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EricSeokgon

egovframe-scaffold-mcp

by EricSeokgon

add_ai_components

Assembles an AI RAG chatbot with document upload, embedding, hybrid search, and LLM response into an existing Boot project. Supports dry-run for preview.

Instructions

공식 egovframe-ai-rag 샘플 기반 AI RAG 챗봇(문서 업로드→임베딩→하이브리드 검색→LLM 응답)을 기존 Boot 프로젝트에 조립합니다. 소스·설정(application-ai.yml 프로필)·UI·인프라를 복사하고 pom에 누락 의존성만 마커 구간으로 삽입합니다(백업 생성, 제거 시 원복). 기존 파일과 충돌하면 아무것도 쓰지 않고 거부합니다. dryRun=true로 먼저 미리볼 수 있습니다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoegovframe-ai-rag 브랜치/태그 (기본: 카탈로그 기준 브랜치)
stackYesAI 스택: spring-ai(Redis Stack) | langchain4j(PGVector). 상호 배타
dryRunNotrue면 복사·병합 없이 계획만 미리보기(네트워크 불필요)
includeUiNo채팅 UI(chat.html·static) 복사
projectDirYes대상 프로젝트 디렉터리(절대경로 권장). egovframe-boot-starter-parent 기반 Boot 프로젝트
includeInfraNodocker-compose.ai.yml·Dockerfile.ai·k8s/ai 복사
includeTestsNo샘플 테스트 복사
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 key behaviors: copies sources/config/UI/infra, inserts dependencies, creates backup, refuses on conflict, and supports dryRun preview. It does not detail side effects like modification of existing files despite backup, but overall it is transparent.

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 paragraph in Korean, about three sentences. It is concise and packs essential information without redundancy, though it could be slightly restructured for readability.

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 7 parameters, no output schema, and no annotations, the description covers core functionality, conflict handling, dryRun, and stack details. It does not cover error handling or success indications, but it is sufficiently complete for an AI agent to decide selection and invocation.

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 100%, so baseline is 3. The description adds context beyond schema: it explains the purpose of stack choices (spring-ai vs langchain4j with backends) and details dryRun behavior. This adds meaning beyond the parameter descriptions.

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 adds an AI RAG chatbot based on the egovframe-ai-rag sample to an existing Boot project. It specifies the actions (copy sources, config, UI, infra) and distinguishes from sibling tools like remove_egovframe_components or add_egovframe_components.

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 indicates usage for adding an AI chatbot to a Boot project, mentions safe mode (dryRun), conflict behavior, and stack choices with mutual exclusivity. However, it does not explicitly state when not to use or provide alternatives, though the sibling list implies different purposes.

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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