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storehausai

ECOUNT MCP Server

by storehausai

ecount_create_customer

Create new customer and supplier records in ECOUNT with required code and name, plus optional contact and business details. Handles up to 300 records per request.

Instructions

거래처 등록. 새로운 거래처(고객사/협력사)를 ECOUNT에 등록합니다. 거래처코드(CUST_CD)와 거래처명(CUST_DES)은 필수이며, 대표자/주소/연락처/사업자번호 등은 선택입니다. 최대 300건까지 한 번에 등록 가능합니다. [Rate Limit: 10초/1회] [Response: success(성공건수), failed(실패건수), slipNos(전표번호 배열), details(상세결과 - IsSuccess, TotalError, Errors 배열)]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customersYes등록할 거래처 목록. 1~300건까지 한 번에 등록 가능
Behavior4/5

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

With no annotations, the description carries the burden of disclosing behavior. It adds rate limit (10초/1회), batch size (max 300), and a structured response summary (success/failed counts, slipNos, details with errors). It also clarifies it creates only new customers ('신규'), implying no overwriting. However, it does not mention authentication requirements or potential duplicate handling, which could be important for a write operation.

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 compact: three sentences plus bracketed rate limit and response info. It is front-loaded with the main purpose, then details requirements and limitations. No filler or repetition, though the rate limit and response format are arguably additional but valuable.

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?

Given the tool's moderate complexity (single array parameter with 9 nested fields), the description covers the core aspects: what it does, required/optional fields, batch limit, rate limit, and response shape. The absence of an output schema is compensated by the explicit response description. It could be slightly more complete with details on prerequisites or error scenarios, but overall it provides enough context for an agent 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.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage for the single 'customers' parameter and all nested fields, so the baseline is 3. The description reiterates that CUST_CD and CUST_DES are required and groups optional fields (대표자/주소/연락처/사업자번호), but adds no new semantic details beyond what the schema already documents. It does not provide example values or dependencies beyond the schema.

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 the tool's function: it registers a new customer (client/partner) in ECOUNT. The verb '등록' (register) and resource '거래처' (customer) are specific, and it distinguishes itself from sibling tools like ecount_create_product or ecount_create_sale_order by targeting customers. It also clarifies the required vs optional fields.

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?

The description provides clear context: this is for registering new customers in ECOUNT, with batch capacity up to 300 and a rate limit. However, it does not explicitly mention when to use this tool over alternatives or any exclusions, though the tool's name and sibling differentiation make the use case obvious. No explicit 'when-not' guidance is given.

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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