프로토타입 · 미리 보기: 공식 레지스트리의 모든 서버를 담고 있으며 30분마다 동기화해요. 리뷰와 계정 기능은 다음 단계에서 열려요.
PopNx MCPAI를 쓰는 사람을 위한 MCP 서버 · 쉬운 말로 설명해요

Vulcanus

제작자: sunsatosolutions버전 0.6.3i★ –i내 컴퓨터에서 실행
레지스트리 이름io.github.sunsatosolutions/vulcanus

설치

개요

제작자가 쓴 설명(영어 원문)

A validated Markdown memory vault your coding agent can recall, project by project.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 내 컴퓨터에서 실행
  • 전송 방식: stdio
  • 카테고리: 검색 및 AI 기억

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 레지스트리에 입력해야 할 키가 나와 있지 않아요.

필요한 것

  • npmNode.js 설치(npx 명령어용)

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": {
    "vulcanus": {
      "command": "npx",
      "args": [
        "-y",
        "@sunsato/vulcanus",
        "serve"
      ]
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add vulcanus -- npx -y @sunsato/vulcanus serve

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "vulcanus": {
      "command": "npx",
      "args": [
        "-y",
        "@sunsato/vulcanus",
        "serve"
      ]
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "vulcanus": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@sunsato/vulcanus",
        "serve"
      ]
    }
  }
}

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.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 sunsatosolutions 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

검색 및 AI 기억 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 7274개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 7274개 중 61%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 7274개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

Vulcanus, 안전한가요?

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.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.6.3)은 24일 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Context7, Codebase Memory, Exa

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Context7

제작자: upstash

Up-to-date docs for AI agents

컴퓨터 또는 클라우드★ 64,224MIT어제 업데이트
설치

Codebase Memory

제작자: DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

내 컴퓨터에서 실행★ 48,148MIT21일 전 업데이트
설치

Exa

제작자: exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

클라우드에서 실행★ 5,213MIT지난달 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

Vulcanus

Your coding agent starts every session cold. You re-explain the architecture, repeat decisions you already made, and watch it break a rule you set last week. The context lives in your head and in a scrollback buffer that is already gone.

Vulcanus fixes that at the source. It builds an AI-readable second brain — a Git-versioned vault of linked Markdown that you and your agents both read — and serves it over MCP, so an agent can recall a project instead of guessing at it.

npm install -g @sunsato/vulcanus

vulcanus

Install it globally: the MCP server (vulcanus serve), the skills, and the commit hook all run the vulcanus command, so it has to be on your PATH. npx @sunsato/vulcanus works for a one-off look, but nothing you wire to your agents can reach it.

You answer a few questions, and Vulcanus writes the whole vault — routing layer, operator profile, agent protocol, and one memory cluster per project — then validates that the graph actually holds together. Already keep an Obsidian vault? It adds the memory structure to that vault instead of creating a separate one.

Requires Node 22.12 or newer. It runs locally: no account, and no automatic network call beyond an optional once-a-day version check. Upgrade with npm install -g @sunsato/vulcanus@latest.

A Sunsato product · vulcanus.sunsato.com

Why a vault and not a prompt

A CLAUDE.md that grows forever is the thing this replaces. It gets read in full on every task, it drifts out of date silently, and nothing checks that what it claims is still true.

A vault is layered instead, so recall is scoped: an agent reads the Recall Map, then the one Capsule its task needs, and goes deeper only for authority or detail. vulcanus stats measures that on your own vault — in the demo above, a task-scoped recall reads 59% less than the whole vault. vulcanus doctor then enforces that every link resolves and every project is reachable, so the memory fails loudly instead of rotting quietly.

Wire it to your agent

The vault is only worth as much as the recall it gives your tools, so that is one command each:

claude mcp add vulcanus -- vulcanus serve   # Claude Code, or any MCP client
vulcanus skills --install   # skills that run the real commands, in every repo
vulcanus agents             # the block to paste into a tool's global instructions

MCP server, Skills, and Making agents actually use it below cover what each one exposes.

What it creates

YourVault

├─ AGENTS.md # the protocol AI agents must follow in this repo ├─ CLAUDE.md # points Claude Code at that protocol ├─ USING-WITH-AI.md # how to make every tool recall this vault ├─ README.md ├─ vulcanus.json # the manifest everything is derived and validated from ├─ .claude/skills/ # invocable skills for Claude Code ├─ .agents/skills/ # the same skills for Codex, Cursor, and Gemini CLI ├─ 00_System/ # Index, Recall Map, Admin Profile, Rules, Update Format … ├─ 02_Projects/ # one cluster per project └─ _imports/ # raw AI exports, ignored by Git

Each project cluster is five notes plus any specialized ones you ask for:

NoteHolds
Capsulethe compressed must-remember summary an agent reads first
Hubnavigation and cluster boundary
Contextidentity, definitions, scope
Decisionsconfirmed choices and corrections
Rulesconstraints on future behavior
Architecture / Flow / Visual Direction / Content Guidelinesoptional domain depth

The point of the layering is token economy: an agent reads the Recall Map, then one Capsule, and only goes deeper when the task actually needs authority or detail.

What a project is, and who may hear about it

Two optional fields on each project in vulcanus.json carry what a note cannot enforce:

FieldValuesAnswers
kindumbrella, product, lab, service-brand, client, client-productwhat this project *is*, so an agent knows how to place and summarize it
visibilitypublic, privatewhether an agent may name this project outside the vault

init and add project ask for both, and visibility is recorded either way — "nobody has said" and "the operator said public" are different states, and only one of them is safe to act on. recall returns the marker and, for a private project, an instruction not to name it in anything public. doctor warns on a value it does not recognize rather than failing the vault, because a typo like privte would otherwise read as public to every agent.

This is a signal, not access control: nothing here encrypts or hides a file.

Notes you keep yourself

The generated system layer is a fixed list, so a note you write under the system directory would otherwise read as unmanaged, and the System Hub linking it would read as over-linked — two findings for doing nothing wrong. systemNotes in vulcanus.json names those notes:

{ "systemNotes": ["Release Notes", "Semantic Index Strategy"] }

They are branded like every other system note, so Release Notes resolves to Release Notes in a branded vault. Declaring one only makes the vault aware of it: the note is never created, never rewritten, and update leaves it alone. The System Hub may link it, sync adds the bullet if it is missing, and a note declared but never written is reported so the declaration cannot quietly point at nothing. A system note nobody declared is still reported as unmanaged.

The first question is import

Before anything else, Vulcanus offers to read an existing AI history and propose your project tree from it:

SourceWhat it reads
ChatGPT data exportconversations-000.json … split batches
Claude.ai data exportconversations.json + projects.json
Claude Code~/.claude/projects/**/*.jsonl
Codex~/.codex/**/rollout-*.jsonl
Gemini CLI~/.gemini/tmp/**/logs.json and saved checkpoint- .json chats
Cursorper-workspace chat history (state.vscdb), read through the built-in node:sqlite
Markdown folderany directory of notes — the folder names become the project signal

Locations are auto-detected, so usually you just pick one from a list. A Markdown folder is the exception: it is never probed on its own, and only scanned when you name the path.

Re-running import on the same source proposes only what is new — conversation ids already read are remembered in the vault's state directory. --all re-reads everything. --json prints the candidates with their evidence and writes nothing.

Nothing from your history is copied into the vault unless you accept it. Conversations are read locally, reduced to candidate project names with evidence counts, and discarded. Only the names you tick become notes; the Import Log records how many conversations were scanned, never their content.

Names the source itself grouped conversations under — a Claude project, a repository directory — are the strong signal and come pre-checked. Names inferred purely from title frequency are proposals and start unchecked.

Decisions you already made

Your history usually holds more than project names: the choices you settled and the rules you kept repeating. After the project step, import asks whether to look for them (--memory says yes up front, --no-memory skips the question, and --memory-only skips project discovery for a vault that already has its projects).

  • Only your own messages are read. An assistant's sentence is not your decision.
  • Sentences are matched against decision and rule phrases in English, Turkish, German, and Spanish — "we decided", "from now on", "karar verdik", "ab sofort", "a partir de ahora" — and ranked by how strong the phrase is, how many conversations repeat it, and how recent it is. Questions and code are ignored.
  • A conversation counts toward a project when its source grouped it there or its title names the project. Body mentions do not.
  • What the vault already records is dropped. A candidate that looks like a revision of a live decision is offered as its replacement, so the old one is marked rather than contradicted (see When a decision changes).
  • You review each candidate: accept as decision, accept as replacement, accept as rule, edit the wording, skip, or stop. At most 15 per project per run. Nothing is accepted unattended — --json with --memory-only reports counts only.
  • What you accept is written in the wording you accepted, with a source:: import · · line. Skipped candidates are discarded, and reviewed conversations are remembered so they are not proposed again (separately from project discovery, so one never hides conversations from the other).

--ai-extract [cli] hands the candidate sentences — not transcripts — to an installed AI CLI to merge duplicates, drop noise, and tighten wording, after showing you what will be sent. Anything it returns that does not trace back to a sentence it was given is discarded.

Commands

vulcanus init            # create a vault, or add memory to an existing Obsidian vault (default command)
vulcanus status          # one-screen vault health: projects, notes, doctor result, git state
vulcanus stats           # token budget: what a cold-start agent reads, what recall saves
vulcanus doctor          # validate the vault against its manifest
vulcanus add project     # add projects and wire them into the graph
vulcanus project remove "Name"        # unlink a project; its notes move to _archive/

vulcanus project rename "Old" "New" # folder, notes, and every link, in one move vulcanus project archive "Name" # mark it archived (--restore undoes it)

vulcanus import          # propose more projects from an AI export or a notes folder
vulcanus serve           # serve the vault to MCP clients: recall, search, append_decision, …
vulcanus agents          # print the block that makes your AI tools use the vault
vulcanus skills          # agent skills that run these commands for you
vulcanus update          # bring the vault up to date with a newer CLI
vulcanus sync "topic"    # validate, then commit and push

vulcanus sync --watch # regenerate and revalidate on every edit; never commits

vulcanus hooks install   # a pre-commit hook that refuses to commit a broken graph
vulcanus completion zsh  # completion script for bash | zsh | fish | pwsh

init accepts --lang en|tr|de|es, --ai [cli], and a target directory; add project and import accept --ai [cli]; status and stats accept --json; doctor accepts --repair and --json; import accepts --source, --path, --ai-group [cli], --memory, --no-memory, --memory-only, --ai-extract [cli], --json, and --all; update accepts --dry-run, --force, --profile core|full, and --json; sync accepts --dry-run, --json, and --watch; skills accepts --raw, --install, and --force.

--verbose and --quiet work on every command, and --json implies quiet so machine-readable output owns stdout.

Exit codes are part of the contract: 0 the command did what it said, 1 the vault or the operation failed validation, 2 the command was used wrongly (no vault, bad flag), 130 cancelled at a prompt. Every failure prints what happened, why, and what to do next.

Scripting `

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