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zlinzzzz

FinData MCP Server

by zlinzzzz

bak_basic

Retrieve fundamental data for a stock over a specified date range. Includes financial metrics such as PE, EPS, and revenue growth.

Instructions

Name:
    股票基本面信息。

Description:
    获取某支股票在指定时间范围内的基本面数据,数据从2016年开始。

Args:
    | 名称       | 类型  | 必填 | 描述                                   |
    |------------|-------|------|----------------------------------------|
    | ts_code    | str   | 是    | 股票代码,每次只能查询一支股票    |
    | start_date | str   | 是    | 开始日期(YYYYMMDD) |
    | end_date | str   | 是    | 开始日期(YYYYMMDD) |
    | fields     | list  | 否    | 从Fields中选取需要查询的字段  |

Fields:
    - trade_date:交易日期
    - ts_code:TS股票代码
    - name:股票名称
    - industry:行业
    - area:地域
    - pe:市盈率(动)
    - float_share:流通股本(亿)
    - total_share:总股本(亿)
    - total_assets:总资产(亿)
    - liquid_assets:流动资产(亿)
    - fixed_assets:固定资产(亿)
    - reserved:公积金
    - reserved_pershare:每股公积金
    - eps:每股收益
    - bvps:每股净资产
    - pb:市净率
    - list_date:上市日期
    - undp:未分配利润
    - per_undp:每股未分配利润
    - rev_yoy:收入同比(%)
    - profit_yoy:利润同比(%)
    - gpr:毛利率(%)
    - npr:净利润率(%)
    - holder_num:股东人数

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ts_codeYes
start_dateYes
end_dateYes
fieldsNo
Behavior2/5

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

No annotations exist, so the description carries full burden. It notes data starts from 2016 but does not disclose read-only nature, rate limits, pagination, or other behavioral traits. For a query tool, this lack of transparency is a gap.

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?

The description is well-structured with separate sections for purpose, parameters, and fields. It is reasonably concise given the amount of information, though the end_date parameter has a typo ('开始日期' instead of '结束日期').

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

Completeness3/5

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

Given no output schema, the Fields list implicitly indicates return values, but the description does not explicitly explain the output structure or any sorting/ordering. For a data retrieval tool, this lacks completeness compared to similar tools with richer descriptions.

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

Parameters4/5

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

The input schema provides no descriptions (0% coverage), but the description adds a detailed table explaining each parameter's purpose, format, and constraints (e.g., ts_code single stock, date format YYYYMMDD). The fields parameter is linked to the Fields list, providing clear selection guidance.

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

Purpose4/5

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

The description clearly states it retrieves fundamental data for a specific stock in a date range. However, it does not explicitly differentiate from sibling tools like stock_basic or daily, leaving some ambiguity about when to choose this tool.

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

Usage Guidelines3/5

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

The description mentions the constraint of querying only one stock per call, but does not provide guidance on when not to use this tool or suggest alternatives. Usage context is implied rather than explicit.

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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