query-layer
Query Layer API용 MCP 서버
metrics query-layer API를 AI 어시스턴트에 노출하는 MCP(Model Context Protocol) 서버입니다. 공식 Python MCP SDK v2로 구축되었습니다.
빠른 시작
# Prerequisites: Python 3.10+, uv
uv sync
uv run mcp dev src/server.py콘솔에 출력된 URL로 MCP Inspector를 엽니다. ping 도구를 사용할 수 있습니다.
Related MCP server: prometheus-mcp
Cursor 통합
.cursor/mcp.json 파일이 미리 구성되어 있습니다. Cursor를 재시작하면 query-layer MCP 서버를 AI 어시스턴트에서 사용할 수 있습니다.
Phoenix를 사용한 로컬 트레이싱(선택 사항)
# Terminal 1: start Phoenix
uv run phoenix serve
# Terminal 2: run the server pointing at Phoenix
export PHOENIX_COLLECTOR_ENDPOINT=http://localhost:6006
uv run mcp dev src/server.pyhttp://localhost:6006에서 트레이스를 확인하세요 — Phoenix는 MCP 도구 호출, 프롬프트, GenAI span을 기본적으로 렌더링합니다.
문서
문서 | 목적 |
문제 정의, 해결 접근 방식, 구현 계획 | |
프로덕션 수준 계획 검토 | |
MCP 개념, 기술 선택, 설계 결정 | |
설정, 설치, 실행, 구성, 문제 해결 | |
로깅, 트레이싱, OTLP, 로컬 Phoenix |
프로젝트 구조
src/
server.py # MCPServer + tools (entry point)
logging_config.py # Structured JSON logging to stderr
otel_config.py # OpenTelemetry exporter configuration
.cursor/
mcp.json # Cursor MCP server configuration
docs/
plan.md # Full implementation plan
review.md # Plan review
design.md # Design document
getting-started.md # Setup guide
observability.md # Observability guide라이선스
Apache 2.0 — LICENSE 참조.
This server cannot be deployed
Maintenance
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