Generate data-based A-share stock analysis reports by code for fundamental, technical, or comprehensive review, clearly separating analysis from investment advice.
提供基于数据的股票分析报告，而非投资建议。
Args:
code: 股票代码，如'sh.600000'
analysis_type: 'fundamental'|'technical'|'comprehensive'
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| analysis_type | No | fundamental |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the output is '基于数据的股票分析报告' (data-based stock analysis reports), which hints at read-only behavior, but doesn't disclose critical traits like authentication needs, rate limits, response format, or whether it's a real-time or cached analysis. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise and structured. It opens with the core purpose, adds a clarifying boundary (not investment advice), and lists parameters with examples. No wasted sentences, though minor formatting issues (extra whitespace) prevent a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (analysis generation), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the analysis report contains, its format, potential limitations, or how it differs from sibling data tools. For a tool that presumably synthesizes data into insights, more context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful parameter semantics beyond the schema. The schema has 0% description coverage (no parameter descriptions), but the description explains: 'code: 股票代码，如'sh.600000'' (stock code, e.g., 'sh.600000') and 'analysis_type: 'fundamental'|'technical'|'comprehensive''. This clarifies the code format and analysis_type options, compensating well for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '提供基于数据的股票分析报告' (provide data-based stock analysis reports). It specifies the resource (stock analysis reports) and distinguishes it from investment advice. However, it doesn't explicitly differentiate from sibling tools like 'get_market_analysis_timeframe' or 'get_stock_basic_info', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance: it clarifies the tool provides analysis reports rather than investment advice, but offers no explicit when-to-use guidance relative to alternatives. With many sibling tools for stock data (e.g., 'get_historical_k_data', 'get_fina_indicator'), there's no indication of when this analysis tool is preferred over raw data tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.