# stack-guardian

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

A health doctor for a home AI stack. It collects a snapshot of your nodes (Ollama endpoints, loaded models, NVIDIA
GPUs, tailnet peers), runs small Python rules against it, remembers the last runs, and reports findings with a
confidence grade: one sample is at most `LIKELY`, the same finding twice in a row is `VERIFIED`. A finding can carry a
typed fix that you run on purpose, never automatically.

Status: **0.1.0, first release.** Standard library only, Python 3.11+.

## Install

```
pip install stack-guardian
```

This gives you three commands: `stack-guardian` (the CLI), `stack-guardian-serve` and `stack-guardian-mcp`.

Try it offline first: the example snapshot makes every shipped rule fire once.

```
curl -LO https://raw.githubusercontent.com/LuminariSoftwares/stack-guardian/main/examples/snapshot.json
curl -LO https://raw.githubusercontent.com/LuminariSoftwares/stack-guardian/main/examples/ctx.json
stack-guardian doctor --snapshot snapshot.json --ctx ctx.json
```

From a clone instead (for writing rules or running the tests):

```
git clone https://github.com/LuminariSoftwares/stack-guardian.git
cd stack-guardian
python -m venv .venv
.venv\Scripts\activate          # Windows;  macOS/Linux: source .venv/bin/activate
pip install -e .
stack-guardian doctor --snapshot examples/snapshot.json --ctx examples/ctx.json
```

## For coding agents

- **MCP server** (any MCP client): `stack-guardian-mcp` speaks MCP over stdio with three read-only tools,
  `stack_health`, `stack_snapshot` and `explain`. In Claude Code:

  ```
  claude mcp add stack-guardian -e STACK_GUARDIAN_NODES=/path/to/nodes.json -- stack-guardian-mcp
  ```

  Other clients: command `stack-guardian-mcp`, args `["--nodes", "/path/to/nodes.json"]`. `stack_health` grades
  against the saved run history without adding to it, and reuses one snapshot for 10 s (`--ttl SECONDS`).
- **DSH plugin** (`npm install dsh-stack-guardian`, source in `dsh/`, Node 20+): adds one line at the start of a DeepSeek Harness session when the doctor reports
  WARN or FAIL, and nothing otherwise. It runs `stack-guardian doctor --json --quiet`, so set `STACK_GUARDIAN_NODES`
  to your nodes file in the harness environment.
- **Watching and scraping:** `stack-guardian doctor --watch 30 --json` prints one JSON object per finding that changes
  state (new, resolved, severity change), so an agent can tail it; `stack-guardian-serve --nodes FILE` also answers
  `GET /metrics` in Prometheus text format.

## What it does

### 1. `doctor` - run the checks

Describe your nodes in a JSON file (see [examples/nodes.json](examples/nodes.json)) and check them live:

```
stack-guardian doctor --nodes examples/nodes.json --ctx examples/ctx.json
```

With neither `--snapshot` nor `--nodes`, `doctor` reads the nodes file named by `$STACK_GUARDIAN_NODES`.

Options: `--checks-dir DIR` (your own rules, default is the shipped `checks.d`), `--ctx FILE` (rule settings such as
the model each node is expected to hold), `--state FILE` (default `$STACK_GUARDIAN_STATE` or
`~/.stack-guardian/state.json`), `--json`, `--quiet` (print nothing when all is OK).

Exit codes: `0` all OK/INFO, `1` a WARN, `2` a FAIL, `3` usage, `4` the doctor itself crashed.

Shipped rules: `node.unreachable`, `ollama.unreachable`, `model.not_loaded`, `model.expiring`, `model.slow_ttft`,
`vram.unattributed`, `gpu.unavailable`, `tailnet.unavailable`, `tailnet.peer_offline`, `tailnet.relayed`. Each raises one finding per affected node. A rule that
raises becomes a `check.crashed` WARN - a buggy rule never reports a broken stack.

**LM Studio, vLLM, llama.cpp server.** Give the node `"kind": "openai"` and list its base URLs under `endpoints`.
It is read from `GET /v1/models` (and `GET /v1/models/<id>` when the server has it) into the same shape as an Ollama
endpoint, so every rule above applies; a model a server lists as unloaded does not count as loaded.

```
{"gpu-box": {"kind": "openai", "endpoints": ["http://127.0.0.1:1234"]}}
```

**`model.expiring`** (WARN): a loaded Ollama model whose `/api/ps` `expires_at` is within 60 s of the snapshot time;
INFO when `/api/ps` is unavailable. Change the window with `"model_expiring_s": 120` in `--ctx`.

**`--watch N`** re-runs the doctor every N seconds and prints only the findings that changed state, each with a
timestamp. Ctrl+C exits `0` with a one-line summary. With `--json`, each change is one JSON object on stdout.

```
stack-guardian doctor --nodes examples/nodes.json --watch 30
```

**`--ttft`** sends a 1-token generation to every loaded model on each Ollama endpoint and records `ttft_ms` on the
model in the snapshot. It costs a generation, so it never runs unless you pass it, and it needs `--nodes`. The probe
keeps each model's unload time as it was. `model.slow_ttft` (INFO) fires above 2000 ms; change it with
`"ttft_slow_ms": 1500` in `--ctx`.

```
stack-guardian doctor --nodes examples/nodes.json --ttft
```

### 2. `doctor --fix ID` - run a finding's typed fix

A rule may attach `fix={"action": ..., "args": {...}}` to a finding. There are exactly three actions and never a raw
shell string:

| action | what it does | needs |
|---|---|---|
| `unload_model` | POST `keep_alive: 0` to an Ollama `/api/generate` | `node`, `url`, `model` |
| `restart_lane` | runs the argv list you declared for that lane | `lane` declared in `--config` |
| `set_env` | writes `NAME=value` to `<state folder>/env` and sets it for the process | `name`, `value` |

```
stack-guardian doctor --checks-dir examples/checks.d --snapshot examples/snapshot.json --fix ollama.stuck --config examples/lanes.json
```

That is a **dry run**: it prints the plan and changes nothing. Add `--apply` to perform it. An applied fix first saves
the snapshot under `<state folder>/snapshots/`, then runs, then appends one JSON line to `<state folder>/fixes.jsonl`
(`--audit FILE` to move it). A `set_env` value is never written to the audit, only its length. `ID` is the finding
code, or `code@node` when several nodes raise the same code. With `--fix`, the exit code is the fix's: `0` plan or
success, `1` refused or failed, `3` usage.

### 3. Other commands

- `stack-guardian snapshot --nodes FILE [--ttft]` prints the raw snapshot the rules read.
- `stack-guardian-serve --nodes FILE [--ctx FILE] [--port 8788]` serves a fresh snapshot at `GET /snapshot` (read-only,
  loopback unless you pass `--host H --allow-remote`), so one machine can ask another for its health.
  no auth: bind to localhost or a tailnet IP only.
- The same server answers `GET /metrics` in Prometheus text format, running the shipped rules on a fresh snapshot:
  `stack_guardian_finding{code,severity,node}` (1 while raised, 0 once resolved),
  `stack_guardian_worst_severity` (0 OK, 1 INFO, 2 WARN, 3 FAIL) and `stack_guardian_last_run_timestamp`.

  ```
  curl http://127.0.0.1:8788/metrics
  ```
- `stack-guardian-mcp --nodes FILE [--ctx FILE] [--state FILE] [--ttl SECONDS]`: see [For coding agents](#for-coding-agents).

## Writing a rule

```python
from stack_guardian import finding as F

def ollama_up(snapshot, ctx):
    if not snapshot["nodes"]["gpu-box"].get("reachable"):
        return F.make_finding("ollama.unreachable", "missing", "FAIL", "gpu-box", "Ollama does not answer")
    return None

CHECKS = [("ollama.up", ollama_up)]
```

Drop the file in a folder and point `--checks-dir` at it. A full example with a fix is in
[examples/checks.d/20_ollama_fix.py](examples/checks.d/20_ollama_fix.py). A rule may also return a list of findings,
one per node.

## Examples

| file | what it is |
|---|---|
| `examples/nodes.json` | three nodes: a local one with GPUs, two remote ones over the tailnet |
| `examples/ctx.json` | rule settings: which model gpu-box-2 should hold |
| `examples/snapshot.json` | an offline snapshot on which every shipped rule fires once, for trying `doctor` and `--fix` |
| `examples/lanes.json` | the lanes `restart_lane` is allowed to run |
| `examples/checks.d/20_ollama_fix.py` | a custom rule whose finding carries a fix |

## Test

```
pip install pytest
python -m pytest -q
```

CI runs this on Ubuntu, Windows and macOS with Python 3.11, plus a privacy scan of every tracked file.

## License

MIT