AI Guardian

Name: AI Guardian
Author: AIops-tools

by AIops-toolsversion 0.14.0i★ –iRuns on your computer

Registry nameio.github.AIops-tools/ai-guardian

Install

Overview

Description from the maker (original English)

Governed local-LLM observability: model policy, prompt scanner, capture proxy, 21 tools.

PopNx MCP’s three pillars

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

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

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

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "ai-guardian": {
      "command": "uvx",
      "args": [
        "ai-guardian-aiops"
      ]
    }
  }
}

Run this in your terminal

claude mcp add ai-guardian -- uvx ai-guardian-aiops

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "ai-guardian": {
      "command": "uvx",
      "args": [
        "ai-guardian-aiops"
      ]
    }
  }
}

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": {
    "ai-guardian": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "ai-guardian-aiops"
      ]
    }
  }
}

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 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account AIops-tools, 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 5676 servers in this category
Published to the registry more recently than 82% of the other 5676 servers in this category
Basic checks equal to or better than 100% of the other 5676 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 AI 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 (0.14.0) reached the registry 9 days ago.

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: Context7, Codebase Memory, Exa

Alternatives

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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 yesterday

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 21 days ago

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Exa

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

Runs in the cloud★ 5,213MITUpdated 2 months ago

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.

AI Guardian

Audit availability: Real writes require a committed pending audit row and stop before contacting the platform if storage is unavailable. Reads and previews may continue with a warning; new audit rows store outcome summaries rather than full responses.

Disclaimer: Community-maintained open-source project. Not affiliated with, endorsed by, or sponsored by Ollama, IGEL, or any AI-security vendor. Product and trademark names belong to their owners. MIT licensed.

Governed observability + governance for on-endpoint local LLMs. It lets you observe + audit what your local models are actually fed, and gate what leaves in a prompt — the complement to IGEL AI Armor. AI Armor governs *whether* a local model may run on the endpoint; ai-guardian records *what it did* and gates *what goes into the prompt* (secrets, PII, source, jailbreaks) plus *which model* may serve it. Self-contained: it talks to each runtime's REST API and needs nothing beyond httpx and the MCP SDK. v0.1 provides opt-in route-through content governance, plus a transparent capture proxy for clients that did not opt in.

Supported runtimes

One tool, several local runtimes, selected per target by a runtime field in config.yaml (the init wizard asks). Ollama uses its native API; the other three share one OpenAI-compatible transport (/v1/models + /v1/chat/completions).

Runtime	`runtime`	Default port	List / policy	Scan + route-through guard	Provenance
Ollama	`ollama`	11434	✅	✅	digest (content hash — strong)
llama.cpp (`llama-server`)	`llamacpp`	8080	✅	✅	props — `/props` model path/size → pinnable id
LM Studio	`lmstudio`	1234	✅	✅	id only — weaker; pins report `unverifiable`
vLLM (local single-node)	`vllm`	8000	✅	✅	id only — weaker; pins report `unverifiable`

The allow/deny model policy, the deterministic prompt scanner, the route-through guard (guarded_generate / observe_chat), provenance drift, and doctor work across all runtimes. Model lifecycle writes (pull / remove / unload) are Ollama-only — the OpenAI-compatible servers load a model at startup and expose no lifecycle endpoint, so those writes are refused with a clear message.

Provenance honesty: only Ollama (content digest) and llama.cpp (a /props-derived path/size identity) expose something to pin. LM Studio and vLLM expose only a model id, so a pinned digest is reported unverifiable rather than a false DRIFT.

vLLM here is a LOCAL endpoint-guarding use case. GPU inference-cluster

operations (autoscale, drain, Ray Serve/Jobs, model lifecycle at fleet scale)

belong to a different tool in the line — GPU cluster ops → inference-aiops.

What it does

Ollama persists no queryable prompt/response history — conversational context is client-supplied on every request. So ai-guardian observes on two fronts:

Passive inventory / state auditing — over /api/tags, /api/ps, /api/show, /api/version: what models are installed and running, their VRAM residency, license/params/capabilities, and their provenance digests. Every model is annotated with an allow/deny policy verdict, so shadow (unsanctioned) models show allowed: false.
Opt-in route-through content governance — callers send a prompt *through* ai-guardian (guarded_generate / observe_chat). It scans the text (secrets / PII / source / jailbreak), checks the model against policy, records the interaction to its own usage log (~/.ai-guardian/usage.db), and only then calls Ollama — blocking when the risk band is too high or the model is disallowed. The raw prompt is never stored (only its length + redacted findings).

Now available (ai-guardian proxy serve): a transparent capture proxy that

applies the same scan + model policy + recording to traffic from clients that

never opted in — point them at the proxy instead of the runtime. It inspects

requests and streams responses through untouched, and it is a

chokepoint, not an enforcement boundary: a client that can still reach the

runtime's real port bypasses it entirely. proxy_guidance returns the command

and that caveat; the CLI prints it on every start.

Key features

Deterministic, offline prompt scanner — no I/O, no network, so it is fully testable offline. Flags secrets (AWS AKIA, private-key blocks, GitHub / Slack / OpenAI / Google tokens, JWTs, assigned api_key=…, high-entropy fallback), PII (email, US SSN, credit card with a Luhn check), source/config-leak heuristics, and jailbreak / prompt-injection signatures — rolled up into a weighted risk band (low / medium / high / critical; any critical dominates). Findings are redacted — the scanner never re-emits the secret it caught.
Model allow/deny policy (shell-glob patterns) so shadow / unsanctioned models surface as allowed: false, plus provenance digest pinning to flag a model whose digest drifted (re-pulled / tampered).
Route-through guard — guarded_generate / observe_chat scan + policy-gate
record + run-if-allowed, blocking on risk-band >= block_threshold (default high) or a disallowed model.
Vendored governance harness — audit log, token/runaway budget guard, descriptive risk tiers, and undo-token recording, bundled in the package (no external dependency).
Highly self-testable — Ollama is free + local for the API parts; the scanner, policy, and risk-band are pure deterministic offline logic.

What this tool does, and does not, decide

It delivers local-LLM observability and operations — reads and writes — accurately, and records every one of them. It does not decide whether a write to the model estate is allowed to happen. That is the agent's judgement, or the permission of the host and account you run it under: point it at a runtime the account cannot administer — an Ollama daemon whose model store the user can't modify, or an endpoint the agent reaches read-only — and the writes fail at the runtime, the place that actually owns the permission. Simplest of all, hand the connecting agent only the scan/observe tools.

So the harness has no read-only switch, no deny-rules file, and no approval gate to configure. (Content governance is a separate, product-level thing that stays: the model allow/deny policy and the guarded_generate block threshold still scan and gate what a model is asked to do.) The one thing the harness guarantees is that nothing is silent: every call, over MCP and over the CLI alike, lands an audit row in ~/.ai-guardian/audit.db, and destructive writes still capture their before-state and record an inverse where one exists.

Each tool declares a risk_level, kept in agreement with its [READ]/[WRITE]

documentation tag by a test, and carried into the audit row as a descriptive

tier — so a reviewer can see at a glance that a row was a high-risk delete. It

is a label, not a gate.

Running a smaller / local model? See agent-guardrails.md — it lists the guardrails this tool now enforces for you (so you don't spend prompt budget restating them) and gives a ready-made system prompt for what's left.

Capability matrix (21 MCP tools)

Reads (11)

Tool	Risk	What it returns
`list_models`	low	installed models, each with the allow/deny verdict (shadow → `allowed:false`)
`running_models`	low	loaded models: VRAM footprint + residency expiry
`model_details`	low	license / parameters / capabilities for one model
`server_status`	low	Ollama reachability + version
`vram_usage`	low	total VRAM used by loaded models; flag over-budget
`policy_view`	low	current allow/deny policy + provenance digest pins
`model_provenance`	low	each installed digest vs its pin; flag drift
`scan_prompt`	low	pure text scan → findings + weighted risk band (no model call)
`usage_events`	low	query the observed-usage log
`anomaly_report`	low	rollup: shadow models, digest drift, high-risk + blocked prompts
`proxy_guidance`	low	the `proxy serve` command + the client change, and the explicit caveat that the proxy is a chokepoint, not an enforcement boundary; writes nothing, starts nothing

Writes (8)

Tool	Risk	Undo / safety
`pull_model`	medium	refused if it violates policy
`remove_model`	high	dry-run + undo (re-pull)
`unload_model`	medium	evict from VRAM (`keep_alive:0`)
`set_model_allowlist`	medium	undo → prior allowlist
`set_model_denylist`	medium	undo → prior denylist
`pin_model_digest`	medium	pin a model's expected provenance digest
`guarded_generate`	medium	the route-through guard: scan + policy-gate + record + run-if-allowed
`observe_chat`	medium	same, for `/api/chat` messages

Undo (2)

Tool	Risk	What it does
`undo_list`	low	list recorded undo tokens
`undo_apply`	medium	replay a recorded inverse descriptor

Risk-band gating: guarded_generate / observe_chat block when the prompt's risk band >= block_threshold (default high) or the model is disallowed. Blocked calls never reach Ollama and are recorded as blocked in the usage log.

Quick start

As a Claude Code plugin

One install gives an agent both the skill and the MCP server:

/plugin marketplace add AIops-tools/marketplace

/plugin install ai-guardian@aiops-tools

The MCP server is fetched with uv and pinned to the package version this plugin declares, so an audit row can be traced back to the code that wrote it. Credentials are still configured with ai-guardian init — see below.

As an OpenClaw plugin

The same bundle is published on ClawHub, where one install delivers the skill and its MCP server together:

openclaw plugins install clawhub:@zw008/ai-guardian

openclaw skills info ai-guardian # expect: Visible to model: yes

Restart the OpenClaw gateway afterwards so it loads the plugin. The MCP server is fetched with uv, pinned to this exact release, so uvx has to be on PATH — without it the skill still installs but reports Visible to model: no. Credentials are configured exactly as below.

As a CLI or standalone MCP server

uv tool install ai-guardian-aiops          # or: pipx install ai-guardian-aiops

ai-guardian doctor # Ollama reachability + policy summary (works zero-config) ai-guardian overview # models installed/running, shadow count, usage stats ai-guardian model list # installed models with allow/deny verdicts ai-guardian guard scan "my key is [PopNx MCP ตัดค่าที่หน้าตาเหมือนกุญแจออก]" # deterministic scan → risk band

The command and the package have different names. The CLI, the skill and

this repo are all ai-guardian; the PyPI distribution is `ai-guardian-aiops`

because ai-guardian was already taken. Both spellings below are correct and

intentional — install ai-guardian-aiops, then run ai-guardian.

Route a prompt through the guard (scan + policy-gate + record + run-if-allowed) via MCP:

guarded_generate(model="llama3.2:3b", prompt="…", block_threshold="high")

Run as an MCP server (stdio) — the full 21-tool surface; the CLI is a convenience subset:

export AI_GUARDIAN_AIOPS_MASTER_PASSWORD=...   # only if a target has a stored token

ai-guardian mcp # or: ai-guardian-mcp

**Where that pas

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

AI Guardian

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

AI Guardian

Supported runtimes

What it does

Key features

What this tool does, and does not, decide

Capability matrix (21 MCP tools)

Reads (11)

Writes (8)

Undo (2)

Quick start

As a Claude Code plugin

As an OpenClaw plugin

As a CLI or standalone MCP server