composite_stock_diagnosticDiagnose any stock with a single call merging technical indicators, fundamentals, and news sentiment into one comprehensive view.
复合技能：一键获取技术面、基本面和消息面的综合诊断数据
| Name | Required | Description | Default |
|---|---|---|---|
| market | No | 市场: sh=沪市, sz=深市, bj=北交所, hk=港股, us=美股 | sh |
| symbol | Yes | 股票代码 |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present at all, so the description carries the full burden. It implies a read-only data retrieval with '获取' but does not disclose any side effects, aggregation behavior, latency, rate limits, or output characteristics. For a composite tool that likely pulls from multiple sources, this lack of behavioral transparency is a significant gap.
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 a single concise sentence with no filler, front-loading the 'composite skill' nature and then listing the three diagnostic dimensions. It is appropriately sized and easy to parse, though it could carry more useful information without becoming verbose.
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?
With no output schema and no annotations, the description must explain what the tool returns and how to interpret it. It names three aspects (technical, fundamental, news) but does not specify the output structure, whether it returns a score/rating, or how the result relates to the input parameters. This is incomplete for an agent deciding whether this tool is the right fit.
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?
Schema description coverage is 100%: both 'symbol' and 'market' have descriptions, so the schema already defines the parameters. The description adds no parameter-specific semantics, only mentioning the three diagnostic dimensions at a high level, which aligns with the baseline score of 3.
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 states a clear verb ('获取' / obtain) and a specific resource: comprehensive diagnostic data covering technical, fundamental, and news aspects. This conveys the tool's purpose and conceptually distinguishes it from single-faceted siblings, though it does not explicitly say the tool is for stocks, relying on the tool name and title.
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?
No usage guidance is provided. The description does not indicate when to prefer this tool over alternatives such as individual_info, stock_quote, or the crypto/pm composite diagnostics, nor does it state any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.