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Governed AI-ops for managed-endpoint fleets (thin clients / VDI) — login-storm & drift analysis with built-in audit/budget/undo/risk-tier harness

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Install in Claude Code / Claude Desktop
Method: UVX (Python) · endpoint-aiops
Claude Code CLI
claude mcp add endpoint-aiops -- uvx endpoint-aiops
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "endpoint-aiops": {
      "command": "uvx",
      "args": ["endpoint-aiops"],
      "env": {
        "ENDPOINT_AIOPS_MASTER_PASSWORD": "<endpoint_aiops_master_password>"
      }
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Package name inferred from the repository name. Verify it exists on PyPI, or clone https://github.com/AIops-tools/Endpoint-AIops and follow its README.
Detected environment variables
ENDPOINT_AIOPS_MASTER_PASSWORD
Use cases

MCP Servers overview

<!-- mcp-name: io.github.AIops-tools/endpoint-aiops -->

# Endpoint AIops

> **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](#dialects--which-server-are-you-actually-pointing-at). 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`](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)

| Category | Tools | Count | R/W |
|----------|-------|:-----:|:---:|
| **Overview** | `overview` | 1 | read |
| **Inventory** | `endpoint_list`, `endpoint_get`, `endpoint_health_score` | 3 | read |
| **Sessions** | `session_list`, `login_storm_analysis` | 2 | read |
| **Drift** | `drift_report`, `patch_status`, `patch_compliance` | 3 | read |
| **Remediation** | `endpoint_assign_profile` | 1 | write (high) |
| | `endpoint_reboot` | 1 | write (medium) |
| **Undo** | `undo_list` | 1 | read |
| | `undo_apply` | 1 | write (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](skills/endpoint-aiops/references/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

```bash
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):

```bash
export ENDPOINT_AIOPS_MASTER_PASSWORD=...   # unlock secrets non-interactively
endpoint-aiops-mcp
```

## 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, result, 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.

| Dialect | Transport | Auth | Status |
|---------|-----------|------|--------|
| `generic` (default) | `/api/v2.0` on 443 | static Bearer API key | **Neutral placeholder — not a real vendor API.** Useful only once you describe your server's paths in a `dialect:` block. |
| `igel-ums` | `/umsapi/v3` on **8443** | HTTP Basic login → `JSESSIONID` cookie | **Documented-but-unverified dialect for IGEL UMS (IMI). This is not a claim that IGEL is supported** — see [Status](#status). |

```yaml
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. 
agent-skillsai-opsendpoint-managementgovernancemcpthin-clientvdi

What people ask about Endpoint-AIops

What is AIops-tools/Endpoint-AIops?

+

AIops-tools/Endpoint-AIops is mcp servers for the Claude AI ecosystem. Governed AI-ops for managed-endpoint fleets (thin clients / VDI) — login-storm & drift analysis with built-in audit/budget/undo/risk-tier harness It has 0 GitHub stars and was last updated today.

How do I install Endpoint-AIops?

+

You can install Endpoint-AIops by cloning the repository (https://github.com/AIops-tools/Endpoint-AIops) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is AIops-tools/Endpoint-AIops safe to use?

+

AIops-tools/Endpoint-AIops has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.

Who maintains AIops-tools/Endpoint-AIops?

+

AIops-tools/Endpoint-AIops is maintained by AIops-tools. The last recorded GitHub activity is from today, with 0 open issues.

Are there alternatives to Endpoint-AIops?

+

Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.

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