mlflow-mcp-server: Instructions file for Claude Code

CLAUDE.md

mlflow-mcp-server CLAUDE.md is an instructions file for Claude Code from us-all/mlflow-mcp-server. It costs 3,898 tokens per session, scanned A, original, MIT.

A set of Claude Code instructions for working on a specific MLflow MCP server project. MLflow is a platform for tracking machine-learning experiments, runs, and models.

In plain words
What is it for?
Guiding development and local verification of the TypeScript MLflow MCP server, including its experiments, runs, models, traces, and assessments features.
Why use it?
It gives the coding agent project-specific details about the architecture, supported MLflow version, authentication, build commands, and write permissions.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: mentions CLAUDE.md; mentions Claude Code.

This is us-all/mlflow-mcp-server's own configuration. It tells Claude Code how to work on mlflow-mcp-server itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything mlflow-mcp-server configures →

Reuse

Borrowing it

Nothing to install: this file belongs to us-all/mlflow-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/us-all/mlflow-mcp-server/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/us-all/mlflow-mcp-server

Made for: Claude Code.

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ModelPer sessionOnce invoked
Fable 5.1 $0.03898 $0.03898
Opus 5 $0.01949 $0.01949
Sonnet 5 $0.00780 $0.00780
Haiku 4.5 $0.00390 $0.00390

Measured 7d ago against content hash 310a7af14afe, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

mlflow-mcp-server CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

이 파일은 Claude Code가 이 저장소에서 작업할 때 참고하는 컨텍스트입니다.

프로젝트 개요

@us-all/mlflow-mcp — MLflow REST API를 MCP stdio 서버로 노출하는 TypeScript 구현. 66개 도구 + 4개 워크플로우 프롬프트로 experiments / runs / registered-models / model-versions / logged-models(v3) / traces / assessments 전 영역 커버. 쓰기는 MLFLOW_ALLOW_WRITE=true로 명시 옵트인.

  • 타겟 MLflow: 3.5.1+ (v3 traces/assessments REST 사용)
  • 런타임: Node 20+, stdio transport (vitest 4 의존성)
  • 인증: Bearer 토큰(Databricks PAT) / Basic auth
  • 빌드: pnpm builddist/index.js

디렉토리

src/
├── index.ts          # MCP 도구 66개 + 프롬프트 4개 등록
├── config.ts         # 환경변수 로딩
├── client.ts         # MlflowClient (fetch 래퍼, /api/ 절대경로 지원, DELETE w/ body)
├── prompts.ts        # MCP 워크플로우 프롬프트 4개
└── tools/
    ├── utils.ts                # wrapToolHandler, assertWriteAllowed, sanitize, applyExtractFields
    ├── experiments.ts          (9)
    ├── runs.ts                 (17, get-best-run/compare-runs/search-runs-by-tags 포함)
    ├── registered-models.ts    (12)
    ├── model-versions.ts       (9)
    ├── logged-models.ts        (8, MLflow 3 LoggedModel)
    ├── traces.ts               (6, v3 REST + extractFields)
    └── assessments.ts          (5, v3 REST)
dev/
├── seed.py           # demo 실험/런/모델/트레이스 시딩 (idempotent)
└── smoke.mjs         # 66 도구 + 4 프롬프트 전수 호출 자동 테스트
docker-compose.yml    # mlflow v3.12.0 + seed + mcp 프로필

로컬 검증

docker compose up -d mlflow
docker compose run --rm seed   # 실험/런/모델/트레이스까지 모두 시드됨
pnpm build && node dev/smoke.mjs   # 76/76 통과 기대

설계 원칙

  • Read-only by default: 쓰기 도구는 assertWriteAllowed()로 게이트
  • REST 직통: MLflow Python SDK 의존 없음
  • 민감정보 마스킹: 에러 메시지에서 token/password 패턴 [REDACTED]
  • camelCase ↔ snake_case: 도구 핸들러에서 수동 변환

최근 변경사항

  • v1.12.7 (2026-06-19): MCP tool annotations 적용 — @us-all/mcp-toolkit ^1.3.0inferToolAnnotations를 중앙 tool() 헬퍼에 추가, 전 도구에 readOnlyHint/destructiveHint/openWorldHint 자동 부여 (override 0). 비파괴 패치. 22/22 test.
  • v1.12.2 (2026-05-17): 3.12.0 라이브 smoke 회귀 결과 반영. (1) search-traces.maxResults 클라이언트 clamp = 500 (MLflow 3.12+ 서버가 per-page max_results>500을 INVALID_PARAMETER_VALUE로 거부; v1.12.0 changelog 의 "1000-trace 한계 제거" 표현은 부정확이었음 — 실측 cap은 오히려 1000→500). pageToken으로 unbounded 페이지 순회는 정상. (2) list-trace-attachments/get-trace-attachment describe를 "Databricks MLflow only — OSS 서버는 404"로 정정. OSS MLflow 3.12.0 handler 목록에 attachment 라우트 0개 확인. v1.7.0 의 "MLflow 3.9+" 주석은 추측 기반이었음. 76/76 smoke 유지.
  • v1.12.1 (2026-05-15): 보안 — pnpm.overrides에 fast-uri ^3.1.2 / hono ^4.12.18 / ip-address ^10.1.1 추가 (CVE-2026-6321/6322, 44455~44459, 42338 transitive 흡수). toolkit ^1.2.2 → ^1.2.3.
  • v1.12.0 (2026-05-15): MLflow 3.12.0 트래킹 — docker-compose 핀 3.11.1 → 3.12.0. search-tracesmax_results/page_token pass-through라 코드 변경 0줄로 진행. attachment 도구 회귀 검증은 v1.12.2 에서 수행 → 위 항목 참조. @us-all/mcp-toolkit ^1.2.1 → ^1.2.2 dep 핀 동반 cascade.
  • v1.11.2 (2026-05-06): MCP Server Registry 발행 — mcpName: "io.github.us-all/mlflow" 추가 + 루트 server.json (MLFLOW_TRACKING_URI required + token/basic-auth optional 메타데이터). 코드 변경 0줄.
  • v1.11.1 (2026-05-05): @us-all/mcp-toolkit ^1.2.1 핀 업데이트 — 자동 cascade. 코드 변경 0줄.
  • v1.11.0 (2026-05-05): Apps SDK UI 카드 — compare-runs 도구 결과를 _meta["openai/outputTemplate"] 통해 ChatGPT/Apps SDK 클라이언트에서 카드로 렌더 (run summary + metric/param 테이블, differing_params 하이라이트). 새 리소스 ui://widget/compare-runs.html (text/html+skybridge). Claude 클라이언트는 _meta 무시 — non-breaking. 빌드 시 src/ui/*.htmldist/ui/로 자동 복사.
  • v1.10.0 (2026-05-05): startMcpServer 채택 — toolkit v1.2.0의 런타임 헬퍼로 stdio 부트스트랩을 1줄로 교체. MCP_TRANSPORT=http로 Streamable HTTP transport 옵트인 가능 (기본 stdio). Bearer 인증, /health 엔드포인트. 기존 stdio 사용자 영향 0.
  • v1.9.1 (2026-05-05): @us-all/mcp-toolkit ^1.2.0 핀 업데이트 — 자동 cascade. 코드 변경 0줄.
  • v1.9.0 (2026-05-04): MCP Prompt analyze-failed-traces 추가 — 기존 debug-failed-traces(one-shot 조사)와 보완. 현재 윈도우 vs 직전 윈도우 비교로 실패율 추세, top N 실패 패턴 by 임팩트, 새 regression 식별. MLflow 3 GenAI 트레이싱 흐름 + 가시성 sprint Week 2 P0 항목.
  • v1.8.5 (2026-05-03): @us-all/mcp-toolkit ^1.1.0 채택 + aggregate() 헬퍼로 summarize-experiment 마이그레이션. caveats 라벨 텍스트 변경 (get-experiment failed:getExperiment failed:, search-runs failed:searchRuns failed:). summarize-runPromise.all + per-fetcher .catch() 패턴이라 마이그레이션 대상 외 — 그대로 유지.
  • v1.8.4 (2026-05-03): @us-all/mcp-toolkit ^1.0.0 핀 업데이트. toolkit API freeze (semver 1.x 보장 시작) — 코드 변경 0줄, 20/20 테스트 통과.
  • v1.8.3 (2026-05-03): Wave 6 — summarize-run의 metricHistory 각 포인트에서 중복 key 필드 제거(4k-point 기준 ~100KB 절감). summary.artifactsIncluded가 fetch 실패 시({error:...})에도 truthy였던 문제 수정 — 실제 fetch 성공 여부 반영.
  • v1.8.2 (2026-05-03): summarize-experiment의 topRuns metrics 항목에서 step/timestamp default drop (key/value만). caller-supplied extractFields 우선.
  • v1.8.1 (2026-05-02): summarize-run metric history가 MLflow 3.x에서 비어있던 문제 — /metrics/get-historymax_results 없이 호출되면 next_page_token만 반환. 25000(문서상 최대) 명시.
  • v1.8.0 (2026-05-02): summarize-experiment 어그리게이션 — experiment + topN runs (metric/start_time 정렬) + metric stats(min/max/mean) 1 call. 3-5 round-trip 대체.
  • v1.7.0 (2026-05-02): Wave 3 Resources 확장 — mlflow://run/{runId}/artifacts, mlflow://experiment/{expId}/runs, mlflow://registered-model/{name}/versions 추가. 또한 trace attachment 도구 2개(list-trace-attachments, get-trace-attachment, MLflow 3.9+).
  • v1.6.2 (2026-05-02): Wave 1 — describe trim 13, 의존성 bumps, fat-read 3개에 default extractFields.
  • v1.6.1 (2026-05-02): @us-all/mcp-toolkit ^0.2.0 채택 — 로컬 sanitize / wrapToolHandler 본문 제거, createWrapToolHandler factory로 위임. redactionPatterns: [/basic\s+\S+/i] + errorExtractors(WriteBlockedError → passthrough, MlflowError → structured {status, body})만 명시. utils.ts 87→56 lines.
  • v1.6.0 (2026-05-01): @us-all/mcp-toolkit ^0.1.0으로 마이그레이션 — tool-registry.tsapplyExtractFields를 toolkit에서 import. utils.ts의 inline 구현 제거. 단위 테스트(tool-registry, extract-fields)는 toolkit이 owns. ~170 lines 코드 절감.
  • v1.5.1: pnpm token-stats 스크립트 + CI TOKEN_BUDGET=12000 가드 추가.
  • v1.5.0: summarize-run 어그리게이션 도구 — run info + (opt) metric history per key + (opt) artifacts. 3-5 round-trips → 1 call.
  • v1.4.0: MCP Resources (mlflow:// URI scheme) 6개 — run, experiment, experiment-by-name, registered-model, model-version, trace.
  • v1.3.1: extractFields auto-apply via wrapToolHandler. experiments / runs 핵심 read 스키마에 명시적 선언.
  • v1.3.0: 카테고리 ENV 토글(MLFLOW_TOOLS / MLFLOW_DISABLE) 8 카테고리 + search-tools 메타툴.
  • v1.2.0: Webhooks 6개 + Prompt Optimization 5개 도구 추가 (도구 66→77).

Read the full file on GitHub · 141 lines

Changes

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Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 141 lines · 3,898 tokens per session scan A 310a7af14afe

Subscribe to this mod's changes

mlflow-mcp-server CLAUDE.md is an instructions file published in the GitHub repository us-all/mlflow-mcp-server (1 stars, last pushed 1mo ago), licensed MIT. It adds 3,898 tokens to every session, about $0.0195 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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