Seoul Mobility Advisor
Integrates real-time Seoul data (weather, air quality, traffic, bike-sharing) and visual context to recommend optimal transportation modes for users.
README
Seoul Mobility Advisor (mobility-mcp)
카카오 PlayMCP 공모전 출품작 — 날씨·대기질·실시간 교통·따릉이 데이터와 **사진(시각 맥락)**을 종합해 상황에 맞는 이동수단(도보/따릉이/대중교통/자가용)을 추천하는 MCP 서버.
설계 원칙
서버는 '데이터 + 결정론적 도구'만 제공한다. 자연어/이미지 해석과 추천 판단은
**호스트 LLM(PlayMCP AI / Claude)**이 담당한다. 사진 분석도 호스트가 수행해
constraints 태그(예: cannot_ride_bike, formal_attire)로 툴에 넘긴다.
빠른 시작
uv sync --extra dev # 의존성 설치
cp .env.example .env # (선택) 키 입력 — 키 없어도 mock으로 동작
uv run mobility-mcp # 로컬 실행 (stdio)
MCP Inspector로 점검
npx @modelcontextprotocol/inspector uv run mobility-mcp
원격 실행 (PlayMCP 등록/배포용, Streamable HTTP)
MCP_TRANSPORT=http uv run mobility-mcp # → http://localhost:8000/mcp/
테스트
uv run --extra dev pytest
현재 툴 (Phase 1)
| 툴 | 설명 |
|---|---|
get_bike_availability(query?, lat?, lng?, limit=5) |
서울 따릉이 실시간 대여가능 수 (키 없으면 샘플) |
로드맵
~/.claude/plans/temporal-drifting-peacock.md 참고.
Phase 2 날씨/대기/교통 클라이언트 · Phase 3 compare_transport_options 점수엔진 · Phase 4–5 Docker→KC 배포·PlayMCP 등록.
구조
src/mobility_mcp/
server.py # FastMCP 앱 + 툴
config.py # .env 키 로딩
clients/seoul_bike.py# 따릉이 클라이언트(+mock)
tests/
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