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kevynf

AKBridge MCP Server

by kevynf

fund_portfolio_hold_em

Read-onlyIdempotent

Fetch a fund's portfolio holdings by fund code and optional year. Returns stock holdings data for investment analysis from Eastmoney's fund archives.

Instructions

天天基金网-基金档案-投资组合-基金持仓 https://fundf10.eastmoney.com/ccmx_000001.html :param symbol: 基金代码 :type symbol: str :param date: 查询年份;传入空字符串时返回最新可用年份数据 :type date: str :return: 基金持仓 :rtype: pandas.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNo2024
symbolNo000001

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3/5.0
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 contributes provenance (the Eastmoney data source URL) and notes that an empty date returns the latest available year, which is useful behavioral context, but says nothing about pagination or data freshness.

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 content is compact but formatted as a raw docstring, and the ':type symbol: str' / ':type date: str' lines merely restate schema types. The URL and param notes earn their place; the type annotations do not.

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 two-parameter read-only fetch with no output schema, the description covers inputs and indicates the return is a pandas.DataFrame of fund holdings. It omits the returned fields/columns and the meaning of the holdings data, leaving an agent to discover the shape of the result empirically.

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 burden: it documents symbol as the fund code and date as the query year, and crucially explains that an empty string yields the latest available year data – behavior not inferable from the schema's static default of '2024'.

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

Purpose3/5

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

The description names the resource (基金持仓 / fund holdings) and gives provenance (天天基金网 fund archive), so an agent can tell this is a holdings-fetch tool. However, there is no explicit verb and no differentiation from close siblings such as fund_portfolio_bond_hold_em, fund_portfolio_change_em, or fund_portfolio_industry_allocation_em, which all concern fund portfolios.

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 prefer this tool over the bond-holdings, holdings-change, or industry-allocation siblings, and no prerequisites or context are given. The example URL implies usage but the agent must infer the selection rule entirely.

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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