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get_social_volunteer_matching

Retrieve social volunteer matching expenses by date range and keyword, showing amounts and details like matching grants, Onnuri gift certificates, and volunteer snack costs.

Instructions

사회공헌 봉사활동 매칭 지출 내역 조회 (877건).

봉사활동 매칭 관련 지출의 관리번호, 순번, 지출일자, 사용금액(원), 사용내역을 제공한다. 매칭그랜트·온누리상품권·봉사요원 간식비 등.

date_from: 지출일자 시작 date_to: 지출일자 종료 keyword: 사용내역 부분일치 필터 (예: "매칭그랜트", "봉사자", "온누리")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
date_toNo
keywordNo
date_fromNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the output fields and filter behavior (partial keyword match), and notes an approximate result count (877건). However, it does not state read-only behavior, pagination, sorting, or any prerequisites—gaps that annotations might otherwise cover.

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 concise and well-structured: a purpose sentence, a field list, example categories, and parameter explanations. It avoids fluff, though the inline count '877건' is minor extra context. Slightly more detail than strictly necessary but each line serves a purpose.

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 an output schema exists and parameters are optional with clear semantics, the description is sufficient for correct invocation. The only minor gaps are lack of explicit mention of sorting, pagination, or whether date range is inclusive—common but not critical for a simple filter tool.

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 description coverage is 0%, so the description must and does compensate fully. Each parameter (date_from, date_to, keyword) is explained with explicit meanings and examples (e.g., keyword '매칭그랜트'), making the call semantics unambiguous and highly actionable.

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 retrieves social contribution volunteer matching expense details, listing the exact fields returned (management number, sequence, expense date, amount, usage details) and example categories. This is specific and distinguishable from sibling tools like get_social_funds or get_social_donations.

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 description implies usage for volunteer matching expense queries but does not explicitly contrast with any sibling tools or state when not to use it. No alternative tools are mentioned, leaving the agent to infer based on the domain name and examples.

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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