Context7
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제작자가 쓴 설명(영어 원문)
Typed record store + hybrid search over MCP: deterministic lookups + semantic memory, per-customer.
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.
VECTROS_API_KEY, VECTROS_KEYRING_ALIAS, VECTROS_API_BASE_URL, VECTROS_MCP_TOOLS비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
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": {
"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"
}
}
}
}터미널에서 이 명령어를 실행하세요
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-server실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"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"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"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"
}
}
}
}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.
VECTROS_KEYRING_ALIAS envVECTROS_API_BASE_URL envVECTROS_MCP_TOOLS env이 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 vectros-ai 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.
입력해야 할 값: VECTROS_API_KEY
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(0.18.2)은 11일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
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제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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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