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isnow890

Data4Library MCP Server

by isnow890

search_popular_books_by_library

Find popular borrowed books by library or region using filters like age, gender, and rental period. Get data from South Korea's public library system.

Instructions

도서관/지역별 인기대출 도서를 조회합니다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageNo연령코드 (0=영유아, 6=유아, 8=초등, 14=청소년, 20~60=연령대, a8/a10/a12=초등 학년군, a14=중등, a17=고등)
kdcNo대주제분류 (0=총류, 1=철학, 2=종교, 3=사회과학, 4=자연과학, 5=기술과학, 6=예술, 7=언어, 8=문학, 9=역사)
endDtNo검색종료일자 (대출기간) - YYYY-MM-DD 형식
genderNo성별코드 (0=남성, 1=여성, 2=미상)
pageNoNo페이지번호
regionNo지역코드 (11=서울, 21=부산, 22=대구, 23=인천, 24=광주, 25=대전, 26=울산, 29=세종, 31=경기, 32=강원, 33=충북, 34=충남, 35=전북, 36=전남, 37=경북, 38=경남, 39=제주)
to_ageNo종료연령
addCodeNoISBN부가기호 (0=교양, 1=실용, 2=여성, 4=청소년, 5=학습참고서(중고), 6=학습참고서(초등), 7=아동, 9=전문)
dtl_kdcNo세부주제코드 - search_detailed_kdc_codes 도구로 검색하세요
libCodeNo도서관코드 - search_library_codes 도구로 검색하세요
startDtNo검색시작일자 (대출기간) - YYYY-MM-DD 형식
from_ageNo시작연령
pageSizeNo페이지크기 (최대 1000)
book_dvsnNo도서구분 (big=큰글씨도서, oversea=국외도서)
dtl_regionNo세부지역코드 - search_detailed_region_codes 도구로 검색하세요

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.7

TDQS

C2.9/5.0
Behavior2/5

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

The verb '조회합니다' implies a read-only query, but no behavioral details are disclosed. There is no mention of pagination, filtering defaults, output format, or side effects. With no annotations provided, the description carries the full burden and fails to provide meaningful transparency beyond the obvious.

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 concise sentence that directly states the purpose without excess words. It is appropriately front-loaded and economical, though it lacks the richness that would elevate it to a 5.

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

Completeness2/5

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

Given the tool's complexity (15 parameters, no output schema, no annotations), this description is far too sparse. It does not explain what the response contains, how to construct a meaningful query, or whether any minimum filter is needed. The schema covers parameter meanings but the description fails to tie the tool together for an agent.

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?

The input schema describes 100% of parameters, so the baseline is 3. The description itself adds no parameter-level detail; it only mentions the broad categories 'library/region' which are already covered by existing parameter descriptions.

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

Purpose4/5

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

The description clearly states a specific action ('조회합니다') and a specific resource ('도서관/지역별 인기대출 도서'), making the tool's purpose unambiguous. However, it does not explicitly differentiate itself from similar sibling tools like search_popular_books or get_popular_books_by_library, so it falls short of a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It neither mentions prerequisites, exclusions, nor recommended scenarios, leaving the agent to infer usage from the name alone.

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