query_stock_quote
查询个股实时行情,支持股票代码(如 sh600519、600519)或名称(如 贵州茅台)。返回现价、涨跌额、涨跌幅、今开、昨收、最高、最低、成交量等数据。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| stock | Yes |
查询个股实时行情,支持股票代码(如 sh600519、600519)或名称(如 贵州茅台)。返回现价、涨跌额、涨跌幅、今开、昨收、最高、最低、成交量等数据。
| Name | Required | Description | Default |
|---|---|---|---|
| stock | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does so by listing the returned fields (现价, 涨跌额, 涨跌幅, 今开, 昨收, 最高, 最低, 成交量) and specifying input formats. It does not mention delay, data source, or error handling, but for a simple read-oriented query tool this is reasonably transparent.
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, well-structured sentence: it first states the action, then the supported input formats, then the output fields. Every clause adds value and no redundant or filler words are present.
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?
For a tool with one parameter and no output schema, the description provides complete context: what it does, how to specify the parameter, and what data it returns. It also differentiates from the sibling index tool through the term '个股'. No critical information is missing for correct invocation.
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 0% and the only parameter, stock, has no schema-level description. The tool description fully compensates by stating that it can accept stock codes with or without a market prefix (sh600519, 600519) as well as Chinese stock names (贵州茅台). This gives the agent all the semantic information needed to populate the parameter correctly.
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 uses a specific verb (查询, query) and resource (个股实时行情, real-time quotes for individual stocks), clearly distinguishing it from the sibling tool query_index, which focuses on indices. It also provides concrete input examples (sh600519, 600519, 贵州茅台), making the tool's purpose unmistakable.
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 clearly implies when to use the tool: for individual stock quotes, as opposed to index data. However, it does not explicitly mention query_index or state 'use this for stocks, use that for indices,' so the guidance is contextual rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The two tools are clearly separated by target asset: one covers broad market indices and the other covers individual stock quotes. An agent can easily choose the correct tool based on whether it needs an index or a single security.
Both tool names follow the same verb_noun snake_case pattern: query_index and query_stock_quote. The naming convention is predictable and internally consistent.
With only two tools, the server feels thin for a stock-market MCP, though each tool serves a distinct and useful purpose. Additional query types like historical data or market lists would make the tool count feel more complete.
For a realtime-quote-focused server, the major surfaces are covered: major indices and individual stocks with essential quote fields. Minor gaps exist, such as historical/candlestick data or batch quote queries, but basic market lookup tasks can be completed without dead ends.