Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Local-first MCP server: semantic search over library docs. No cloud, no API key, no telemetry.
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。
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": {
"deadzone": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/laradji/deadzone:v0.7.4",
"server"
]
}
}
}在终端中运行这条命令
claude mcp add deadzone -- docker run -i --rm ghcr.io/laradji/deadzone:v0.7.4 server粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"deadzone": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/laradji/deadzone:v0.7.4",
"server"
]
}
}
}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": {
"deadzone": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/laradji/deadzone:v0.7.4",
"server"
]
}
}
}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。
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 laradji 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。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
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(0.7.4)于6个月前进入注册中心。
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 Docker。
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。
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semantic doc search. local file. no cloud. no key.
you ask in english. it answers in snippets.
Status. Vector search wired end-to-end. MCP over stdio. One binary, Linux + macOS, zero telemetry. See releases for the latest tag and the roadmap for in-flight work.
The scraper is still the messy half — #64 is honest about it.
Your AI client says "how do I register a tool?". The doc says AddTool. A grep-based index shrugs; a vector index doesn't. Deadzone is the vector index — nomic-embed-text-v1.5 over Turso's native cosine distance, wrapped in a Go binary that speaks MCP over stdio and keeps every byte on your laptop. It is, roughly, Context7 with the internet turned off.
deadzone. Subcommands for everything. No pip install, no npm i, no docker compose up.FTS5 is not invited.DEADZONE_DB_OFFLINE=1 refuses to guess. Verification failures in the scraper drop the doc, not the run.# macOS Apple Silicon — the one-liner
brew install laradji/deadzone/deadzone
VERSION=$(curl -fsSL https://api.github.com/repos/laradji/deadzone/releases/latest | grep '"tag_name"' | cut -d'"' -f4) ARCH=amd64 # or arm64
curl -L -O "https://github.com/laradji/deadzone/releases/download/${VERSION}/deadzone_${VERSION}_linux_${ARCH}.AppImage" chmod +x "deadzone_${VERSION}_linux_${ARCH}.AppImage" mv "deadzone_${VERSION}_linux_${ARCH}.AppImage" deadzone
curl -L "https://github.com/laradji/deadzone/releases/download/${VERSION}/deadzone_${VERSION}_linux_${ARCH}.tar.gz" \ | tar xz --strip-components=1
Both flavors land a deadzone executable in your current directory, so the ./deadzone server snippet below works as-is.
# Container — multi-arch (linux/amd64 + linux/arm64), ships with the DB baked, runs offline by default
docker pull ghcr.io/laradji/deadzone:latest docker run --rm -i ghcr.io/laradji/deadzone:latest server
The image bakes the binary, libonnxruntime, deadzone.db, and the nomic-embed-text-v1.5 ONNX weights (~230 MB total), and runs as a non-root user out of distroless (no shell, no package manager). DEADZONE_DB_OFFLINE=1 is set in the image so first launch is instant — no download, no volume mount, no --network access required. To refresh the index, pull a newer tag.
Windows is blocked upstream — no libtokenizers.a. Use WSL.
Verify checksums (optional but cheap):
curl -L -O "https://github.com/laradji/deadzone/releases/download/${VERSION}/deadzone_${VERSION}_checksums.txt"
sha256sum --ignore-missing -c "deadzone_${VERSION}_checksums.txt" # Linux shasum -a 256 --ignore-missing -c "deadzone_${VERSION}_checksums.txt" # macOS
AppImage needs FUSE v2. Most desktops ship it; minimal servers don't. If you get dlopen(): libfuse.so.2, either apt-get install libfuse2 (or dnf install fuse-libs) or pass --appimage-extract-and-run to bypass FUSE entirely.
./deadzone server
That's the quick-start. On first launch it fetches deadzone.db matched to this binary's version, SHA256-verifies, caches it under the platform data dir, and serves. Second launch onwards: zero network. Upgrade the binary and the DB re-fetches on next launch; don't, and the cache is served forever.
MCP client wire-up — native binary (Brew tap, tarball, or AppImage):
{
"mcpServers": { "deadzone": { "type": "stdio", "command": "/path/to/deadzone", "args": ["server"] } } }
MCP client wire-up — container (multi-arch on ghcr.io). The image ships with deadzone.db baked, so no volume mount is needed and every container start is offline-instant:
{
"mcpServers": { "deadzone": { "type": "stdio", "command": "docker", "args": ["run", "--rm", "-i", "ghcr.io/laradji/deadzone:latest", "server"] } } }
Then, from the client:
search_libraries("terraform aws") → ranked (lib_id, version) pairs
search_docs("creating an s3 bucket", lib_id=...) → snippets, token-budgeted
┌─────────────────────────────────────────────────────────────────────┐
│ search_libraries(name, limit?) → []LibraryHit │ │ ───────────────────────────────────────────── │ │ free text ──► vector match against the libs table │ │ ──► [{lib_id, version, doc_count, match_score}] │ ├─────────────────────────────────────────────────────────────────────┤ │ search_docs(query, lib_id?, version?, tokens?) → []Snippet │ │ ────────────────────────────────────────────────── │ │ natural ──► 768-dim embed ──► cosine over docs │ │ language ──► token-budgeted snippets back │ └─────────────────────────────────────────────────────────────────────┘
| Arg | Shape | Notes |
|---|---|---|
query | string | Matched semantically. Don't write keywords; write what you want. |
lib_id | /org/project | Optional filter. Grab one from search_libraries. |
version | "1.14" or similar | Optional pin; requires lib_id. version alone is rejected. |
tokens | int | Response budget. Default 5000, min 1000, ≈ 4 chars/token. |
limit | int | On search_libraries — max results. Default 10, max 50. |
name | string | Free text on search_libraries. Empty returns the most-indexed libs. |
deadzone server
│ ▼ ┌──────────────┐ stdio JSON-RPC ┌───────────────┐ │ MCP client │ ─────────────────────► │ handler │ └──────────────┘ └──────┬────────┘ │ ┌──────────────────┴──────────────────┐ ▼ ▼ ┌────────────────┐ ┌──────────────────┐ │ embedder │ │ Turso (local) │ │ hugot + ORT │ │ F32_BLOB(768) │ │ nomic v1.5 │ │ vector_distance │ └────────┬───────┘ └──────────────────┘ │ 768-dim ▲ └────────────── query vector ────────┘
| Layer | Choice |
|---|---|
| Language | Go 1.26.2, pinned via mise |
| Storage | Turso local file — native F32_BLOB(N) + vector_distance_cos |
| Driver | tursogo — CGO-free via purego |
| Embedder | hugot → nomic-ai/nomic-embed-text-v1.5, 768-dim, 8192-token ctx (int8 quantized) |
| Runtime | ONNX Runtime — binary CGO-linked at build time; libonnxruntime auto-fetched + SHA256-verified on first launch |
| Protocol | modelcontextprotocol/go-sdk over stdio |
The binary itself is CGO-linked (hugot ORT backend + static libtokenizers.a). At runtime the only native surface is libonnxruntime, loaded via dlopen after a SHA256-verified auto-download. Everything else — Go stdlib, tursogo, the model weights — is either statically linked or fetched on first launch against a pinned hash. No system installs. No sudo. If a download drifts from its pinned hash, the run aborts; there is no fallback to an unverified fetch.
Escape hatches for air-gapped boxes:
| Env var | Effect |
|---|---|
DEADZONE_ORT_LIB_PATH | Hand-positioned libonnxruntime path. Skips the download. |
DEADZONE_ORT_CACHE | Override the ORT library cache dir. |
DEADZONE_HUGOT_CACHE | Override the model-weights cache dir. |
DEADZONE_DB_CACHE | Override the deadzone.db cache dir. |
DEADZONE_DB_OFFLINE=1 | Refuse any network call. Fails loudly if nothing is cached. Set by default in the container image (which ships deadzone.db baked). |
DEADZONE_DB_AUTOUPDATE=0 | Disable the boot-time DB freshness probe (the probe runs by default; fetch-db always probes regardless of this flag). |
Default cache paths per platform:
| Platform | deadzone.db lives at |
|---|---|
| macOS | ~/Library/Application Support/deadzone/deadzone.db |
| Linux | $XDG_DATA_HOME/deadzone/deadzone.db (else ~/.local/share/...) |
| Windows | %LOCALAPPDATA%\deadzone\deadzone.db |
A sibling deadzone.db.release JSON manifest records {tag, sha256, fetched_at}. Startup compares the cached tag against the binary's compiled-in version: match → fire a 3-second freshness probe against deadzone.db.sha256 on the matching GitHub Release, atomic-swap if the remote sha differs (soft-fail to the cache on any network error); differs → fetch the new tag's release and atomic-swap; dev build → fall back to /releases/latest with a server.db_version_dev_fallback WARN. Pre-#197 binaries wrote a single-line tag-only sidecar; the JSON reader still accepts that format and rewrites it to v1 on first probe.
Contributor path. End users don't touch this — they just get what ships in deadzone.db.
Not editing YAML yourself? Open an issue via the New issue page and pick Add a library or Refresh a library. The template collects exactly what a registry entry needs.
Editing YAML yourself? Append to libraries_sources.yaml:
libraries:
versions key, urls used as-is.只显示了开头部分;其余内容请在仓库中阅读