Health doctor for a home AI stack: Ollama nodes, GPUs and tailnet, with graded confidence.
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: 0.2.1. Standard library only, Python 3.11+.
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
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.
stack-guardian-mcp (also stack-guardian mcp) speaks MCP over stdio. It needs a nodes file
(examples/nodes.json); give its path with --nodes FILE or STACK_GUARDIAN_NODES. Optional:
--ctx FILE, --state FILE, --ttl SECONDS (how long one snapshot is reused, default 10).
Run it without installing anything:
uvx stack-guardian mcp --nodes /path/to/nodes.json
Tools, all read-only (none writes or deletes anything):
| tool | argument | what it returns |
|---|---|---|
stack_health | node (optional) | graded findings (FAIL/WARN/INFO/OK) with counts, an overall status and what to try; call this first |
stack_snapshot | node (optional) | the raw snapshot the rules read: Ollama endpoints, loaded models, GPUs, tailnet |
explain | code (required) | what one finding code means, what to try and what evidence would settle it |
Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"stack-guardian": {
"command": "uvx",
"args": ["stack-guardian", "mcp"],
"env": {"STACK_GUARDIAN_NODES": "/path/to/nodes.json"}
}
}
}
Claude Code: claude mcp add stack-guardian -e STACK_GUARDIAN_NODES=/path/to/nodes.json -- uvx stack-guardian mcp.
doctor - run the checksDescribe your nodes in a JSON file (see 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
doctor --fix ID - run a finding's typed fixA 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.
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.
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. A rule may also return a list of findings,
one per node.
| 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 |
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.
MIT. See LICENSE.
Source-derived launch command. Check the maintainer’s required arguments and credentials before running:
uvx stack-guardianMerge this template into ~/Library/Application Support/Claude/claude_desktop_config.json. Keep existing servers. Add any arguments, credentials, and permissions required by the maintainer; this template has not been install-tested.
{
"mcpServers": {
"io-github-luminarisoftwares-stack-guardian": {
"command": "uvx",
"args": [
"stack-guardian"
]
}
}
}Restart Claude Desktop completely for changes to take effect. Confirm the server appears connected in the client’s tool list, then try a read-only example from its documentation.
Claude Desktop setup referenceStack Guardian works with any MCP-compatible client. Copy the config snippet from the Configuration section above and add it to the file shown for your client, then restart the application.
~/Library/Application Support/Claude/claude_desktop_config.jsonRestart Claude Desktop completely for changes to take effect.~/.cursor/mcp.jsonRestart Cursor for changes to take effect..vscode/mcp.jsonReload VS Code window for changes to take effect.~/.codeium/windsurf/mcp_config.jsonRestart Windsurf for changes to take effect..mcp.jsonSave at the project root, then start Claude Code in that project and review the MCP server approval prompt. Keep real credentials out of shared files.