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kevynf

AKBridge MCP Server

by kevynf

stock_individual_notice_report

Read-onlyIdempotent

Retrieve individual stock announcements by stock code, report type, and date range from Eastmoney data center.

Instructions

东方财富网-数据中心-公告大全-个股 https://data.eastmoney.com/notices/stock/300237.html :param security: 股票代码 :type security: str :param symbol: 报告类型;choice of {"全部", "重大事项", "财务报告", "融资公告", "风险提示", "资产重组", "信息变更", "持股变动"} :type symbol: str :param begin_date: 开始日期 :type begin_date: str :param end_date: 结束日期 :type end_date: str :return: 个股公告 :rtype: pandas.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNo全部
end_dateNo
securityYes
begin_dateNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3/5.0
Behavior2/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds only the data source and the fact that the result is a pandas.DataFrame; it discloses nothing about pagination, rate limits, date-range defaults, or which columns come back.

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

Conciseness3/5

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

The docstring is compact and front-loads the source and URL, but the ':type' lines merely restate the JSON schema types and add no information. Every remaining sentence does carry some value, so it is acceptable but not tight.

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?

With no output schema, the description should describe the returned columns or at least the date format, but it only says 'pandas.DataFrame'. For a 4-parameter data-retrieval tool whose annotations already cover safety, it is adequate but leaves the agent guessing about the return shape and date syntax.

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?

Schema description coverage is 0%, so the description carries the full burden and does so well: it names all four parameters, explains that security is a stock code, that begin_date/end_date bound the range, and crucially enumerates the eight valid values for symbol, which the schema itself does not constrain.

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 identifies the exact resource and scope: East Money's data center, the notice compendium ('公告大全'), for an individual stock, with a concrete example URL. The verb is implicit ('fetch announcements') rather than stated, and it does not differentiate itself from the sibling stock_notice_report, which likely covers market-wide notices.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool versus alternatives, no prerequisites, and no exclusions. The only usage signal is the implicit '个股' scope, which the agent must infer.

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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