

• Add explicit JSON Schema input definitions to each tool with type='string', minLength=1, maxLength=10, pattern='^[A-Z0-9]{1,5}$' for stock_ticker. Document the constraint in the parameter description: 'Stock ticker symbol (1-5 uppercase alphanumeric characters, e.g., AAPL, MSFT).'
• Document output schemas. E.g., stock_price should return {"ticker": string, "prices": [{"date": ISO8601, "close": number}], "period": string} instead of raw string dumps.
• Expand tool descriptions to 50-150 characters with WHEN/WHY context. E.g., 'Get the current stock price for a ticker. Use this to check recent price movements. Returns last month of daily closing prices.'
• Add try-catch blocks with actionable error messages. E.g., 'Invalid ticker: INVALID. Valid tickers are 1-5 uppercase letters (e.g., AAPL, MSFT). Try search_tickers() to find the correct symbol.' instead of raw yfinance exceptions.
• Distinguish stock_info and income_statement in naming and descriptions. Consider renaming to get_company_info and get_quarterly_income_statement to clarify intent and prevent LLM confusion.