Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Typed record store + hybrid search over MCP: deterministic lookups + semantic memory, per-customer.
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.
VECTROS_API_KEY, VECTROS_KEYRING_ALIAS, VECTROS_API_BASE_URL, VECTROS_MCP_TOOLSAls geheim markierte Werte sind Schlüssel zu deinem Konto. Gib sie nie weiter und füge sie nie in einen Chat ein.
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": {
"vectros-ai": {
"command": "npx",
"args": [
"-y",
"@vectros-ai/mcp-server"
],
"env": {
"VECTROS_API_KEY": "your-value",
"VECTROS_KEYRING_ALIAS": "your-value",
"VECTROS_API_BASE_URL": "your-value",
"VECTROS_MCP_TOOLS": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add vectros-ai --env VECTROS_API_KEY=your-value --env VECTROS_KEYRING_ALIAS=your-value --env VECTROS_API_BASE_URL=your-value --env VECTROS_MCP_TOOLS=your-value -- npx -y @vectros-ai/mcp-serverTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"vectros-ai": {
"command": "npx",
"args": [
"-y",
"@vectros-ai/mcp-server"
],
"env": {
"VECTROS_API_KEY": "your-value",
"VECTROS_KEYRING_ALIAS": "your-value",
"VECTROS_API_BASE_URL": "your-value",
"VECTROS_MCP_TOOLS": "your-value"
}
}
}
}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": {
"vectros-ai": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@vectros-ai/mcp-server"
],
"env": {
"VECTROS_API_KEY": "your-value",
"VECTROS_KEYRING_ALIAS": "your-value",
"VECTROS_API_BASE_URL": "your-value",
"VECTROS_MCP_TOOLS": "your-value"
}
}
}
}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.
VECTROS_KEYRING_ALIAS envVECTROS_API_BASE_URL envVECTROS_MCP_TOOLS envDiese 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 vectros-ai 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.
Das musst du eintragen: VECTROS_API_KEY
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 (0.18.2) kam vor 11 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
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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.
One-click badges install the server entry in your client. You still supply
a key — run
npx -y @vectros-ai/cli bootstrap(recommended) or paste your
ssk_.... See Connect from your client and the
honest caveats.
A Model Context Protocol server for Vectros — a typed, multi-tenant record store unified with hybrid search and citation-grounded RAG. Deterministic lookups and enumeration *and* semantic search over one isolated, per-customer index of records and documents — so an agent gets memory that's precise, not just fuzzy recall. Reached agent-natively here over MCP (Claude Desktop, Cursor, Claude Code, Cline, Continue, VS Code, hosted platforms) — and the same data is human-accessible through the Vectros app + SDKs.
npx -y @vectros-ai/mcp-server
Your agent can search your indexed corpus, query structured records, ingest documents, and ask questions grounded against documents — reaching only your tenant's data, never the public web (there are no web tools).
The fastest way to set up is the @vectros-ai/cli bootstrap command. It mints a least-privilege scoped key (ssk_*) bound to a narrowed AccessProfile, optionally scaffolds a use-case data model, and safe-merges the vectros server into your MCP client config — no root key, and no hand-editing JSON:
npx -y @vectros-ai/cli bootstrap
You pick what to set up (a blank read-only credential, or a blueprint like task tracking) and sign in once with a token from the developer portal. The command then:
ssk_* for this machine (independently rotatable),claude_desktop_config.json (Claude Desktop, Cursor, Cline). For Claude Code, add --client code: it merges the project .mcp.json and prints the equivalent claude mcp add command.Restart your MCP client and you're done. It's idempotent (re-run any time); --rotate replaces this machine's key.
Want to browse the data yourself? bootstrap sets up the key for your *agent*, not a login for *you* — so a blueprint's context won't appear in the data-plane app's switcher until you join your own user to it (the app lists only contexts your user has access in). Grant yourself a role once, either in the admin app (Access → Contexts → _your context_ → Profiles → Create profile, pick yourself from the by-email picker, choose a role — no raw id needed) or from the CLI with --principal me (resolves to your own user):
vectros access grant --principal me --context <context-id> --role <role>
Blueprints that ship a human role (e.g. agentic-sdlc's editor) let you use --role; otherwise grant inline scopes with --actions records:r,search:r,….
For scripted / agent use, set the sign-in token in the environment and skip the prompts:
VECTROS_BOOTSTRAP_TOKEN=… npx -y @vectros-ai/cli bootstrap \
--blueprint task-management --yes
Prefer to wire it up by hand? See Configure manually below.
| Client | One-click | Manual |
|---|---|---|
| Claude Desktop | Desktop Extension (.mcpb) — double-click, paste your key | JSON snippet |
| Cursor | .cursor/mcp.json, same shape as below | |
| VS Code | .vscode/mcp.json, the same entry under a top-level servers key | |
| Claude Code | claude mcp add (below) | project .mcp.json |
| Cline / Continue | — | same JSON snippet as Claude Desktop |
| Smithery | npx -y @smithery/cli install @vectros-ai/mcp-server | — |
| Codex | — | TOML snippet (below) |
The fastest path on every client is npx -y @vectros-ai/cli bootstrap — it mints a scoped key and writes the config for you. The one-click buttons install the server entry; you then supply the key (bootstrap, or paste your ssk_...).
Codex (~/.codex/config.toml):
[mcp_servers.vectros]
command = "npx" args = ["-y", "@vectros-ai/mcp-server"] env = { VECTROS_API_KEY = "ssk_live_..." }
Precision is the pitch — what this server deliberately does *not* do:
If @vectros-ai/cli is installed globally (npm install -g @vectros-ai/cli, not a one-off npx run — the server needs vectros to still be on PATH later, when IT runs, not just while bootstrap ran) and you've run vectros bootstrap with it, omit the env block entirely — the server resolves your key from the local vectros CLI keyring automatically (see Credential resolution below). vectros login alone does not do this — it stores a separate sign-in session, not this key — so bootstrap is the step that actually has to have run:
{
"mcpServers": { "vectros": { "command": "npx", "args": ["-y", "@vectros-ai/mcp-server"] } } }
No CLI installed, or a machine you can't log in on? Set the key directly instead:
{
"mcpServers": { "vectros": { "command": "npx", "args": ["-y", "@vectros-ai/mcp-server"], "env": { "VECTROS_API_KEY": "ssk_live_..." } } } }
This file lives under Claude Desktop's own per-user app-support directory, not a shared repo — but a real key pasted into it is still a live secret at rest: don't paste it into a chat, ticket, or screenshot with the value intact, and prefer the keyring form above whenever you can.
Restart Claude Desktop. The agent now sees the Vectros tools and two resources as callable surfaces.
Claude Code reads a project-scoped .mcp.json with the same shape. Never commit a real key into it — .mcp.json is exactly the kind of file teams check in to share a project's tooling, and a live ssk_*/sk_* value inside it is a credential leak the moment it lands in git history, not just if the repo is public.
The way to share this config safely: commit the entry with no `env` block at all, and let each developer's own vectros CLI keyring supply the key (see Credential resolution below — this is the same delegation git credential / docker-credential-* use). This needs each teammate to have @vectros-ai/cli installed globally and to have run vectros bootstrap with it — npx-ing the CLI once, or running vectros login alone (a separate sign-in session, not this key), leaves nothing on PATH or in the keyring for the server to find later:
{
"mcpServers": { "vectros": { "command": "npx", "args": ["-y", "@vectros-ai/mcp-server"] } } }
Or let Claude Code's CLI write that keyring-based entry for you:
claude mcp add vectros -- npx -y @vectros-ai/mcp-server
If a machine genuinely has no vectros CLI installed, you can pass the key directly with -e VECTROS_API_KEY=ssk_live_... — but then keep that config local: add .mcp.json to .gitignore first, rather than committing it with the key inside.
Add -e VECTROS_API_BASE_URL= to point the server at a different environment.
Load it into a session by restarting. A Claude Code session that was already open when you added the server won't pick it up mid-session — fully quit and reopen the project (not just re-select the tab). The /mcp panel shows the connector marketplace, not locally-configured stdio servers, so it won't confirm the server is loaded — ask the agent to call a Vectros tool instead. Config is keyed by the git common root, so a linked worktree resolves to its main repo's .mcp.json — add and open from the same project.
Windows note: if your
.npmrc(or a global npm config) points the
@vectros-aiscope at another registry, a barenpx -y @vectros-ai/mcp-server
can resolve a build other than the public release. If the command above fails
to start, either remove the scoped-registry override for a plain
npxrun, or
pin an explicit version (
npx -y @vectros-ai/mcp-server@) known to
work.
Search & RAG
| Tool | What it does |
|---|---|
hybrid_search | Hybrid BM25 + dense search across the tenant's indexed content (records + documents). Narrow by ownership (scope for one dimension, scopeFilters for several at once — e.g. one client within one org), folder, type, metadata filters, a created date window, and keyword-precision (textMode) / relevance floors. Returns the indexed projection of each hit. |
rag_ask | Ask a question grounded against the indexed corpus. Scope retrieval through search (ownership — search.scope or multi-dimension search.scopeFilters — / folder / type / metadata filters / date window) and steer generation (instructions, and temperature for the models that accept one). Streaming generation aggregated; progress notifications keep the call alive for the generation window. Optional providerAlias routes one call through your own model provider (see below). |
document_ask | Ask a question grounded against a single document. Same aggregation + progress-notification shape as rag_ask, including the optional providerAlias. |
Records (structured, schema-validated data)
| Tool | What it d
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