Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Typed record store + hybrid search over MCP: deterministic lookups + semantic memory, per-customer.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
VECTROS_API_KEY, VECTROS_KEYRING_ALIAS, VECTROS_API_BASE_URL, VECTROS_MCP_TOOLS标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"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"
}
}
}
}在终端中运行这条命令
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-server运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"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"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"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"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
VECTROS_KEYRING_ALIAS envVECTROS_API_BASE_URL envVECTROS_MCP_TOOLS env这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 vectros-ai 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
你需要填写:VECTROS_API_KEY
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.18.2)于12天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
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.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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
只显示了开头部分;其余内容请在仓库中阅读