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fastroute

로컬 MCP 플러그인: agent가 새 작업을 받으면 로컬에 설치된 모든 skill(사용자 레벨/플러그인 레벨/프로젝트 레벨)에서 Top-N 추천을 검색합니다. 추천만 하고 실행하지 않습니다.

인덱싱 방법은 SkillRouter(arXiv 2603.22455)에서 가져왔습니다: all-field 분할 멀티벡터(name+description 하나의 벡터 + 본문 약 800자 청크 각각 하나의 벡터), 쿼리는 각 청크에 대해 코사인을 계산하고 skill 점수는 max-pool을 사용합니다.

도구

도구

용도

recommend_skills

작업 시작 전 가장 관련성 높은 skill을 검색; score, tier(auto/prompt/skip, 참고값), 호출명과 경로를 반환

reindex_skills

skill 설치/삭제 후 인덱스를 증분 재구성(mtime diff), 세션 재시작 불필요

Related MCP server: skills-mcp-server

설치(Claude Code)

claude mcp add fastroute -- node /absolute/path/to/fastroute/dist/index.js

데이터 및 모델

  • 데이터 디렉터리 ~/.fastroute(FASTROUTE_DATA_DIR로 재정의 가능): fastroute.db, config.json, models/

  • embedding 모델: Xenova/paraphrase-multilingual-MiniLM-L12-v2(q8, 약 135MB), 도구를 처음 호출할 때 인터넷에서 다운로드하며 기본적으로 ModelScope 미러(FASTROUTE_EMBEDDING_HOST로 https://huggingface.co 지정 가능)를 사용; 이미 ~/.knowyoubetter/models 캐시가 있으면 바로 재사용

  • 모델은 필수 의존성: 오프라인이고 캐시가 없으면 도구는 오류와 다운로드 안내를 반환하며, 어휘 폴백 없음

  • config.json에서 재정의 가능: chunkSize(800), autoThreshold(0.9), promptThreshold(0.6), topK(5), userSkillsDir, pluginCacheDir, projectSkillsEnabled

  • chunkSize 변경 후 기존 인덱스는 자동으로 다시 분할되지 않음: ~/.fastroute/fastroute.db를 삭제한 다음 reindex_skills를 호출해야 함

개발

npm install
npm test            # 单测 + 集成(fake embedder,不联网)
npm run test:real   # 真实 embedding 冒烟(首次下载模型)
npm run eval        # distractor 评测:Hit@1/Hit@5/MRR
npm run build
npx @modelcontextprotocol/inspector node dist/index.js

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