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Wudao A-Share Stock Data MCP

by io.github.jcdreamjc
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kline

Read-onlyIdempotent

获取个股日K线数据，盘中实时融合最新行情，支持自定义天数范围；adjust=qfq 可返回前复权价格，默认 none 不复权 何时使用: 日K线原子工具：返回指定股票或批量股票的 OHLC、涨跌幅、成交额 amount（元）、换手率等历史序列；适合计算区间涨幅、阶段高低点、价格分位、放量倍数等窗口型条件。 【链接】rows/items/嵌套股票对象含 code/name；列股必须用 [name(code)](https://stock.quicktiny.cn/quote/<code>) markdown 链接。

Input Schema

NameRequiredDescriptionDefault
codeNo单股查询：股票/指数代码或名称，例如 600519、600519.SH、贵州茅台。返回按日期升序的日K序列，包含 open/high/low/close、涨跌幅、成交额 amount（元）、换手率等字段。
daysNoK 线天数，默认 15。窗口型条件要传足够天数，例如计算 120 日区间位置时传 121+。
codesNo批量查询：代码或名称数组，例如 ["600519","000001","平安银行"]。上限 20 只。多股同时分析时优先使用 codes 而不是逐股调用；适合给事件池候选批量补历史价格、成交额、区间高低点。
adjustNo复权方式：none=不复权（默认）；qfq=前复权。当前不支持 hfq 后复权。qfq 使用本地昨收参考价因子并以查询截止日定基，可覆盖现金分红、送转、配股等参考价变化且不会引入截止日之后的公司行为；返回时仍应检查 qualityWarnings。none
formatNocontent[0].text：默认1行 headline；markdown=完整 markdown 表格；json=完整 JSON。
endDateNo可选。历史截止日期，支持 YYYY-MM-DD 或 YYYYMMDD，返回该日期（含当天）及之前的 K 线；事件日分析应传事件发生日期，不传则取最新交易日/盘中实时合并数据。
maxRowsNo每只股票 rows 最多返回多少根K线。不传默认返回最近60根；因子/窗口计算需要100+根时可传100/120，上限150。days 也必须同步传足；codes 较多时仍可能触发全局响应预算截断，建议分批。
embedStockNo是否回完整 stock 档案（行业/地区/交易所等）。不传=false。
detailLevelNo档位：默认 standard（Agent 可计算标准字段）；summary=压缩摘要；raw=全字段+原始数据。

Output Schema

NameRequiredDescriptionDefault
dataNo
metaNo
successNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / detailLevel / description
      Previous value: -"输出详细度（不传=standard，返回 Agent 可计算标准字段）。summary=压缩摘要；raw=全字段+原始数据。"New value: +"档位：默认 standard（Agent 可计算标准字段）；summary=压缩摘要；raw=全字段+原始数据。"
    • changedInput schema / properties / format / description
      Previous value: -"content[0].text 形态。通常不传=1 行 headline；auction_market_scan / auction_theme_strength 在 standard/raw 档默认直接返回 JSON 明细。markdown=完整 markdown 表格；json=完整 JSON。"New value: +"content[0].text：默认1行 headline；markdown=完整 markdown 表格；json=完整 JSON。"
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "data": {},
+    "meta": {},
+    "success": {}
+  },
+  "type": "object"
+}
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive/openWorld, so safety is covered. The description adds real behavioral nuance beyond them: qfq is computed from a local 昨收参考价 factor定基于截止日, covers dividends/配股 without pulling in post-endDate corporate actions, and warns the agent to inspect qualityWarnings and watch for global response-budget truncation on large batches.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core verb+resource before the 何时使用 block, and every section carries actionable content. The trailing 【链接】 paragraph is a slightly jarring tack-on and the whole text is dense, but nothing is padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present the description needn't explain return shape, and it correctly focuses on batching constraints, adjustment caveats and quality warnings. It is nearly complete for a 9-param tool; only the exact interplay of days vs maxRows (must be passed together) is left mostly to the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3; the description's adjust/codes/days remarks largely restate what the schema already documents. The one addition with real value is the output-formatting requirement that listed stocks use `[name(code)](...)` markdown links, which hints at handling rather than parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opening line states a specific verb+resource: 获取个股日K线数据, with the scope modifiers that matter (盘中实时融合, 自定义天数, adjust). It also positions itself as the 日K线原子工具, implicitly separating it from minute_data and admission/auction siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit '何时使用' with concrete target workloads (区间涨幅、阶段高低点、价格分位、放量倍数等窗口型条件) and batch guidance (多股分析优先用 codes 而不是逐股调用). No explicit when-not or named sibling alternative, so it stops short of 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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