Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Self-hosted permission-aware context layer. Search returns only what the caller may read.
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。
NACRE_SERVICE_KEY「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": {
"nacre": {
"command": "npx",
"args": [
"-y",
"@nacre.work/mcp"
],
"env": {
"NACRE_SERVICE_KEY": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add nacre --env NACRE_SERVICE_KEY=your-value -- npx -y @nacre.work/mcp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": {
"nacre": {
"command": "npx",
"args": [
"-y",
"@nacre.work/mcp"
],
"env": {
"NACRE_SERVICE_KEY": "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": {
"nacre": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@nacre.work/mcp"
],
"env": {
"NACRE_SERVICE_KEY": "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。
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
レジストリ名はドメイン nacre.work で確認済みです(レジストリがドメインの所有を確認しています)
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:NACRE_SERVICE_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(0.41.0)がレジストリに届いたのは13 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。
Nacre Your index. Your access rules. Your perimeter.
Agents see exactly what they're allowed to see.
nacre.work · Docs · Quickstart · Discussions
Nacre is a self-hosted knowledge index with fine-grained access control. Agents reach it over MCP, applications over a REST API. No chat interface, no company assistant — just the context layer underneath them.
Vector search is a solved problem. What isn't solved: making sure an agent querying a company index sees exactly the documents the requesting user is cleared for — and being able to prove it to an auditor.
read/write/admin inherited top-down. write does not imply read; admin implies both. Document-level grants and deny rules are commercial — this build refuses them and says so, rather than accepting a rule it cannot propagate.top_k returns k permitted results rather than k minus whatever got stripped out.SQLSTATE 23505, an invoice number or a variable name needs the literal match, and a dense-only index does not reliably return it.npx @nacre.work/cli signs in, creates a layer, walks a directory into it and searches — the four curl invocations the quickstart spells out, for when you want the result rather than the contract. A document that fails to index is a non-zero exit, so a nightly ingest cannot report success having indexed nothing.git clone https://github.com/nacre-work/nacre && cd nacre
cp .env.example .env docker compose --profile minimal up -d
Full walkthrough: docs/quickstart.md.
On an Apple Silicon Mac, read docs/apple-silicon.md first — the images are arm64 and the stack is native, but the embedder is the one piece you run on the host.
packages/api REST API and authorization service
packages/mcp MCP server (Streamable HTTP + STDIO) packages/worker indexing pipeline: parse, chunk, embed packages/core data model, permission resolver, shared types packages/sdk TypeScript SDK packages/admin community admin UI services/parser Python sidecar: bytes → {text, blocks, metadata} docs/ specifications — normative, and ahead of the code
Early, and it runs. The loop works end to end and has been driven by hand against a real PostgreSQL and a real Qdrant: create an organization, create a layer, grant someone read, ingest a document, poll the job to indexed, search and get the chunk back — and search as someone without the grant and get nothing while the vectors are still sitting in the index. Both surfaces work, REST and MCP over Streamable HTTP and STDIO alike. Revoking a grant removes the document from results on the next request: the permitted set is computed from the grants on every search, so there is no cache in the index to fall behind.
Search is rate limited per organization, unsafe methods take an Idempotency-Key, collections page by cursor, and reranking runs on the search path when a deployment configures a reranker. Tombstoned vectors are collected, and the SDK and the admin UI are written.
Signing in works: email and password, with rotating refresh tokens that end the session if one is replayed. init creates the first administrator and prints a generated password once. SSO is a commercial module.
The access log is readable: GET /v1/audit, newest first, cursor-paged, as JSON, JSONL or CSV. org_admin sees which documents were read — the question an audit log exists to answer — and platform_admin sees administrative actions and never that, which is rule 2 applied to the journal.
A layer can be moved onto a different embedding model. Qdrant will not add a named vector to a collection that exists, so the collection is replaced rather than altered: every point copied across with no embeddings computed, one statement to switch the pointer, then re-embedding one layer at a time. Search stays available and stays one query throughout. Before a layer switches, its reference query set is scored against the new model and a migration that lost recall stops instead of going live — that gate is off until you write a set, because it needs documents only you can pick.
A document can be uploaded as a form as well as sent as JSON, and a PDF, an Office or OpenDocument file (text, presentation or spreadsheet), an EPUB or an RTF is extracted by the parser sidecar. Both signals have to agree — the part declares the format's type and the bytes begin with its signature — because a declared type the bytes contradict is a disagreement, and sniffing alone would make the declared type decoration. Any other binary format is still refused at the edge, and a scanned PDF with no text layer is refused rather than indexed as nothing.
Tokens can be signed with an Ed25519 key instead of a shared secret, in which case the public half is published at /.well-known/jwks.json and only the process issuing tokens holds the private one.
`docker compose --profile minimal up` has been run from a clean clone, and the whole loop driven through it.
What is not built is what a commercial licence covers, and docs/licensing.md lists it: multi-tenancy, SSO, document-level permissions and deny rules, ID-JAG, SIEM export, a global admin, and quotas.
docs/ is the specification, and it still runs ahead of the code in places — start with docs/authz.md, which everything else depends on.
Six rules. Breaking any of them is a security incident, not a bug. Details in docs/authz.md.
write does not imply read.Apache 2.0 — all of it. Everything above is in this repository and stays there.
Tenancy (suspension, offboarding, quotas), SSO over OIDC with SCIM group sync, issuing document-level grants and deny rules, EMA, SIEM forwarding, global admin, backup, a required second factor and filtered directory administration are commercial modules. They live in a separate private repository under a separate license and are not distributed with this one — see docs/licensing.md for the line between the two and the one question that decides it.
Where this build meets one of them it refuses in the open: a deny rule or a document-scoped grant is answered 400 with the reason, rather than accepted and silently not enforced.
The Nacre name and mark are trademarks; see TRADEMARK.md.
There is no hosted tier, no seat count and nothing metered here, so the open half earns nothing by being used — which is the point, and also why it is worth saying who pays for it. The commercial modules do, and they are for the organizations that need them; a developer running this on a laptop is never the person being asked.
If it saved you a week, the Sponsor button at the top of this repository is the other way to say so. Nothing in this repository is behind it, and nothing will be.