Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
A validated Markdown memory vault your coding agent can recall, project by project.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"vulcanus": {
"command": "npx",
"args": [
"-y",
"@sunsato/vulcanus",
"serve"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add vulcanus -- npx -y @sunsato/vulcanus serveIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"vulcanus": {
"command": "npx",
"args": [
"-y",
"@sunsato/vulcanus",
"serve"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"vulcanus": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@sunsato/vulcanus",
"serve"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto sunsatosolutions bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.6.3) kam vor 24 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Context7, Codebase Memory, Exa
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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
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.
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.
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:
| Note | Holds |
|---|---|
Capsule | the compressed must-remember summary an agent reads first |
Hub | navigation and cluster boundary |
Context | identity, definitions, scope |
Decisions | confirmed choices and corrections |
Rules | constraints on future behavior |
Architecture / Flow / Visual Direction / Content Guidelines | optional 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.
Two optional fields on each project in vulcanus.json carry what a note cannot enforce:
| Field | Values | Answers |
|---|---|---|
kind | umbrella, product, lab, service-brand, client, client-product | what this project *is*, so an agent knows how to place and summarize it |
visibility | public, private | whether 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.
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.
Before anything else, Vulcanus offers to read an existing AI history and propose your project tree from it:
| Source | What it reads |
|---|---|
| ChatGPT data export | conversations-000.json … split batches |
| Claude.ai data export | conversations.json + projects.json |
| Claude Code | ~/.claude/projects/**/*.jsonl |
| Codex | ~/.codex/**/rollout-*.jsonl |
| Gemini CLI | ~/.gemini/tmp/**/logs.json and saved checkpoint- .json chats |
| Cursor | per-workspace chat history (state.vscdb), read through the built-in node:sqlite |
| Markdown folder | any 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.
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).
--json with --memory-only reports counts only.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.
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.
Nur der Anfang wird gezeigt; den Rest findest du im Repo