Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Local-first evidence tracking, formal logic checks and prerequisite planning for AI agents.
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": {
"noetra": {
"command": "npx",
"args": [
"-y",
"noetra",
"mcp",
"--project",
"your-value"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add noetra -- npx -y noetra mcp --project your-value이 서버는 실행 명령어에 값을 넣어야 해서, 설정을 복사한 뒤 직접 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"noetra": {
"command": "npx",
"args": [
"-y",
"noetra",
"mcp",
"--project",
"your-value"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 실행 명령어에 값을 넣어야 해서, 설정을 복사한 뒤 직접 채워 주세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"noetra": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"noetra",
"mcp",
"--project",
"your-value"
]
}
}
}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 NOETRA2 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(0.1.0-alpha.4)은 13일 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, Systemdox
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.
Your architecture in your agents' context: checks, specs, ADRs and docs over MCP, at write time.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Give AI agents a checkable trail: evidence, formal logic and prerequisite plans.
Local-first, open-source MCP server · CLI · TypeScript SDK. The core runs on an ordinary CPU with Node.js; no GPU, AI subscription or model API key needed. It stores attributed evidence, checks supplied integer constraints through Z3, and plans from explicit dependencies. It does not replace your host AI.
Open the recorded preview · Try the engine locally · npm · Connect your agent · Tool API · Launch status
Developer alpha `0.1.0-alpha.4`. Check release availability for current npm, MCP Registry and hosted-demo status. Source and local tarball installation are available independently of those services. Image-to-3D is an optional, unfinished advanced workflow; no validated 90% fidelity claim exists.
With Node.js 24.11+ in 24.x, or Node.js 26+, run the published exact version:
npx --yes noetra@0.1.0-alpha.4 demo
Open http://127.0.0.1:7860. Check a claim, toggle a missing prerequisite or inspect an evidence record. These controls execute the actual engine, not canned results. Every run has a separate in-memory database, discarded afterward. No model downloads or optional 3D tools are installed. Stop with Ctrl+C.
No installation yet? The free online preview lets you inspect real recorded examples and the walkthrough. It displays stored results; it does not run the engine remotely. The command above runs the actual engine locally. The npm artifact was checked against the reviewed upload and a fresh anonymous registry installation passed the MCP/SQLite/Z3 self-test.
Watch the 60-second result walkthrough · How to reproduce the examples
The walkthrough presents actual engine outputs as labelled cards; it is not a browser recording. Capture provenance.
| Example | What NOETRA does | Scope |
|---|---|---|
Integer premise x ≥ 2, claim x ≥ 1 | Z3 checks entailment | Valid only for the supplied integer premises |
Same premise, stronger claim x ≥ 5 | Returns a counterexample | A formal counterexample, not a real-world measurement |
| Build → review → release, tests missing | Reports blocked actions | Actions/effects are supplied; nothing is published |
| One observed test result | Stores origin, digest and limitations | Remains an untrusted observation, not proof |
Maintained by NOETRA2. Core code is MIT licensed. Optional providers have their own licences and are not bundled or installed automatically. NOETRA is independent of Nexa apps and is not endorsed by Tencent, OpenAI, REA, Blender or other upstream projects.
Prerequisite: Node.js 24.11+ in the 24.x line, or Node.js 26+ and npm. No GPU is needed for the core reasoning/MCP tools.
git clone https://github.com/NOETRA2/NOETRA.git
cd NOETRA npm ci --ignore-scripts npm run build node dist/apps/cli/src/index.js help node dist/apps/cli/src/index.js doctor node dist/apps/cli/src/index.js test
GitHub's Code → Download ZIP also provides the source; extract it and run the npm commands from that folder. Once the exact release appears under the verified owner harshavardhan2 on npm, it can be run without a source build:
npx --yes noetra@0.1.0-alpha.4 test
npx --yes noetra@0.1.0-alpha.4 demo
Use a project-local installation for durable AI-host registration, rather than relying on an npm temporary cache path. Availability and publish verification are tracked in the release-status document; these commands are not a claim that an unverified package or hosted endpoint is live.
To build a local distributable:
npm pack
node dist/apps/cli/src/index.js setup --client codex --scope project
node dist/apps/cli/src/index.js setup --client cursor --scope project node dist/apps/cli/src/index.js setup --client claude --scope project
Setup previews configuration first. Cursor/Claude JSON setup requires a reviewed --apply --yes; Codex registration prints a TOML snippet for manual review. Reconnect the host afterward. No existing AI-client or REA settings are silently replaced. See installation.
| Component | Role | What it does not establish |
|---|---|---|
| Codex host agent | Interpreted the brief, authored scripts/candidates and reviewed output | Exact host model identifier was not recorded in the original experiment |
| Built-in image-generation tool | Created reference/concept artwork | Those images are not renders of a mesh; exact image-model/version was not recorded |
| Tencent Hunyuan3D-2mini Turbo | Actual learned lion geometry | Hidden surfaces, accurate anatomy, physical scale and materials remain unverified |
| TripoSR + U2Net/rembg | Optional local shape backend / foreground estimation | Not automatically fused detailed multi-view reconstruction |
| Blender 5.2.2 LTS | Mesh assembly, PBR baking, export and actual renders | A successful render is not an accuracy score |
| xatlas 0.0.11; OpenCV; NumPy/SciPy | UV charts, measured appearance, image/geometry calculations | Colour clusters do not identify anatomy or recover depth |
| Z3 | Checks explicit formal constraints | Does not judge artistic quality or physical truth |
| REA 6.7.0 / optional Ghidra | Authorized software investigation | They are analysis tools, not image-to-3D generators |
The lion model revision is f90a0f7df7d5e6f71109cf333f6a95a0ae3194a6; the upstream source commit is f8db63096c8282cb27354314d896feba5ba6ff8a. The pinned manifest includes full checkpoint/source hashes. Model weights, third-party applications, private accounts and local model caches are excluded.
Hunyuan is a separately licensed optional provider, not MIT-licensed model weights. Review its licence, notice and model/source provenance before provisioning it. Its licence contains territory and use restrictions. Powered by Tencent Hunyuan when that provider is enabled; Tencent does not sponsor, endorse or operate this independent project.
The images below are Blender renders of actual meshes, not generated concept pictures presented as 3D output. Details, settings and limitations are in comparisons.
Left: without NOETRA, a new host-authored procedural lion built directly in Blender from primitives, remeshing and solid geometry. No NOETRA code or learned shape model generated that mesh. Right: with NOETRA, the existing lion created through its Hunyuan3D-2mini Turbo workflow. Both pictures render real meshes using the same clay material, lighting, view directions and normalized height. The procedural lion is stylized; these different methods are not a controlled test of NOETRA's quality or accuracy. The downloadable mesh, script and second angle are in comparisons.
We also ran a separate direct Hunyuan inference with NOETRA bypassed and otherwise matching inputs/settings. That result is identical to the workflow's raw mesh; the report preserves this evidence. No numerical accuracy gain is claimed.
The local core TypeScript/build checks, 71 Node tests and CLI protocol self-test passed. The reconstruction Python suite and focused cache/region tests passed (156 tests across the recorded runs). The example meshes were actually exported, reimported and rendered. GitHub Actions results, once available, are separate from these local checks.
The lion mesh and painting remain partial. Accurate facial anatomy, semantic part-to-mesh correspondence, calibrated detailed multi-view fusion, intrinsic albedo/PBR recovery and reliable unseen geometry are unfinished. The automatic appearance-readiness gate remains false; an explicitly requested unverified paint preview does not override it or replace the accepted model.
Accuracy is not measured by triangle count, watertightness, successful export or a visually pleasing screenshot. We need a defined metric, independent ground-truth 3D data, held-out inputs and reproducible evaluation before making a numerical fidelity claim such as 90%.
Architecture · Tool API · Security · REA · Reconstruction · Appearance analysis · Surface painting · Third-party notices
npm ci --ignore-scripts
npm run check
Python reconstruction tests require separately provisioned local dependencies; model inference is optional and is not run merely by installing the core package. Report reproducible problems through GitHub Issues, with sanitized inputs and logs. Do not upload credentials, private images, AI-client settings or model weights.