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storehausai

ECOUNT MCP Server

by storehausai

ecount_get_inventory_list

Check inventory quantities for all or selected products as of a specific date. Use product codes, warehouse, and zero-stock filters to narrow results.

Instructions

재고현황 다건 조회. 여러 품목 또는 전체 품목의 재고를 한 번에 조회합니다. 기준일(baseDate)은 필수입니다. 품목코드 목록(prodCodes)을 생략하면 전체 품목의 재고를 조회합니다. includeZeroStock=true로 재고 0인 품목도 포함할 수 있습니다. [Rate Limit: 10분/1회, 결과 10분간 캐싱됨] [Response: date(조회기준일), count(조회건수), inventory 배열 - 각 항목: PROD_CD(품목코드), BAL_QTY(재고수량)]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whCodeNo창고코드 (선택). 특정 창고의 재고만 조회. 생략시 전체 창고
baseDateYes조회 기준일 (필수). YYYYMMDD 형식. 예: 20240115
prodCodesNo품목코드 목록 (선택). 생략하면 전체 품목의 재고 조회
includeZeroStockNo재고 0인 품목 포함 여부 (선택). true: 포함, false: 제외. 기본값: false
includeDiscontinuedNo사용중단 품목 포함 여부 (선택). true: 포함, false: 제외. 기본값: false
includeBalanceExcludedNo수량관리 제외품목 포함 여부 (선택). true: 포함, false: 제외. 기본값: false
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses rate limiting (10분/1회), result caching (10분), and the response structure, which goes beyond schema and adds useful behavioral context.

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 compact and front-loaded with the core purpose, followed by essential parameter behavior and metadata. Every sentence adds value 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?

With no annotations and no output schema, the description compensates by including response format, rate limits, and caching. It covers the main behavior well, though it leaves some boolean flags to schema descriptions (which is acceptable given full schema coverage).

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?

Schema description coverage is 100%, so the baseline is 3. The description reinforces key parameters (baseDate required, prodCodes optional, includeZeroStock) but doesn't add new semantic detail beyond what the schema already provides.

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 '재고현황 다건 조회' (multi-item inventory query) and explains it queries multiple or all items at once, distinguishing it from single-item siblings like ecount_get_inventory.

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?

It provides clear context for when to use: for multiple/all items, with baseDate required and prodCodes optional. It doesn't explicitly name alternatives, but the use case is unambiguous.

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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