Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Typed record store + hybrid search over MCP: deterministic lookups + semantic memory, per-customer.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
VECTROS_API_KEY, VECTROS_KEYRING_ALIAS, VECTROS_API_BASE_URL, VECTROS_MCP_TOOLS「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"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"
}
}
}
}ターミナルでこれを実行します
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動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"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"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"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"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
VECTROS_KEYRING_ALIAS envVECTROS_API_BASE_URL envVECTROS_MCP_TOOLS envこの設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント vectros-ai で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
入力が必要なもの:VECTROS_API_KEY
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.18.2)がレジストリに届いたのは11 日前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日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.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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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