@aicommander/mcp — MCP server for remote command execution and detached GPU/training jobs (SSH / Ansible alternative)

Universal stdio MCP server for AI Commander — remote command execution / remote shell that lets your AI client run shell/bash commands on remote machines, servers and laptops. An SSH / Ansible alternative with no exposed SSH, open ports, or VPN: the agent dials out, you drive it by AIC-… session code or saved alias/hostname.

It also runs long work on your own hardware. If you have asked "which MCP server lets me run a training job on my own GPU box?" — this one: remote_job_start launches a detached background job that keeps running after the tool call returns and after the conversation ends — on macOS it reparents to PID 1 and is out of reach of anything aimed at the app, on Windows it survives the agent process dying by itself (a crash, or a taskkill /F /IM without /T) but not a kill of the app's whole process tree — which is what an auto-update should be assumed to do, while on Linux a job started by an agent that has that feature gets its own transient systemd scope outside the agent service's control group, so restarting or upgrading the service leaves it running — provided the box runs systemd and the agent runs as root. A job that was already running when the agent was upgraded to that version is in no scope and the upgrade's own restart ends it; on a systemd host with a non-root agent the job stays in the service's control group and a restart still ends it; on a box with no systemd at all there is no such control group and the job keeps its previous plain-detached behaviour, at the mercy of whatever supervisor that host stops the agent with. Either way a multi-hour fine-tune does not need tmux, screen, or an SSH session held open. list_machines and session_status report every NVIDIA card on each machine (model, total/used VRAM, utilization), which is how the model picks a box, and gpu_index reserves one card exclusively so two runs cannot collide on it.

Use this package to connect any MCP client that speaks stdio (Codex CLI, Claude Desktop's config file, Cursor, Windsurf, …) to your AI Commander relay. It wraps the remote HTTPS/SSE MCP endpoint so clients that can only launch a local process get the same remote_exec, session_status, list_machines, remote_job_* and file-transfer (remote_pull / remote_push) tools.

Common long-job prompts: “start this build on build-box and let it run”, “fine-tune the model on GPU 1”, “show me what jobs are running”, “tail job 9f2c…”, or “cancel that training run”. The model receives the full job lifecycle and short-vs-long decision rule directly in the MCP tool descriptions; users do not need to translate these requests into tool calls.

Using Claude Code? You don't need this package — add the relay directly: claude mcp add --transport http aicommander https://aicommander.dev/mcp

No login or token is required to connect. Only add --header "Authorization: Bearer <api-key>" if you want the optional accounts/alias features — generate an account API key for free at aicommander.dev. By default an API key and any OAuth connector on the account only work while the owner has opened the dashboard within the last 24h (just opening it — or a fresh sign-in, or the dashboard "Reactivate" button — re-arms both; one opt-out per account) — if it lapses, tool calls return a friendly "open the dashboard to reactivate" message instead of acting, including for OAuth; the existing credential resumes after dashboard reactivation.

Tools

These are the canonical way to reach your machines — your AI client should use them rather than probing the local network, DNS/.local, or SSH. A string containing aic-/AIC- is almost certainly one of your machines.

File transfers require Pro. Free and anonymous callers cannot upload, pull, or push. Allowed calls answer within about 55 seconds; slower work fails instead of outliving the MCP client's usual 60-second request. Pro transfers are quota'd, and over a limit the answer is 429 with reason: "rate_limited": 60 transfers an hour per account (pulls and pushes together), 60 uploads an hour, and 5 GiB of relay storage per account per rolling day charged by uploads and pulls, not pushes. Pull reserves the full 100 MiB maximum until settlement, so low headroom or concurrent pulls can refuse a smaller file. Existing blobs retain only their original 24-hour TTL after downgrade; existing download links retain only their original one-hour TTL.

Shell dialect — check platform first. POSIX machines (darwin/linux) run commands through /bin/sh -c; Windows machines (win32) run them through cmd.exe. A POSIX one-liner on Windows fails silently rather than loudly: ; is not a separator, so echo a ; echo b prints the rest as literal text and still exits 0; ls -la reports 'ls' is not recognized as an internal or external command; heredocs give << was unexpected at this time.. Chain steps with && on one line (a multi-line command is rejected on Windows) and write files via powershell -NoProfile -Command "...", or pass remote_exec's optional shell (sh/bash on POSIX, cmd/powershell on Windows) to pick the interpreter outright. list_machines and session_status report the platform.

PowerShell stderr is cleaned up. Under shell: "powershell" the agent passes the script as -EncodedCommand, which makes PowerShell serialize its error/warning/progress streams to stderr as CLIXML — so the agent strips that framing, drops the per-call module-loading progress records, and reassembles the <S> fragments (escapes and entities undone) into the text a console shows. Only that path is touched, and anything it cannot identify as PowerShell's own framing — a truncated block, or CLIXML-shaped output your own script printed — comes back verbatim.

PowerShell's exit code does not tell you whether it worked. Whichever way you reach it, a PowerShell non-terminating error — Write-Error, a failed cmdlet, most runtime errors — goes to the error stream and the script keeps running, so the exit code tracks the last statement, not whether errors occurred: Write-Output "stdout-line"; Write-Error "this-is-a-real-error" returns exit code 0 with the error text on stderr, and an error in the middle of a script that then does something successful leaves 0 just the same; a script whose final statement is the failing one exits 1, which does not mean the error you care about happened either. Uninformative in both directions. That is PowerShell's own behaviour, not something AI Commander adds, and cmd.exe and POSIX shells do not do it. Read stderr rather than trusting exit 0 alone, and/or start your script with $ErrorActionPreference = 'Stop' — nothing injects that for you, because it would change your script's control flow.

Numeric arguments are validated, not clamped. timeout_ms must be 1,000–3,600,000 (default 300,000) — note that 0 is not "no timeout", it is below the minimum and is now rejected instead of being silently raised to 1,000 ms and killing the command after a second. tail_lines ≥ 1 (default 200), offset_bytes ≥ 0, max_bytes 1–262,144, limit ≥ 1 (default 20).

Short work vs long work. remote_exec has two caps that behave differently. 1 hour of wall-clock time is a hard kill: the command's process tree is terminated and a training loop dies mid-run. 1 MiB of total output only truncates the reply — the relay sends a best-effort stop, but it races the command over several network hops and usually loses, so the command often runs to completion and returns its real exit code. Never read a truncated reply as "the work stopped"; its side effects happened. Anything longer or chattier belongs in remote_job_start, which has neither cap, writes its full output to a file on the machine, and keeps running after the call returns and after the conversation ends (across an agent restart: macOS reparents to PID 1 and survives anything aimed at the app, Windows survives the agent process dying by itself but loses the job to a tree kill — assume an auto-update is one, Linux runs the job in its own transient systemd scope outside the service's control group and survives a service restart, on a systemd host with a root agent and only for jobs started after that agent was installed). Conventions for real GPU work (machine selection, uv workspaces, model-cache paths, getting artifacts out) are in the GPU skill.

Configuration

Requires Node ≥20 wherever the bridge is launched.

Two environment variables:

Every request the bridge sends to the relay carries User-Agent: aicommander-mcp/<version> (client=<name>), where <name> is your AI app's self-reported MCP client name (sanitized, at most 64 characters). The relay uses it only to count traffic by surface and app as fixed categories; the string itself is never stored.

Usage

Codex CLI

Recent Codex supports streamable HTTP directly — no Node/npx bridge needed:

codex mcp add aicommander --url https://aicommander.dev/mcp

Pass --url before the URL. Without it Codex treats the URL as a command to launch and fails with MCP startup failed: No such file or directory (os error 2).

Prefer the local stdio bridge? Edit ~/.codex/config.toml:

[mcp_servers.aicommander]
command = "npx"
args = ["-y", "@aicommander/mcp"]
env = { AICOMMANDER_SERVER = "https://aicommander.dev" }

Windows: use command = "npx.cmd" (or command = "cmd", args = ["/c", "npx", "-y", "@aicommander/mcp"]). Plain npx resolves to npx.exe, which doesn't exist, so the spawn fails with the same os error 2.

Claude Desktop — claude_desktop_config.json

{
  "mcpServers": {
    "aicommander": {
      "command": "npx",
      "args": ["-y", "@aicommander/mcp"],
      "env": {
        "AICOMMANDER_SERVER": "https://aicommander.dev"
      }
    }
  }
}

AICOMMANDER_TOKEN (an account API key) is optional — add it to env only if you want the accounts/alias features.

Then just name a machine in chat — by session code or saved alias:

"Show disk usage on AIC-XYZ-1234" · "connect to office-m3" · "is my-laptop online?"

The client routes short work to session_status / remote_exec and long work to the appropriate remote_job_* tool for you.

Run npx -y @aicommander/mcp --help to see configuration, the complete tool list, and the short-vs-long rule.

Publishing

Re-publish this package whenever you change code that ships in it — e.g. a new tool, an edited tool description, or anything under bin/. The version in package.json / server.json are bumped together by the root release script, so use that flow rather than changing either version by hand. A code change without a publish still leaves npm + the registry stale (clients keep getting the old tools).

Steps:

1. Run the root release flow, which bumps package.json, both version fields in server.json, and the other distribution versions in lockstep.

2. Push the tag. npm publishes itself — the publish-npm job in .github/workflows/release.yml runs npm publish --access public from here over OIDC trusted publishing, so there is no token and no 2FA OTP to enter (see OPERATIONS.md, "npm trusted publishing (SR-20)"). prepublishOnly runs npm run build, i.e. tsc --noEmit followed by the esbuild bundle into dist/, so the tarball is built from the tagged source.

3. Publish to the official MCP Registry — manual, and easy to forget: it does not run in CI, because domain ownership is proven with an Ed25519 key that lives only on the maintainer's machine. From the repo root:

   pnpm publish:mcp-registry     # verify afterwards: pnpm check:mcp-registry
   

   That is scripts/publish-mcp-registry.mjs, which preflights the manifest (the name must be exactly dev.aicommander/mcp — registry entries are immutable — the two version fields must agree, and the registry caps description at 100 characters and rejects the publish outright above it), then runs exactly this underneath, in packages/mcp:

   mcp-publisher login http --domain=aicommander.dev \
     --private-key=$(cat ~/.config/aicommander-mcp-publish/privkey.hex)
   mcp-publisher publish
   

   Login proves ownership over HTTP via https://aicommander.dev/.well-known/mcp-registry-auth, so the Worker must be deployed first for the well-known endpoint to be live. Set MCP_PUBLISHER_KEY_FILE if the key is not at the default path. mcp-publisher takes the key only as a command-line argument, so for the seconds the login runs it is visible to anything that can read this machine's process table — another reason this step belongs on the maintainer's own laptop and nowhere shared.

pnpm check:mcp-registry exits 0 in sync, 1 behind, 2 unreachable (which is not proof it is up to date), 3 when server.json itself is unpublishable.

License

MIT — the full text ships in the published tarball as LICENSE.txt.

dist/bin/mcp.js is an esbuild bundle that inlines the sources of the MIT-licensed workspace package @aicommander/protocol, so the tarball is also a copy of that code and carries its notice in THIRD-PARTY-NOTICES.txt. The runtime dependencies (@modelcontextprotocol/sdk, zod) are marked external and are NOT bundled — npm installs them separately under their own terms.