OT AIops

Name: OT AIops
Author: AIops-tools

by AIops-toolsversion 0.3.0i★ –iRuns on your computer

Registry nameio.github.AIops-tools/ot-aiops

Install

Overview

Description from the maker (original English)

Governed OT tap (57 tools): OPC-UA/Modbus/S7/MC/MTConnect/MQTT/EtherNet-IP/EtherCAT.

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: 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).
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": {
    "ot-aiops": {
      "command": "uvx",
      "args": [
        "ot-aiops"
      ]
    }
  }
}

Run this in your terminal

claude mcp add ot-aiops -- uvx ot-aiops

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "ot-aiops": {
      "command": "uvx",
      "args": [
        "ot-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": {
    "ot-aiops": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "ot-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 7, 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 Otheri

More stars than 0% of the other 32678 servers in this category
Published to the registry more recently than 15% of the other 32678 servers in this category
Basic checks equal to or better than 100% of the other 32678 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 OT AIops 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 (0.3.0) reached the registry 4 months 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: Spec Driven Development, Remote Desktop Commander, Starreel

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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.

Industrial-AIOps

English · 中文

Ask an AI agent why the line stopped — and get an answer that cites its evidence.

A vendor-neutral, read-first data tap for the factory floor. It speaks 15 field protocols, correlates what it reads *across* them, and hands your agent an evidence-cited verdict instead of a guess. Every call is audited, and no reading ever phones home.

pip install "iaiops[opcua]"      # pick your protocol — or [all]

iaiops init # write ~/.iaiops/config.yaml iaiops doctor # check the setup before you trust it

Prefer a container? The published image is cosign-signed and runs non-root. It speaks MCP over stdio, so keep stdin open and mount a volume for the audit store:

cosign verify --key deploy/margo/cosign.pub ghcr.io/industrial-aiops/iaiops:0.32.0-factory

docker run -i --rm -v iaiops-state:/home/iaiops/.iaiops \ ghcr.io/industrial-aiops/iaiops:0.32.0-factory

For a hardened or air-gapped deployment (read-only rootfs, cap_drop: ALL, no-new-privileges, optional on-box LLM) use deploy/margo/compose.yaml and deploy/airgap/. The analysis engine needs no GPU and no model API — it is deterministic; an LLM is optional and only *phrases* the verdict.

What you get


Reads	OPC-UA (+ Historical Access, tag auto-discovery) · Modbus TCP/RTU · S7comm · Mitsubishi MC · Omron FINS · MTConnect · MQTT/Sparkplug B · EtherNet/IP · EtherCAT · PROFINET · SECS/GEM · HART-IP · BACnet/IP · IO-Link · serial ASCII instruments (RS-232/485, serial device servers) — plus read-only REST layers for BAS supervisors (Metasys / Niagara) and Ignition Gateway
Figures out	downtime root cause (the flagship copilot), alarm floods (ISA-18.2), broken dataflows, data trustworthiness, OEE, asset inventory, legacy PLC program explainer (ST/AWL/L5X)
Governs	audit · budget · risk-tier · undo — on every call, through one engine, from both MCP and CLI
Stays yours	no telemetry, no phone-home. Six tools can send data off-box by design (`stream_publish`, `stream_publish_event`, `uns_publish`, `historian_push`, `mqtt_publish`, `rca_narrate`) — `IAIOPS_NO_EGRESS=1` withholds all six for an air-gapped posture

Eleven editions ship in this package — ten per industry (fab · factory · process · building · water · warehouse · clinical · pharma · renewables · plcnext), each adding its own read-only advisory checks, and one cross-protocol: sensor (connect, decode and read a sensor; IAIOPS_MCP=sensor). Substation / utility telecontrol (IEC-104 · DNP3 · IEC-61850) ships separately as iaiops-energy.

Your first five minutes

Four commands. Only one of them touches a device: doctor probes each configured endpoint (iaiops doctor --skip-probe contacts nothing).

pip install "iaiops[modbus]"     # pick the protocol you actually have, or [all]

iaiops onboard status # ← run this first. Contacts NOTHING. iaiops doctor # config, secrets, reachability — and the version iaiops readiness # every scenario, and what each gap needs

onboard status answers the smaller questions you have first: which journey is this site on — reading its devices directly, or subscribing to a UNS broker its data already flows through — which step of it are you on, and what is the one command that advances it? It is derived from your store and config.yaml every time. The one thing it keeps is a namespace audit verdict, and that counts only for the broker and topic filter it was taken on and always shows its age — so edit config.yaml by hand and the answer stays true.

readiness reads your config and local store and answers one question: which scenarios can this site run today, and what does each gap need? Every gap comes with the command that closes it, ranked by how much it unlocks. No agent, no cloud, no account, and nothing on the wire.

Then the path, in the order that matters — survey what is there, take a bounded sample, and only then explain it:

		contacts a device?
Survey	`iaiops scan plan` → `iaiops scan run`	preview sends nothing; the run itemises every packet class it sent
Configure	`iaiops onboard draft` → you merge it into `config.yaml`	no — it reads the stored scan, and writes nothing
Tap	`iaiops collect run line1 --duration 7d`	yes — and it reports what it saw and what it missed
Declare	`iaiops tags export` → a person fills in `role` → `iaiops tags apply --by`	no — the `role` column comes out empty on purpose
Explain	`iaiops oee measure --since … --until …` · `iaiops investigate open` · `iaiops diag rca`	no — all over collected history

See the whole thing run against a real device in about two minutes, including a genuine mid-run outage, with ./demo/oee-line/run_demo.sh — no hardware, no configuration, nothing written outside a temporary directory. demo/oee-line/ explains what each step is for and what the numbers do and do not claim.

Why read-first

OT is exactly where you want an agent on a tight leash. The read paths are the product; the few write paths are OT-dangerous, off by default, and gated by MOC discipline — dry-run, one-shot approval, undo capture, hash-chained audit.

Proving the analysis needs no model

The analysis layers cannot reach a language model. That is a guard, not a slogan: tests/test_brain_is_llm_free.py scans eight packages — brain, discovery, runtime, readiness, collect, knowledge, retain, connectors — for any import that could reach one, and an empty result is the guarantee. A model is used in exactly two places, and neither is load-bearing: rca_narrate rephrases a verdict that was already computed and already cited, and an agent front-end decides *which* tool to call. Remove both and the numbers are the same numbers.

That guard is static — it proves nothing *can* call a model. For a validation team the sentence they are asked to accept is the executed one, so it is executed:

iaiops verify determinism --out determinism-record.json

A pinned in-repo dataset goes through availability, production counts, the Six Big Losses, ISA-18.2 alarm load, control charts, the conservative baseline and the RCA copilot. Each result is canonically encoded and digested; the suite runs twice in this process and once in each of two fresh interpreters started at different `PYTHONHASHSEED` values — the arm that catches a set or dict iteration order reaching a result, which a single run never can. The socket API raises throughout, so a computation that reached for a device or a hostname fails here instead of quietly working on a machine that happened to be online. Afterwards the run is asked what it pulled in: a model library that was *already* loaded (an MCP server holds iaiops.core.llm for the opt-in narration tool) is recorded, not judged — only what the suite itself imported can condemn it.

The record separates result (identical every run — the part to sign) from context (when and where this run happened). Two good runs are not byte-identical records, and someone will diff them, so the halves are named rather than mixed.

This is the form the claim has to take to be usable: not "our model is accurate", which is not evidence in a GxP context, but a test case someone can write into an IQ/OQ protocol — *remove the model, block the network, re-run the standard dataset, compare the hash* — execute, and sign. verify_determinism is the same check from the MCP side; iaiops verify suite lists what it covers without running it.

How far it's actually been verified

Short version: verified against real protocol libraries, containers and in-process servers — not yet against real plant gear. We grade evidence rather than saying "tested", because a real container round-trip and a synthetic fixture are not the same claim.

Rung	Who judges our frames	Protocols at this rung today
2a — real wire to a third-party server	somebody else's implementation of the spec	OPC-UA (Microsoft `opc-plc`; certificate trust enforced both ways) · Modbus TCP (`pymodbus`) · Modbus RTU (`pymodbus` over a `socat` PTY) · MQTT / Sparkplug B (a real `mosquitto` broker) · BACnet/IP (`bacpypes3`, two IPs on one subnet) · MTConnect (the Institute's own `cppagent`) · SECS/GEM (`secsgem` equipment side) · EtherNet/IP (identification)
2b — real wire to a server we wrote from the spec	the third-party client parses our frames	S7comm (`pyS7`) · Mitsubishi MC (`pymcprotocol`) · EtherNet/IP (tag layer — `pycomm3`, all three driver routes) · PROFINET-DCP
2c — real wire, but both ends are ours	nobody independent	Omron FINS · IO-Link · HART-IP (codec itself at rung 1, against `hart-protocol`) · serial ASCII (our own weighing simulator)
mock only — protocol logic exercised, nothing on the wire	—	EtherCAT · BAS (Metasys / Niagara) · Ignition gateway
3 — real physical / vendor device	the device	zero, for every protocol

Each row's detail — and, per protocol, *what is still not covered* — is in docs/VERIFICATION-RECORD.md. That file is the record; this table is its summary, and a test fails if the two disagree.

**Per-protocol evidence — including what each test does *not* cover — is in docs/VERIFICATION-RECORD.md**, one row per protocol, naming the test behind each claim. Every 待核实 is hardware-gated, not forgotten — each one names the equipment that would settle it.

🧪 测试与共创 / Beta testing & co-creation

我们在找现场测试伙伴。 软件里能验证的我们都验证了（真实 in-process 服务器、真实协议库、Docker 容器 loopback）——剩下的 待核实 清单只有真设备能回答：物理 Modbus-RTU（RS-485）、EtherCAT 从站、HART 网关、在线 BACnet 楼宇设备、在线 Metasys/Niagara BAS 控制器、在线 Ignition 网关、国产 PLC（汇川/信捷）、真机 PLCnext、真实变电站 RTU/IED、欧姆龙 FINS 真机、IO-Link 主站。如果你是 OT 工程师、系统集成商或工厂团队，手上有任何这类设备：装上 iaiops，对你的设备跑一遍 iaiops doctor，把结果告诉我们。经你验证的设备会署名写进支持矩阵；现场反馈的问题我们优先分诊；功能可以通过 GitHub Issues/Discussions 直接共创。

We're looking for field-testing partners. Everything software-verifiable has been verified; what's left on the honest 待核实 list only real equipment can answer — physical Modbus-RTU (RS-485), EtherCAT slaves, HART gateways, live BACnet HVAC, live Metasys/Niagara BAS controllers, live Ignition gateway, domestic PLCs (Inovance/Xinje), live PLCnext, substation RTUs/IEDs, live Omron FINS PLCs, IO-Link masters. If you're an OT engineer, integrator, or factory team with access to any of these: install iaiops, run iaiops doctor against your gear, and tell us what happened. Verified-equipment reports get credited in the support matrix, field-reported issues get fast triage, and features are co-designed in the open via GitHub Issues/Discussions.

👉 参与入口 | Start here: [open an issue](https://github.com/industrial-aiops/industrial-aiops/issues/new) with the protocol and device model in the title, or email . Either reaches a person, and a report gets answered against the current release.

Consolidated capability matrix

Protocol	Tool	Operation	R/W	risk_tier	Returns (key fields)
OPC-UA	`opcua_server_info`	server status	R	low	state, product_name, namespaces
OPC-UA	`opcua_browse`	browse node tree	R	low	[{node_id, browse_name, depth}]
OPC-UA	`opcua_read_node`	read one node	R	low	value, datatype, source_timestamp, good
OPC-UA	`opcua_read_many`	batch read	R	low	[{node_id, value, ...}]

| OPC-UA | opcua_subscribe_sample | bounded sa

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

OT AIops

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 Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Starreel

README from the maker

Industrial-AIOps

What you get

Your first five minutes

Why read-first

Proving the analysis needs no model

How far it's actually been verified

🧪 测试与共创 / Beta testing & co-creation

Consolidated capability matrix