Skip to main content
Glama

회계·공시 자료 전문검색

search_documents
Read-onlyIdempotent

MyFSS MCP의 search_documents 도구는 금융감독원(FSS)·금융위원회(FSC) 회계·공시 자료를 전문검색합니다.

[Purpose]

  • What 감독당국 checks — 심사·감리 지적사례·감독지침·모범사례 등 게시판 11종.

  • Filters alone (no query) sweep a topic.

[Usage]

  1. "전환사채 콜옵션 관련 감독원 자료" → query="전환사채 콜옵션"

  2. "작년 감리 지적사례 전부" → board="sanction_case", date_from="2025-01-01", date_to="2025-12-31"

  3. "내부회계관리제도 주제로 20건" → topics=["내부회계관리제도"], limit=20

[Response]

  • { total, showing, offset, note, results:[{ntt_id, date, board, source, title, nature, topics, depts, summary, url, snippet?}] }

  • total = 전체 건수, showing = 이번 페이지 — paginate with offset.

  • source: "fss"=금융감독원, "fsc"=금융위원회.

  • snippet marks matches with [ … ]; only when a query matched.

[Rules]

  • board is asymmetric: param = English key, response = 한글 라벨 — never feed it back.

  • topics·depts are joined strings (", " / " / "), not arrays — split first.

  • ≥3-char tokens → FTS (AND, rank); 1~2-char → LIKE. No query → 게시일 DESC, no snippet.

  • Corpus = 2010 onward (absent = 미수록); quote body via get_document, not summary·snippet.

Input Schema

NameRequiredDescriptionDefault
deptNo담당부서명 부분일치 (예: '회계감리')
boardNo게시판 필터
limitNo최대 결과 수 (기본 15)
queryNo검색 키워드 (공백 구분, 예: '전환사채 콜옵션')
natureNo문서 성격 필터
offsetNo페이지네이션 오프셋
sourceNo발행기관 필터 (fss=금융감독원, fsc=금융위원회)
topicsNo주제 태그 필터 (하나라도 일치, list_tags의 topics 값)
date_toNoYYYY-MM-DD 이전
date_fromNoYYYY-MM-DD 이후

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover the read-only/idempotent safety profile, and the description goes well beyond them: FTS vs LIKE token-length threshold (AND, rank), default 게시일 DESC ordering with no snippet when query is absent, corpus starting 2010, and the [ … ] snippet marker convention. These are non-obvious retrieval behaviors an agent cannot get from structured fields.

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?

Sectioned into Purpose/Usage/Response/Rules with the core capability front-loaded. Every bullet is operational — examples, response fields, and four hard rules — with no filler prose.

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?

No output schema exists, and the description supplies the full response shape (total/showing/offset/note/results with field list), pagination semantics, and source code meanings. For a 10-parameter, zero-required tool this covers everything needed to call and interpret it.

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 100%, so the baseline is 3, but the description adds real semantics the schema lacks: board is asymmetric (English key in, Korean label out, never feed back), topics/depts are joined strings that must be split, and token length determines search mode. This meaningfully changes how parameters get constructed.

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?

Names a specific verb and resource ('전문검색' of FSS/FSC 회계·공시 자료) and scopes it to 11 boards, immediately distinguishing it from get_document, list_recent and list_tags. An agent can tell what corpus this searches without opening the schema.

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

Usage Guidelines4/5

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

Three concrete query-to-parameter examples show when to use keyword search versus pure filter sweeps, and the Rules section routes body retrieval to get_document. It never explicitly states when to prefer list_recent or list_tags instead, so selection against those siblings stays inferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources