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kgy0617

ECOS MCP Server

by kgy0617
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

통계 메타데이터

get_statistic_meta
Read-onlyIdempotent

Retrieve metadata for a specified statistical dataset from the Bank of Korea's ECOS, including structure, methodology, and description. Supports Korean and English responses.

Instructions

통계 데이터셋의 메타데이터(구조, 작성방법, 설명)를 조회합니다.

Args:
    data_name: 데이터셋 이름 (필수). 예: "경제심리지수", "소비자물가지수"
    start_count: 조회 시작 순번 (기본값: 1)
    end_count: 조회 끝 순번 (기본값: 100)
    language: 응답 언어 — "kr" 또는 "en"

Returns:
    메타데이터

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNokr
data_nameYes
end_countNo
start_countNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare read-only and non-destructive behavior, so no contradiction exists; the description's '조회' matches readOnlyHint. It adds useful context about the content returned (structure, creation method, description) but not deeper behavioral details such as pagination limits or unknown-name handling.

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 purpose is front-loaded and the Args section is compactly organized. The only minor waste is the final 'Returns: 메타데이터' line, which largely restates the opening sentence.

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?

For a read-only metadata lookup with four simple parameters, the description covers the required argument, defaults, allowed language values, and an example data_name. Without an output schema, the return description is still thin (just '메타데이터'), but the description is enough to select and invoke the tool correctly.

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%, so the description carries the burden; it provides a required data_name example, defines start_count/end_count as sequence bounds with defaults, and constrains language to 'kr' or 'en'. This adds real value beyond the typed schema fields.

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 uses a specific verb ('조회' / retrieve) and a concrete resource: metadata of a statistics dataset, including structure, methodology, and description. This clearly separates it from sibling tools that search statistics or return key statistics values.

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 purpose sentence implies use when the agent needs dataset metadata, but it never states explicit conditions or names alternatives such as get_key_statistics or search_statistics. No 'when not to use' guidance is 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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