Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Local-first MCP server: semantic search over library docs. No cloud, no API key, no telemetry.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"deadzone": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/laradji/deadzone:v0.7.4",
"server"
]
}
}
}Run this in your terminal
claude mcp add deadzone -- docker run -i --rm ghcr.io/laradji/deadzone:v0.7.4 serverPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"deadzone": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/laradji/deadzone:v0.7.4",
"server"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"deadzone": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/laradji/deadzone:v0.7.4",
"server"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account laradji, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.7.4) reached the registry 6 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
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.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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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.Only the beginning is shown; read the rest in the repo