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Code Guardian

by LuminariSoftwaresversion 0.6.0i★ –iRuns on your computer
Registry nameio.github.LuminariSoftwares/code-guardian

Install

Overview

Description from the maker (original English)

Safe to run? Will it work? Complete? An A-F grade for any repo, plus a verify loop. Never runs it.

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: Other

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).
  • You fill in yourself: CODE_GUARDIAN_LLM_URL, CODE_GUARDIAN_LLM_MODEL

You need

  • PyPIPython’s uv installed (for the uvx command)

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": {
    "code-guardian": {
      "command": "uvx",
      "args": [
        "code-guardian-mcp",
        "mcp"
      ],
      "env": {
        "CODE_GUARDIAN_LLM_URL": "your-value",
        "CODE_GUARDIAN_LLM_MODEL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add code-guardian --env CODE_GUARDIAN_LLM_URL=your-value --env CODE_GUARDIAN_LLM_MODEL=your-value -- uvx code-guardian-mcp mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "code-guardian": {
      "command": "uvx",
      "args": [
        "code-guardian-mcp",
        "mcp"
      ],
      "env": {
        "CODE_GUARDIAN_LLM_URL": "your-value",
        "CODE_GUARDIAN_LLM_MODEL": "your-value"
      }
    }
  }
}

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": {
    "code-guardian": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "code-guardian-mcp",
        "mcp"
      ],
      "env": {
        "CODE_GUARDIAN_LLM_URL": "your-value",
        "CODE_GUARDIAN_LLM_MODEL": "your-value"
      }
    }
  }
}

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.

Values you fill in

  • CODE_GUARDIAN_LLM_URL env
    Optional: local model for the explain tool (Ollama /api/chat or an OpenAI-compatible /v1/chat/completions URL)
  • CODE_GUARDIAN_LLM_MODEL env
    Optional: model name for the explain tool

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 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account LuminariSoftwares, 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 Otheri

  • More stars than 0% of the other 32505 servers in this category
  • Published to the registry more recently than 96% of the other 32505 servers in this category
  • Basic checks equal to or better than 100% of the other 32505 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 Code Guardian 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 (0.6.0) reached the registry yesterday.

Which apps can use it?

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

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, topo^alpin

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday
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by topoalpin.app

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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.

code-guardian

Status: active development -- changelog

mcp-name: io.github.LuminariSoftwares/code-guardian

0 FAIL findings on 8 of 8 clean, well-known repositories, scanned without running them.

A deterministic A-F grade for any repo -- is it safe to run, will it work, is it complete -- for people about to run code they did not write.

Quick start (60 seconds)

From a clean Python 3.11+ environment (no other dependencies):

pip install code-guardian-mcp

code-guardian scan /home/user/project # the grade, every finding with file:line; exit 0, 1 or 3 code-guardian plan /home/user/project --top 3 # the three fixes worth the most Overall points

Success looks like a first line code-guardian 0.6.0 scan of , then a status: COMPLETE line and four score lines. On a one-file project it takes well under a second. To give the same tools to an AI client, add the MCP server (see MCP server for Claude Desktop and other clients):

claude mcp add code-guardian -- uvx code-guardian-mcp mcp

Proof

Run on 2026-11-05 from a fresh clone of the v0.6.0 tag, with code-guardian-mcp==0.6.0 installed from PyPI:

python tools/calibrate.py

Result: 0 FAIL findings on 8 pinned repositories (5 well-known Python projects and 3 sibling guardians).

python -m pytest -q

1386 passed, 82 skipped in 147.56s (0:02:27)

Caveat: this is static analysis. A grade A is "nothing the scanners recognise", not "proven safe". Python is analysed in full, JavaScript and TypeScript at token level, and every other language gets the secret scan and the repo-hygiene checks only.

Before you run a repository you did not write, or merge a change an AI assistant made, you want to know whether it is dangerous, whether it is broken and what is missing. code-guardian answers that from the source alone, in one command that tells you three things about any repo without running it:

  • Safe: is it safe to run?
  • Works: will it work?
  • Complete: does it have what it needs?

Each is a score out of 100 (higher is better), combined into an Overall score and an A–F grade. Scores come only from deterministic checks with fixed weights: same repo, same version, same output, every time. On top of that:

  • Orphan code: functions, classes and modules nothing reachable from the project's entry points uses, judged against a small goal contract.
  • An optional plain-English explanation from a local model, which never adds, removes or rescores a finding.
  • A verify loop for AI-assisted changes: run it before and after every change to learn what the change fixed, what it broke, what to fix next, and whether the scan itself can be trusted (see The verify loop).

It parses the code (ast for Python; a small lexer for JavaScript and TypeScript; json, tomllib and configparser for manifests; regular expressions for secrets) and never executes, imports or installs it. A scan makes no network calls. Python 3.11+, standard library only.

One of four free, MIT-licensed guardians for people running local models, each answering one question from the outside: tool-guardian (what do my tools cost the window?), context-guardian (will this session hit the limit?), stack-guardian (is my stack healthy?), code-guardian (is this code safe to run?).

Who it is for. Anyone running local AI models, or code an AI assistant wrote, on their own machine: you cloned a repository, or an assistant just rewrote a module, and you want a verdict before you run it. It is the fourth of four free, MIT-licensed guardians, each answering one question about a local setup from the outside:

guardianthe question it answers
code-guardian (this one)is this codebase safe to run, will it work, does it have what it needs?
stack-guardianis my home AI stack healthy — nodes, loaded models, GPUs, tailnet peers?
context-guardianwill this session hit the model's context limit and die?
tool-guardianwhat are my MCP servers' tool definitions costing the context window?

Install it as `code-guardian-mcp`; code-guardian on PyPI is an unrelated project by someone else. You get a CLI, a GitHub Action, a pre-commit hook and an MCP server. The MCP side is a hand-written stdio server speaking MCP protocol 2024-11-05 — no SDK, no dependencies (see ADR 0003) — and it is stdio only: to reach it over a network, put an authenticating proxy in front of it rather than exposing the process. One maintainer, best effort: issues and pull requests get a reply when evenings allow, and there is no paid support (CONTRIBUTING.md, SECURITY.md).

What it analyses

Python is analysed in full, JavaScript and TypeScript at token level, and every other language gets the secret scan and the repo-hygiene checks only. Every report prints a coverage: line saying which files were analysed and how, e.g. coverage: 40 Python analysed; 112 JavaScript/TypeScript analysed (token level); 9 .go secret-scan only, and coverage.languages in the JSON gives the same with the checks that ran.

languagefilesdepthwhat runs
Python.pyfullevery Safe, Works and Complete check, from the parse tree (ast)
JavaScript / TypeScript.js .mjs .cjs .jsx .ts .tsxtokensthe secret scan, the Safe checks and the Works checks marked JavaScript / TypeScript in the tables below, run on a token list
everything elseGo, Rust, shell, .vue, .svelte and the restsecret scan onlykey shapes, manifest and install-hook reading (package.json, setup.py, pyproject.toml, ...) and the Complete checks (licence, README, CI, .env.example)

What "token level" means. A lexer cuts each JavaScript or TypeScript file into tokens, telling strings, template literals, comments, regular expressions and JSX text from code, so a sink named in a string or a comment is never a finding. It is a lexer, not a parser: a check reads call sites, imports, assignments and object-literal properties off the tokens, one statement deep, and follows nothing between statements or functions. The checks are exec.dynamic, exec.shell, deser.unsafe, net.egress, net.bind_all, tls.off, obfuscation, fs.outside, privacy.pii_log and dom.html_sink (Safe) and works.syntax, works.import_unresolved, works.relative_missing, works.entry_missing and works.tests_missing (Works). Not ported from the Python side: privacy.pii_prompt, works.test_no_assert, works.todo_stub, works.commented_code, works.dead_flag and the orphan (dead code) check; coverage.languages.javascript.not_checked lists them. Nothing is installed, imported or run: no npm, no node.

Generated files. A declaration file (*.d.ts), a minified or bundled file (*.min.js, *.min.mjs, *.bundle.js) and any JavaScript or TypeScript file with a line longer than 2,000 characters in its first 8 KB are counted as generated (files.generated). They get the secret scan and the obfuscation and exec.dynamic token checks only, an exec.dynamic WARN there is reported at INFO, and they get no Works check.

When there is neither Python nor JavaScript / TypeScript source, Works is not scored (n/a (no Python or JavaScript), null in JSON), Safe is labelled partial, Overall comes from the scored dimensions only, and the status is PARTIAL. A repo whose source is mostly in other languages is also PARTIAL with the same scores (rule in Scan status). PARTIAL means "do not read this grade as covering the whole repo"; it never fails --fail-on on its own. What the token level cannot see is in Limitations.

Install

pip install code-guardian-mcp

The distribution is code-guardian-mcp (code-guardian on PyPI is someone else's project); the commands are code-guardian and code-guardian-mcp. To run the MCP server without installing anything: uvx code-guardian-mcp mcp.

From a clone (for the tests):

git clone https://github.com/LuminariSoftwares/code-guardian.git

cd code-guardian python -m venv .venv .venv\Scripts\activate # Windows; macOS/Linux: source .venv/bin/activate pip install -e ".[test]" python -m pytest -q

The [test] extra is pytest, build and jsonschema — the whole suite needs them, and the package's own runtime dependencies stay empty. See CONTRIBUTING.md.

The app

code-guardian ui shows the same scan as a page in your browser, in plain words, for people who use AI to write code but do not read code well. code-guardian ui PATH checks that folder first; code-guardian ui alone opens the page and you pick a folder there. It prints one line, code-guardian ui: open http://127.0.0.1:8765/#t=…, and opens that link in your browser; the part after #t= is a key for this run, so use the printed link, not just the address. The page never changes your files, and the grade and findings are the ones code-guardian scan gives.

Settings

The gear at the top of every screen opens three choices: Theme (Dark, the default, or Light), Text size (Normal, Larger or Largest) and whether the Luminari name shows at the top. Your choices are kept by this browser only, not in a file on the computer and not by code-guardian, so another browser or a private window starts from the defaults. The page never turns light because your computer is set to light; light is only ever your choice.

Sizes

Every screen fits a phone, a narrow side window (about 520 px wide) and a full or wide screen, at all three text sizes, with nothing cut off: on a phone or in a side window the summary becomes one line under the tabs that you tap to open, and the file list on the Code screen becomes a drawer. The phone is shown below; the same screen at the other sizes is result_520x900.png (side window) and result_1280x800.png (full screen).

Every file

The Code screen lists every file the check saw, not only the files with a finding, and gives each file one word. A file with a safety finding is marked High, Medium or Low by its worst one. The filter above the list picks which files you see, each with its count:

  • All files: every file the check saw.
  • Files with findings: files with at least one finding of any kind.
  • No known risks found: a Python fil

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

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