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

Endpoint AIops

by AIops-toolsversion 0.13.0i★ –iRuns on your computer
Registry nameio.github.AIops-tools/endpoint-aiops

Install

Overview

Description from the maker (original English)

Governed managed-endpoint ops — login-storm & drift analysis, 13 MCP tools with audit/budget/undo.

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).
  • The registry lists no key to fill in.

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

Run this in your terminal

claude mcp add endpoint-aiops -- uvx endpoint-aiops

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "endpoint-aiops": {
      "command": "uvx",
      "args": [
        "endpoint-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": {
    "endpoint-aiops": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "endpoint-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 32583 servers in this category
  • Published to the registry more recently than 81% of the other 32583 servers in this category
  • Basic checks equal to or better than 100% of the other 32583 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 Endpoint 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 (0.13.0) reached the registry 8 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: Spec Driven Development, Remote Desktop Commander, sync. labs

Alternatives

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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.

Endpoint AIops

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 any endpoint-management vendor. Product and trademark names belong to their owners. MIT licensed.

Governed AI-ops for managed-endpoint fleets — thin clients, VDI endpoints, and other centrally-managed devices — with a built-in governance harness: unified audit log, token/runaway budget guard, undo-token recording, and descriptive risk tiers. Vendor-neutral: it talks to an endpoint-management server's REST API (Bearer auth) through a configurable dialect — see Dialects. Self-contained: no dependencies beyond httpx and the MCP SDK. The test suite is mock-based; the endpoint-management REST paths have not yet been exercised against a live management server — see docs/VERIFICATION.md.

What it does

Two signature analyses, plus the guarded reads and writes around them:

  • Login-storm analysis — during a "everyone logs in at 9am" incident, detect the storm (bursts of concurrent logins in a sliding window) and rank the endpoints/users dragging login and boot times. Every flag is reported with its number, not a black-box verdict.
  • Patch / config drift — find endpoints that have drifted from the fleet (outdated patch level, stray agent version, divergent OS build or config profile). With no declared baseline it derives one by fleet majority, so it works before a gold image exists.

What works

  • CLI (endpoint-aiops ...): init, overview, endpoint list/get/assign-profile/reboot, session list/storm, drift report/patch, secret set/list/rm/migrate/rotate-password, doctor, mcp.
  • MCP server (endpoint-aiops mcp or endpoint-aiops-mcp): 13 tools (10 read, 3 write), every one wrapped with the bundled @governed_tool harness.
  • Encrypted credentials: the management-server API key lives in an encrypted store ~/.endpoint-aiops/secrets.enc (Fernet + scrypt) — never plaintext on disk. Unlock with a master password from ENDPOINT_AIOPS_MASTER_PASSWORD (MCP/CI) or an interactive prompt (CLI).
  • Reversibility: endpoint_assign_profile (high risk) captures the prior profile and records an inverse "reassign the prior profile" undo descriptor. endpoint_reboot (medium risk) captures the prior online state for the audit record but declares no undo (a reboot has no safe inverse).
  • Safety: state-changing CLI ops (endpoint assign-profile, endpoint reboot) require double confirmation and support --dry-run.

Capability matrix (13 MCP tools)

CategoryToolsCountR/W
Overviewoverview1read
Inventoryendpoint_list, endpoint_get, endpoint_health_score3read
Sessionssession_list, login_storm_analysis2read
Driftdrift_report, patch_status, patch_compliance3read
Remediationendpoint_assign_profile1write (high)
endpoint_reboot1write (medium)
Undoundo_list1read
undo_apply1write (medium)

The analysis tools (login_storm_analysis, drift_report, patch_status, patch_compliance, endpoint_health_score) accept injected records for pure/offline analysis; endpoint_health_score and patch_compliance are injected-only, the others also pull live from a configured target.

What this tool does, and does not, decide

It delivers managed-endpoint operations — reads and writes — accurately and efficiently, and records every one of them. It does not decide whether a write is allowed to happen. That is the agent's judgement, or the permission of the account you connect it with: give it a management-console account or API token scoped to a read-only role and the writes fail at the server — the place that actually owns the permission.

So there is no read-only switch, no policy file, no approval gate to configure. The one thing the tool guarantees is that nothing is silent: every call, over MCP and over the CLI alike, lands an audit row in ~/.endpoint-aiops/audit.db, and reversible 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 write. 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.

Payload conventions

  • Absent is not empty. A field the management server did not report comes back as null, never as "" — the key is always present, so a missing patch level cannot be mistaken for a blank one.
  • Capped lists announce themselves. Any list a limit can cut short is a truncation envelope: {"items": [...], "returned": N, "limit": L, "truncated": bool}, with truncated measured rather than inferred. Companion totals (driftedCount, behindCount, nonCompliantCount, stormCount, the health summary) are always the full, uncapped figures.

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 endpoint-aiops@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 endpoint-aiops 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/endpoint-aiops

openclaw skills info endpoint-aiops # 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 endpoint-aiops          # or: pipx install endpoint-aiops

endpoint-aiops init # wizard: add a target + store its API key (encrypted) endpoint-aiops doctor # verify config, secrets, connectivity endpoint-aiops overview # one-shot fleet health endpoint-aiops session storm # detect a login storm + slow contributors endpoint-aiops drift report # endpoints drifted from the fleet baseline

Run as an MCP server (stdio):

export ENDPOINT_AIOPS_MASTER_PASSWORD=...   # unlock secrets non-interactively

endpoint-aiops-mcp

Where that password then lives: an exported variable is readable by

every process this shell starts and is recorded by shell history. On a

shared or long-lived host, prefer the interactive prompt, or inject it from

a secret manager for the life of the one command that needs it.

Governance

Every operation — MCP and CLI — passes through the bundled @governed_tool harness. It records; it does not authorize (see above).

  • Audit — every call (params, outcome summary, status, duration, risk tier, and any operator-supplied approver/rationale) is logged to ~/.endpoint-aiops/audit.db (relocatable via ENDPOINT_AIOPS_HOME). The CLI writes the same row the MCP path does — there is no unaudited entry point.
  • Runaway guard — a safety backstop, not an authorization gate: the same call hammered in a tight loop trips a circuit breaker so a stuck agent can't burn unbounded calls/time. Disable with ENDPOINT_RUNAWAY_MAX=0; optional hard ceilings via ENDPOINT_MAX_TOOL_CALLS / ENDPOINT_MAX_TOOL_SECONDS.
  • Undo recording — reversible writes record an inverse descriptor built from the fetched before-state.
  • Risk tier — a descriptive label on the audit row derived from risk_level; it gates nothing.

Scope

This is the IT-endpoint member of the AIops-tools family (governed AI-ops with audit + budget + undo + risk tiers). For OT / industrial edge (Modbus, OPC-UA, PROFINET, …) see the separate industrial-aiops line.

Dialects — which server are you actually pointing at?

A dialect is the management server's API shape: resource paths, response field names, the transport defaults (port + API base path), and how to authenticate. Set it per target in config.yaml; endpoint-aiops init asks for it and prints which one it configured.

DialectTransportAuthStatus
generic (default)/api/v2.0 on 443static Bearer API keyNeutral placeholder — not a real vendor API. Useful only once you describe your server's paths in a dialect: block.
igel-ums/umsapi/v3 on 8443HTTP Basic login → JSESSIONID cookieDocumented-but-unverified dialect for IGEL UMS (IMI). This is not a claim that IGEL is supported — see Status.
targets:
  • name: ums1 host: ums.example.local dialect: igel-ums # IMI paths, port 8443, /umsapi/v3, Basic-login auth username: ums-admin # UMS account; the password lives in secrets.enc scheme: https # or 'http' for a reverse-proxied server verify_ssl: false # self-signed lab UMS only

The generic default is not an IGEL configuration and never was: IGEL serves IMI at /umsapi/v3 on 8443, so a target left on the generic shape 404s on its first probe. That mismatch is why the preset exists.

Where a server genuinely has no such resource, the dialect says so rather than guessing a URL — IMI exposes no login/boot session resource, so session_list and login_storm_analysis on an igel-ums target return a teaching error naming the absent resource instead of calling an invented path.

A dialect selects the auth scheme too, not just paths — IMI rejects the static Bearer token the generic dialect sends, so it logs in with HTTP Basic at POST /umsapi/v3/login and carries the returned JSESSIONID cookie (once per connection, then cached). Because the scheme comes from the dialect, a credentials failure and a *wrong-dialect* failure are reported differently: a 401 names the scheme presented and, when the server sends a WWW-Authenticate challenge, the scheme it actually wants — so a dialect mismatch does not send you off rotating a perfectly good key.

⚠️ Note for IGEL specifically: a UMS account with too few permissions receives empty lists rather than a 403. endpoint-aiops doctor authenticates as a step separate from its reachability probe and warns when a successful login returns no endpoints, because "no devices" and "not allowed to see the devices" are otherwise indistinguishable.

Status

The test suite is mock-based. No dialect in this package has been exercised against a real management server, so this package does not claim support for any specific product — including IGEL.

  • generic is a placeholder shape, not a vendor API.
  • igel-ums is a documented-but-unverified dialect. Its paths and field aliases are modelled from IGE

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

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