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shenqingtech

deepq-financial-toolkit

by shenqingtech

行业研究观点:获取各券商对于行业的研究观点

industryResearch

Access broker research reports on specific industries, sectors, or themes to analyze market perspectives and investment insights.

Instructions

行业研究观点:获取各券商对于行业的研究观点

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endDateNo结束时间,不填则默认为今天
maxCntNo最大返回条数,默认3条
queryYes行业名称、概念板块名称、题材名称
startDateNo开始时间,不填则默认为今天

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
msgYes
codeYes
dataNo
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets' views, implying a read-only operation, but doesn't specify whether it requires authentication, has rate limits, returns paginated results, or what the output format is. The description is minimal and lacks behavioral details beyond the basic action.

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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized for a simple tool, though it could be more front-loaded with key details. There's no wasted text, but it might be too concise given the lack of behavioral context.

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?

Given that an output schema exists (as indicated in context signals), the description doesn't need to explain return values. However, with no annotations and a tool that involves fetching potentially complex research data, the description is minimal and lacks context about authentication, rate limits, or data freshness. It's adequate as a basic descriptor but has clear gaps in completeness for a research tool.

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 description coverage is 100%, with all four parameters well-documented in the input schema (e.g., 'query' for industry names, date ranges, max count). The description adds no additional parameter semantics beyond what's already in the schema. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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

Purpose2/5

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

Tautological: description restates name/title.

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 versus alternatives. The description doesn't mention any prerequisites, exclusions, or comparison to sibling tools like 'macroResearch' (which might cover broader economic research) or 'sectorReportAnalysis' (which could analyze sector reports). Usage context is implied but not explicit.

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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