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agm_personnel_xml

Extracts director and auditor candidate details from Korean AGM filings, including career, disqualifications, and recommendations, using ticker or receipt number.

Instructions

desc: 이사/감사 선임/해임 정보. 후보자별 경력, 결격사유, 추천사유, 직무수행계획. when: [tier-5 Detail] 사용자가 이사/감사 후보자 경력 상세를 요청했을 때만 사용. ticker만 넣으면 소집공고를 자동 탐색. rule: XML 파싱. 경력 병합(100자+) 시 agm_parse_fallback(parser="personnel", tier="pdf") fallback. 판정 기준은 agm_manual 참조. 기업 식별이 불확실하면 corp_identifier 먼저 호출. ref: agm_parse_fallback, agm_manual, agm_result, news_check, corp_identifier

Args: ticker: 종목코드 또는 회사명 (예: "삼성전자", "005930"). rcept_no 미입력 시 소집공고 자동 탐색. rcept_no: 접수번호 직접 지정 (예: 20260225000123). 입력 시 ticker보다 우선. format: 반환 형식. "md" (마크다운, 기본) 또는 "json"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNomd
tickerNo
rcept_noNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses automatic search for convocation notices, XML parsing, fallback to agm_parse_fallback, and references to agm_manual for judgment criteria. It does not mention auth needs or rate limits but covers key behavioral traits.

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 well-structured with labeled sections (desc, when, rule, ref, Args). It is front-loaded with the purpose and efficiently provides all necessary information without redundancy. Every part contributes to understanding and usage.

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

Completeness5/5

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

Given that an output schema exists and the description is rich with usage context, parameter details, and references to sibling tools, it is complete for an AI agent to select and invoke the tool correctly. The description covers all essential aspects.

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?

Schema coverage is 0%, but the 'Args:' block provides clear descriptions for all three parameters: ticker (company code or name), rcept_no (direct receipt number, overrides ticker), and format (md or json). This adds critical meaning beyond the schema's default values and titles.

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 provides director/auditor appointment/dismissal information with candidate details. The 'when:' section further specifies it is only for tier-5 Detail requests, distinguishing its purpose from other agm_* tools.

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

Usage Guidelines5/5

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

The 'when:' section explicitly states when to use and when not. It provides automated search behavior with ticker, fallback rules, and references corp_identifier for unclear company identification. The 'ref:' lists related tools for further context.

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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