Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Trust score for AI-generated code: scan repos, guard agent file writes, get a 0-100 score.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"umbra": {
"command": "npx",
"args": [
"-y",
"@elberacasa/umbra",
"mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add umbra -- npx -y @elberacasa/umbra mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"umbra": {
"command": "npx",
"args": [
"-y",
"@elberacasa/umbra",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"umbra": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@elberacasa/umbra",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 elberacasa 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.2.0)은 3개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Dialyne
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Real phone calls in France for AI agents: dial +33 numbers, menus, hold, AI disclosure.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Everyone is vibecoding. Nobody is verifying. Umbra scores it.
Umbra is a deterministic Trust Score (0–100) for AI-generated code: the vibe coding security scanner that verifies what your agent shipped, not what it claimed. One command, fully local, evidence for every finding.
→ [umbra-badge.umbrabadge.workers.dev](https://umbra-badge.umbrabadge.workers.dev) — the landing, live badges and report pages
Quickstart · Demo · The Audit · How it works · The Four Axes · FAQ · Roadmap · Contributing · Website
Studies put exploitable vulnerabilities in 40 to 60 percent of AI-generated code, and coding agents routinely claim "all tests pass" when three do. The tooling for *writing* code with AI is a year ahead of the tooling for *trusting* it. Umbra closes that gap: SAST rebuilt for how software gets written now, plus sandboxed verification that catches what static rules cannot.
One command scans any repo an agent produced (Claude Code, Cursor, Copilot, Windsurf, Lovable) and returns a score with file:line evidence for every finding. With --deep it goes further: Umbra builds and boots the repo in a locked-down Docker sandbox, then replays the agent's own claims against reality. If the agent is lying about tests, the score is capped below passing, with receipts.
We ran Umbra over 61 public, actively-maintained AI-built repos and published everything. The Vibe-Coding Security Audit:
| Finding | Repos hit |
|---|---|
Hardcoded-secret findings (committed .env, service keys in source) | 25% |
| API routes with no auth check | 26% |
| Injection sinks (SQL interpolation, unsafe HTML injection) | 49% |
| Entire databases / SQL dumps committed to git | 13% |
| At least one critical finding | 10% |
| Zero scored findings (genuinely clean) | 7 of 61 |
Mean trust score: 74/100. One in five repos fails outright. The full report has per-class deep dives with representative snippets and fixes, the complete per-repo table, and an honest methodology section — including the false positives we found in our own rules while running it, and fixed (rubric v4).
npx umbra-scan # check — scans the directory you're standing in
npx umbra-scan --fix # heal — applies provably-safe fixes, shows the score climbing npx umbra-scan --setup # protect — pre-commit gate, PR checks, agent guardrails
That's the whole interface. Three verbs: check, heal, protect.
Using an AI coding agent? Umbra is built to be driven by agents, not just run by humans:
--setup installs PreToolUse hooks so every file the agent writes is guarded before it lands.umbra-mcp (npx --yes -p @elberacasa/umbra umbra-mcp) and the agent gets scan_repo, guard_content, and get_score as tools.Real output, scanning a typical vibe-coded Next.js app (fixtures/bad-app in this repo, Trust Score 30/100):
$ npx umbra-scan ./fixtures/bad-app
UMBRA TRUST SCORE: 30/100 🔴
SAFE 🔴 5/100 — 14 findings CLEAN ✅ 87/100 — 10 findings RUNS — not measured — run with --deep HONEST — not measured — run with --deep
Score computed over measured axes only (full rubric: SAFE 35%, RUNS 25%, HONEST 25%, CLEAN 15%). Rubric v4. …plus 7 further findings beyond the per-rule cap (see report)
Top findings: [safe/hardcoded-secrets] Hardcoded Supabase service_role JWT — bypasses all row level security — .env:2 [safe/hardcoded-secrets] Hardcoded Supabase service_role JWT — bypasses all row level security — lib/supabase.ts:5 [safe/supabase-antipatterns] Supabase service_role key reachable from client-side code — full database bypass for anyone who opens the bundle — .env:2 [safe/supabase-antipatterns] Supabase service_role key reachable from client-side code — full database bypass for anyone who opens the bundle — app/components/UserList.tsx:10 [safe/hardcoded-secrets] Committed environment file with secret values: .env — .env:1
Notes (low confidence — not scored): [safe/missing-rate-limit] Auth endpoint with no rate-limiting signal in the repo — brute-force / credential-stuffing exposure (heuristic) — app/api/login/route.ts:3
Badge:
The exit code is 1 when the score is below 50, so CI can gate on it.
All commands and flags (the expert layer — most users never need these)
umbra [path] # path defaults to the current directory
umbra [path] --json # machine-readable output umbra [path] --offline # skip npm registry checks, fully local umbra [path] --deep # verify RUNS and HONEST in a Docker sandbox umbra [path] --report # write UMBRA.md: an agent-actionable task list umbra [path] --fix # apply provably-safe fixes and re-scan (score before → after) umbra [path] --dry-run # preview --fix without writing anything umbra [path] --baseline-write # write .umbra-baseline.json: grandfather current findings, gate only on new ones umbra [path] --baseline # use an explicit baseline file ("write" is shorthand for --baseline-write) umbra [path] --publish # self-report the score to the hosted badge service (live README badge) umbra setup # install everything (hooks + Action + agent guards) umbra init # only the pre-commit hook + GitHub Action umbra protect # only the agent PreToolUse hooks (--remove uninstalls) umbra guard --stdin # hook entrypoint (agents call this, not humans) umbra mcp # run the MCP server (bin: umbra-mcp)
The canonical package is @elberacasa/umbra; umbra-scan is the short alias. Same engine either way.
repo in
│ ▼ Layer 0 · static rules (17 SAFE + CLEAN rules, 0 tokens, <1s) ▼ Layer 1 · evidence gating (confidence-scored, low never moves the score) ▼ Layer 2 · --deep sandbox (Docker: build, boot, HTTP probe, claim replay) │ ▼ deterministic Trust Score + verdict + badge
Every finding carries a confidence level and file:line evidence. Only high and medium confidence findings move the score; hunches go to a notes section. The rubric is versioned (currently v3), so the same repo always gets the same score. Full math in RUBRIC.md.
Scanning finds problems after they land. The immune layer checks every file your agent writes before it lands. umbra protect installs PreToolUse hooks into Claude Code and Kimi Code (auto-detected, one command); the same engine backs the umbra-mcp server for MCP-native agents.
flowchart LR
CC[Claude Code hook] --> E KC[Kimi Code hook] --> E MCP["umbra-mcp: guard_content"] --> E E{"guardContent(file, content) file rules + path guard"} -->|allow / warn| W[write lands] E -->|"block (exit 2)"| B["reason fed back: agent fixes the root cause"]
npx umbra-scan protect # install the hooks; --remove uninstalls cleanly
A leaked Stripe key or an alg: none JWT never reaches the file. The path guard hard-blocks agent writes into .git/hooks and .git/config (CVE-2026-26268, the agent-planted git hook escape), and live credentials going into .env. Blocking is reserved for high-confidence critical/high findings; everything else warns, and every failure fails open. Verdicts land in ~0.2 ms, so the guard never slows the agent down. Full story: docs/immune-layer.md.
--deep: verify AI code, don't trust itThe fast scan is static. --deep is LLM code verification with evidence. Umbra copies the repo into a throwaway Docker container (no network at runtime, 512 MB / 1 CPU hard limits, 120-second kill switch), builds it, boots it, HTTP-probes its endpoints, and replays every claim found in READMEs and agent artifacts against what actually happens. Slower (minutes, not seconds) and needs a running Docker daemon. Without Docker the sandboxed axes are skipped and left out of the score; unverifiable is never punished.
Real output, deep-scanning a repo whose README lies (fixtures/claims-app, capped at 49/100 by the liar cap):
$ npx @elberacasa/umbra ./fixtures/claims-app --deep
UMBRA TRUST SCORE: 49/100 🔴
SAFE ✅ 100/100 — 0 findings CLEAN ✅ 100/100 — 2 findings RUNS — not measured — No detectable run path (no Dockerfile, no package.json start script or main entry) HONEST ⚠️ 50/100 — 2 claims failed, 2 verified, 1 unverifiable
Score computed over measured axes only (full rubric: SAFE 35%, RUNS 25%, HONEST 25%, CLEAN 15%). Rubric v4. Score capped below passing: a documented claim was verified false. Trust is the product.
Claim receipts: CLAIM FAILED: "14 tests pass" — README.md:7 — actually 3 tests pass, 0 fail CLAIM FAILED: "build passes" — README.md:9 — actually build exits 1 CLAIM VERIFIED: "All tests pass" — CLAUDE.md:3 — 3 tests pass CLAIM VERIFIED: "All tests are passing" — README.md:8 — 3 tests pass
An
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