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basic

ETF 基础信息(ETF)

Args: symbol: 代码原样匹配(ETF/板块/外汇/港股等) exchange: 交易所:SSE 上交所 / SZSE 深交所 list_status: 上市状态:L 上市 / D 退市 / P 暂停上市 etf_type: ETF 类型,如境内、QDII index_code: 跟踪指数代码(带后缀)

Returns: JSON 数组;字段: symbol, csname, extname, cname, index_code, index_name, setup_date, list_date, list_status, exchange, mgr_name, custod_name, mgt_fee, etf_type

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNo代码原样匹配(ETF/板块/外汇/港股等)
etf_typeNoETF 类型,如境内、QDII
exchangeNo交易所:SSE 上交所 / SZSE 深交所
index_codeNo跟踪指数代码(带后缀)
list_statusNo上市状态:L 上市 / D 退市 / P 暂停上市

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

The description discloses the return format (JSON array) and the exact fields returned, which provides some transparency beyond the schema. However, it does not explicitly state that the tool is read-only, nor describe behavior when filters are omitted or when no matches are found.

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 compact and well-structured with clear 'Args' and 'Returns' sections. The only minor redundancy is the repeated 'ETF' in the title and parenthetical.

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?

The description covers all parameters and return fields, which is useful for a query tool. However, it does not explain the default query behavior when all parameters are optional, nor how multiple filters interact, leaving some ambiguity for a user trying to construct a valid request.

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?

The input schema already describes all five parameters with identical descriptions. The description's Args section simply repeats the schema content, adding no new semantic information. Since coverage is 100%, a baseline of 3 is appropriate.

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 identifies the tool as providing ETF basic information (ETF 基础信息) and lists filtering parameters, but lacks an explicit verb like 'query' or 'get', and does not distinguish it from sibling tools like 'stocks' or 'etf_daily'.

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?

No guidance is provided on when to use this tool instead of alternatives such as etf_daily or tracking_indices. The description only lists parameters without any context about use cases or exclusions.

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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TDQS

B3.3/5.0
Disambiguation3/5

Several tools have overlapping purposes or unclear names, such as daily vs etf_daily vs index_daily vs fx_daily, and fundamentals vs technical_factors_pro (which also includes PE/PB). top_inst and top_list are also easily confused. Descriptions help, but the names alone are not always sufficient to distinguish them.

Naming Consistency3/5

All names use lowercase with underscores, which is consistent, but there is variation in number (daily vs stocks), specificity (daily vs index_daily), and verbosity (top_inst vs top_list). The pattern is not uniform across the set, making it less predictable.

Tool Count3/5

45 tools is on the heavy side for an MCP server, and there is redundancy (technical_factors and technical_factors_pro overlap significantly). For a broad financial data API, the count is justifiable, but it borders on overwhelming.

Completeness4/5

The tool set covers a wide range of financial data: quotes, fundamentals, technicals, financial statements, corporate actions, money flows, ETF data, index data, and news. There are minor gaps (e.g., no bond data) but the core domain of Chinese A-share/ETF/FX data is well covered.

Resources