Mixed quality across 8 tools. Stock analysis tools (analyze-stock, relative-strength, volume-profile, detect-patterns, position-size, suggest-stops) have clear verb-noun naming and reasonable descriptions (50-100 chars), but lack documented output schemas and parameter descriptions are minimal. Weather tools (get_alerts, get_forecast) have better parameter descriptions but inconsistent naming conventions (underscore vs hyphen). No tool includes error handling guidance, recovery paths, or output schema documentation. Parameters lack type constraints (enums, ranges, patterns). Overall: definitions are present but sparse, lacking the depth needed for reliable LLM tool selection and composition.

• Document output schemas for all tools. Example: 'Returns {symbol: string, trend: "bullish"|"bearish"|"neutral", support_level: number, resistance_level: number, confidence: 0-100}'.
• Expand parameter descriptions to 50-150 chars each. For 'symbol', add: 'Stock ticker symbol (e.g., NVDA, AAPL). Must be a valid US equity symbol. Case-insensitive.'
• Add error handling guidance to tool descriptions. Example: 'Returns error if symbol not found or market data unavailable. Retry after 30 seconds if rate-limited.'
• Standardize naming: choose either all hyphens (analyze-stock, get-alerts) or all underscores (analyze_stock, get_alerts). Recommend underscores per Python convention.
• Add parameter constraints: 'lookback_days' should specify min=1, max=365; 'state' should be an enum of valid US state codes (CA, NY, TX, etc.).