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kevynf

AKBridge MCP Server

by kevynf

fund_etf_dividend_sina

Read-onlyIdempotent

Retrieve cumulative dividend history for an ETF fund from Sina Finance by fund symbol; useful for yield and income analysis.

Instructions

新浪财经-基金-ETF 基金-累计分红 https://finance.sina.com.cn/fund/quotes/510050/bc.shtml :param symbol: 基金名称,可以通过 ak.fund_etf_category_sina() 函数获取 :type symbol: str :return: 累计分红 :rtype: pandas.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNosh510050

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description adds only the return type (pandas.DataFrame) and that it returns cumulative dividend data; it says nothing about rate limits, data freshness, or empty-result behavior, so it is a modest add-on rather than rich context.

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?

It is short, but the docstring markup (:param:, :type:, :return:, :rtype:) restates information already implied by the schema, and the raw URL adds bulk without adding selection value. The purpose line is front-loaded, which is a plus.

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?

For a one-parameter, no-output-schema read tool this is roughly adequate: the agent learns the data source, the parameter provenance, and the return type. It is still missing format clarification for the symbol and any note on how the DataFrame is shaped, so it is viable but not complete.

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 coverage is 0%, so the description must compensate, and it partly does by telling the agent the symbol comes from ak.fund_etf_category_sina(). However it labels symbol as 基金名称 (fund name) while the schema default 'sh510050' is a code, leaving the expected format ambiguous.

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?

States a specific resource (Sina Finance ETF fund cumulative dividends / 累计分红) plus the source URL and a concrete example symbol (510050), so the agent knows exactly what data this returns. It does not, however, distinguish itself from dividend-related siblings such as fund_fh_em or fund_announcement_dividend_em, which keeps it below a 5.

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 when-to-use or when-not-to-use guidance; the only procedural hint is that the symbol can be obtained via ak.fund_etf_category_sina(). No alternative tool is named or excluded, so the agent must infer routing on its own.

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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