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Both tools have minimal descriptions (Chinese text, ~80-100 chars each) that lack LLM-optimized structure. Descriptions state WHAT but not WHEN to use or dependencies. Input schemas are present with basic type info but lack constraints (enums, ranges, patterns). No output schemas documented, get_stock_data returns a dict but structure is undefined. No error handling guidance. Tool names are verb-noun (good), but descriptions are too terse and in Chinese, limiting LLM utility. No parameter validation rules stated.
根据上市的完整股票名称获取股票代码 参数: stock_name: str, 股票名称 返回: stock_code: str, 股票代码
根据股票代码获取实时行情数据 参数: stock_code: str, 股票代码 返回: 包含TS_CODE、PRE_CLOSE、PRICE、HIGH、LOW和VOLUME的字典
Descriptions are too short (80-100 chars) and lack LLM-optimized structure. Missing WHEN to use, dependencies, and output field documentation.
No output schema documented. get_stock_data returns 'dict with TS_CODE, PRE_CLOSE, PRICE, HIGH, LOW, VOLUME' but structure is undefined in schema. LLM cannot plan downstream calls.
Input parameters lack constraints. stock_name and stock_code accept free-form strings with no validation rules, format hints, or error recovery guidance.
No error handling guidance. Code raises generic 'Exception' with minimal message. LLM cannot determine if error is retryable, user-fixable, or fatal.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | <=2025-11-25 | v2 |
Descriptions are in Chinese, limiting accessibility and LLM comprehension in English-first environments. English descriptions are standard for production tools.