MCP server for Juhe Finance stock market data providing Shanghai/Shenzhen and U.S. stock information
Two tools with basic schemas and descriptions, but significant gaps in parameter documentation, output schema clarity, and error handling guidance. Tool names follow verb_noun convention (get_*), which is good. However, descriptions are minimal (34-45 chars, below the 50-200 char LLM-optimized baseline), and parameter descriptions lack actionable constraints. No output schema documented. Error handling returns plain text without recovery guidance. The server exposes API keys via environment variables (correct), but tool implementations lack input validation and return unstructured text responses instead of structured JSON.
Get Shanghai/Shenzhen stock or index data
Get U.S. stock market data
Descriptions are too short (34-45 chars) and lack actionable context. LLMs cannot determine when to select these tools vs. alternatives. Baseline for LLM-optimized descriptions is 50-200 chars.
Parameter descriptions lack constraints and format guidance. 'type' enum values ('0', '1', '2', '3') are cryptic, descriptions should explain what each means (e.g., '0: SSE Composite Index, 1: SZSE Component Index'). 'page' parameter has no range or validation hint.
No output schema documented. Tools return plain text strings instead of structured JSON. LLMs cannot parse results reliably or extract fields for downstream tool calls. Baseline: 100% of A+ tools document return types.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 44 | 2026-07-28+ | v2 |
Error handling returns plain error text without recovery guidance. 'Error fetching HS stock data: ...' tells the LLM nothing about whether to retry, ask the user, or try an alternative. Baseline: errors must categorize as retryable/user-fixable/fatal and suggest next steps.
Parameter 'gid' in get-hs-stock-data is optional but the tool logic requires either 'gid' or 'type'. This mutual exclusivity is not documented in parameter descriptions, forcing LLMs to guess or pass both.