The server provides a single well-defined tool with a clear verb-based name and comprehensive input schema. The tool description is detailed and domain-specific, explaining what WorldQuant Alpha factors are calculated and when to use them. The input schema includes all required parameters with proper type definitions and descriptions. However, the output schema is not formally documented in the tool registration, it is inferred from the AlphaResult interface in the implementation code. This gap prevents a higher score. The tool is READ_ONLY and domain-focused, which is appropriate for a quantitative finance tool. No error handling guidance is visible in the tool definition itself.

• Add a 'returns' section to the tool description documenting the output schema structure, including field names, types, and meanings: 'Returns an object with dates (array of YYYYMMDD strings), values (array of numbers), currentValue (last value), mean (average), std (standard deviation), and percentile (0-1 rank of current value)'.
• Specify acceptable date ranges and stock code formats more precisely. Add validation examples: 'Stock codes must be in Tushare format (e.g., 000001.SZ for Shenzhen or 600000.SH for Shanghai). Date range must not exceed 10 years.'
• Document error scenarios and recovery paths: 'If stock_code is invalid, returns error listing available stock codes. If date range has no trading data, returns empty arrays with NaN statistics. Call with a shorter date range to retry.'
• Add constraints to the schema: use minLength/maxLength for date strings (8 chars), add pattern '^\d{8}$' for YYYYMMDD format, and add minimum/maximum items for the factors array.
• Explain the alpha factor selection guidance: 'Choose factors based on your hypothesis, Alpha3 captures price-volume correlation; Alpha50/55 focus on VWAP relationships. Combining 3-5 factors reduces overfitting risk.'