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query-layer

by ramraj-patel
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Python

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.py

콘솔에 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.py

http://localhost:6006에서 트레이스를 확인하세요 — Phoenix는 MCP 도구 호출, 프롬프트, GenAI span을 기본적으로 렌더링합니다.

문서

문서

목적

계획

문제 정의, 해결 접근 방식, 구현 계획

검토

프로덕션 수준 계획 검토

설계

MCP 개념, 기술 선택, 설계 결정

시작하기

설정, 설치, 실행, 구성, 문제 해결

관찰 가능성

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 guide

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Apache 2.0 — LICENSE 참조.

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