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PopNx MCPMCP servers for people who use AI · explained in plain words

Deadzone

by laradjiversion 0.7.4i★ –iRuns on your computer
Registry nameio.github.laradji/deadzone

Install

Overview

Description from the maker (original English)

Local-first MCP server: semantic search over library docs. No cloud, no API key, no telemetry.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

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 server

Open in Cursor

Or copy the settings yourself

Paste 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.

Open in VS Code

Or copy the settings yourself

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.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

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

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account laradji, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7249 servers in this category
  • Published to the registry more recently than 11% of the other 7249 servers in this category
  • Basic checks equal to or better than 100% of the other 7249 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Deadzone safe?

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.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

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).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.7.4) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

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.

         _                _

| | _ __ _ | |_____ _ _ / _ |/ _ \/ _ |/ _` |_ / _ \| '_ \ / _ \ | (_| | __/ (_| | (_| |/ / (_) | | | | / \,_|\_|\,_|\__,_/_\_/|_| |_|\___|

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.


The pitch, in one paragraph

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.


Rules of the deadzone

  1. One binary. deadzone. Subcommands for everything. No pip install, no npm i, no docker compose up.
  2. The index never leaves. Local Turso file. No account. No API key. No egress on the hot path.
  3. Natural language first. Embeddings over cosine. FTS5 is not invited.
  4. The binary is the version. The DB is pinned to the binary. Upgrade the binary, the DB follows; don't, and it won't.
  5. Fail loudly or not at all. DEADZONE_DB_OFFLINE=1 refuses to guess. Verification failures in the scraper drop the doc, not the run.

Install (pick one; they all converge on the same binary)

# macOS Apple Silicon — the one-liner

brew install laradji/deadzone/deadzone

Linux — resolve the latest tag once, then pick a flavor

VERSION=$(curl -fsSL https://api.github.com/repos/laradji/deadzone/releases/latest | grep '"tag_name"' | cut -d'"' -f4) ARCH=amd64 # or arm64

self-mounting AppImage

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

or plain tarball (no FUSE needed)

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.


Run

./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


The two tools

┌─────────────────────────────────────────────────────────────────────┐

│ 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 │ └─────────────────────────────────────────────────────────────────────┘

ArgShapeNotes
querystringMatched semantically. Don't write keywords; write what you want.
lib_id/org/projectOptional filter. Grab one from search_libraries.
version"1.14" or similarOptional pin; requires lib_id. version alone is rejected.
tokensintResponse budget. Default 5000, min 1000, ≈ 4 chars/token.
limitintOn search_libraries — max results. Default 10, max 50.
namestringFree text on search_libraries. Empty returns the most-indexed libs.

Under the hood

  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 ────────┘

LayerChoice
LanguageGo 1.26.2, pinned via mise
StorageTurso local file — native F32_BLOB(N) + vector_distance_cos
Drivertursogo — CGO-free via purego
Embedderhugot → nomic-ai/nomic-embed-text-v1.5, 768-dim, 8192-token ctx (int8 quantized)
RuntimeONNX Runtime — binary CGO-linked at build time; libonnxruntime auto-fetched + SHA256-verified on first launch
Protocolmodelcontextprotocol/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 varEffect
DEADZONE_ORT_LIB_PATHHand-positioned libonnxruntime path. Skips the download.
DEADZONE_ORT_CACHEOverride the ORT library cache dir.
DEADZONE_HUGOT_CACHEOverride the model-weights cache dir.
DEADZONE_DB_CACHEOverride the deadzone.db cache dir.
DEADZONE_DB_OFFLINE=1Refuse any network call. Fails loudly if nothing is cached. Set by default in the container image (which ships deadzone.db baked).
DEADZONE_DB_AUTOUPDATE=0Disable the boot-time DB freshness probe (the probe runs by default; fetch-db always probes regardless of this flag).

Default cache paths per platform:

Platformdeadzone.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.


Add a library

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:

Single-version lib — no versions key, urls used as-is.

  • lib_id: /modelcontextprotocol/go-sdk kind: github-md urls:
  • https://raw.githubusercontent.com/modelcontextprotocol/go-sdk/main/README.md

Only the beginning is shown; read the rest in the repo

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