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JooSeunghyeon

kookmin-stock

get_recent_news

Fetch recent Naver finance news headlines for a stock ticker or keyword, with each headline's positivity score from keyword matches to assess market sentiment.

Instructions

Return recent Naver finance news headlines about a ticker or keyword.

Each item carries a positivityScore (sum of catalyst-keyword matches minus negative-keyword matches) plus the matched keywords.

Args: query: ticker code, Korean stock name, or free-text keyword. top_n: 1..20.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_nNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description explains the positivityScore calculation and that results include matched keywords, offering some behavioral insight. However, it lacks disclosure on whether it's read-only, rate limits, error handling, or other side effects. No annotations to compensate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two sentences plus arg list), front-loaded with the purpose, and includes structured arg descriptions. No unnecessary content.

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

Completeness4/5

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

Given the presence of an output schema, the description adequately covers purpose, parameters, and key behavioral detail (positivityScore). It could mention limitations like article count or recency but remains fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by detailing that query accepts ticker codes, Korean stock names, or free-text, and that top_n ranges from 1 to 20. This adds significant meaning beyond the schema types.

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

Purpose5/5

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

The description clearly states it returns recent Naver finance news headlines about a given query. It specifies a unique function not overlapping with siblings like get_fundamentals or get_stock_quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for news retrieval but does not explicitly differentiate from siblings or provide when-to-use/when-not-to-use guidance. No mention of alternatives.

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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