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gangtiser

gangtise-mcp

by gangtiser

gangtise_security_clue_list

Retrieve AI-generated investment clue lists for individual stocks or industries based on a specified time period.

Instructions

[当前日期 2026-05-27,当前年份 2026,时区 Asia/Shanghai。用户说"今天/最近/今年/当前"时按此日期换算,不要使用训练数据年份。] 查询 AI 生成的个股或行业投资线索列表,需传入时间范围。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fromNo
startTimeYesYYYY-MM-DD HH:mm:ss。当前日期 2026-05-27,当前年份 2026(必填)
endTimeYesYYYY-MM-DD HH:mm:ss。当前日期 2026-05-27,当前年份 2026(必填)
queryModeYesbySecurity=按个股 | byIndustry=按行业(必填)
gtsCodeListNo个股代码或申万行业代码列表
sourceNoresearchReport=研报 | conference=会议 | announcement=公告 | view=观点
sizeNoMax rows (default 20 for paginated endpoints)
fetchAllNoFetch all pages; may be slow for large datasets
Behavior2/5

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

No annotations provided, and the description does not disclose behaviors like read-only nature, pagination, rate limits, or side effects. The date context note is helpful but insufficient for a complete behavioral picture.

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 description is brief but front-loaded with a date context note. While efficient, it omits key usage details and could be better structured.

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

Completeness2/5

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

With 8 parameters, no output schema, and no annotations, the description lacks completeness. It does not explain return format, pagination behavior (despite fetchAll param), or how to use queryMode and gtsCodeList effectively.

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 high (88%), so parameters are mostly explained in the schema. The description adds a date conversion note but no additional meaning for parameters like queryMode or gtsCodeList beyond what the schema provides.

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 the tool queries AI-generated investment clue lists for stocks or industries. However, it does not differentiate from sibling tools like gangtise_investment_logic or gangtise_opinion_list, which may overlap.

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 on when to use this tool versus alternatives. The description only mentions the need for a time range, but lacks context about prerequisites 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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