Container Host AIops MCP server

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

Container Host AIops

Disclaimer: Community-maintained open-source project. Not affiliated with, endorsed by, or sponsored by Docker, Inc., Portainer.io, or any container-platform vendor. "Docker", "Portainer" and all product/trademark names belong to their respective owners. MIT licensed.

Governed AI-ops for non-orchestrator container hosts — the Docker Engine API (over a local unix socket or a TCP host), Portainer (its management API, which also proxies Docker), and Podman (a rootful/rootless service socket speaking the Docker-compatible API plus libpod-native endpoints) — with a built-in governance harness: a unified audit log over both MCP and CLI, a runaway/budget safety guard, and undo-token recording. It records every operation; it does not decide whether a write is permitted — that is the agent's or the account's call. Multi-platform by construction: a registry keyed by platform means a per-target platform field (docker / portainer / podman) selects the API shape, and another host family could be added later without touching the ops/CLI/MCP layers. Exercised against a live Docker Engine 27.5.1 daemon (doctor, overview, the three flagship analyses, and a governed stop_container with audit + undo recorded); the Portainer and Podman API paths are covered by the mock suite only.

What it does

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

• Restart-loop RCA — inspect containers for restart count + exit code, flag the crash-looping ones (restartCount over threshold, or restarting/dead, or a non-zero exit), and map each to a likely cause + action from the exit code (137 OOM/SIGKILL, 143 SIGTERM, 139 segfault, 127 bad entrypoint, …), with a tail of logs. Every ranking carries its numbers, not a black-box verdict.
• Resource-pressure analysis — a one-shot CPU%/memory% sample per running container vs its configured limits, flagging each "near" (≥ 80% of a threshold) or "over", with a recommendation (raise a limit, set a missing memory limit, scale out).
• Image & volume bloat — dangling images + dangling volumes + build cache from system/df, totalled into prune candidates with reclaimable bytes.

What works

Shortened. The full README is on GitHub.