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anboyu-alt

dart-risk-mcp

by anboyu-alt

get_executive_compensation

Retrieve executive compensation data for Korean companies from DART disclosures. Analyze high-paid individuals, personal compensation, and shareholder meeting limits to detect unfair trading risks.

Instructions

임원 보수 현황을 조회합니다 (불공정거래 탐지 참고 자료).

5억 이상 고액수령자·개인별 보수·미등기임원 보수·주총 승인 한도 4개 섹션을 반환합니다.

Args: company_name: 기업명 또는 종목코드 year: 사업연도 (기본값: 직전 연도) report_type: annual(사업) | half(반기) | q1(1분기) | q3(3분기)

Returns: 임원 보수 4섹션 텍스트

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYes
yearNo
report_typeNoannual

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries full burden. It discloses that the tool returns 4 sections of text and describes parameters. However, it does not reveal behavioral traits such as data source, update frequency, rate limits, or any side effects. The description is adequate but not rich.

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 remarkably concise: a two-sentence purpose, then bulleted sections, followed by Args and Returns. It is front-loaded with important information, and every sentence contributes meaning without redundancy.

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 tool has 3 parameters, no nested objects, and an output schema, the description is fairly complete. It explains the four return sections and parameter defaults. The output schema exists, so detailed return value explanation is not necessary, but the description still provides useful context.

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%, but the description provides meaningful explanations for all 3 parameters: company_name (company name or stock code), year (fiscal year, default previous year), and report_type (annual, half, Q1, Q3). This adds significant semantic value beyond the raw schema.

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 the tool's purpose: 'Inquire about executive compensation status (reference material for unfair trade detection)'. It lists the four specific sections returned. The verb 'inquire' and resource 'executive compensation' are unambiguous, and the tool is distinct from siblings which cover other financial or disclosure topics.

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?

The description does not provide explicit guidance on when to use this tool versus alternatives. It mentions it is for unfair trade detection reference but does not give conditions, prerequisites, or exclusions. No comparisons to sibling tools are provided.

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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