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PopNx MCPMCP servers for people who use AI · explained in plain words

jobd

by musharnaversion 0.5.52i★ –iRuns on your computer
Registry nameio.github.musharna/jobd

Install

Overview

Description from the maker (original English)

Self-hostable GPU-aware job broker: submit, route by VRAM, babysit jobs across machines via MCP.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other
  • You need to fill in: JOBD_API_TOKEN

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: JOBD_URL, JOBD_API_TOKEN

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "jobd": {
      "command": "uvx",
      "args": [
        "jobd"
      ],
      "env": {
        "JOBD_URL": "your-value",
        "JOBD_API_TOKEN": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add jobd --env JOBD_URL=your-value --env JOBD_API_TOKEN=your-value -- uvx jobd

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "jobd": {
      "command": "uvx",
      "args": [
        "jobd"
      ],
      "env": {
        "JOBD_URL": "your-value",
        "JOBD_API_TOKEN": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "jobd": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "jobd"
      ],
      "env": {
        "JOBD_URL": "your-value",
        "JOBD_API_TOKEN": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • JOBD_URL env
    Base URL of the jobd broker the MCP server talks to. Defaults to http://127.0.0.1:8765.
  • JOBD_API_TOKEN secret env
    Bearer token; must match the broker's JOBD_API_TOKEN. Required unless the broker runs with JOBD_ALLOW_NO_AUTH=1.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account musharna, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 18431 servers in this category
  • Published to the registry more recently than 69% of the other 18431 servers in this category
  • Basic checks equal to or better than 100% of the other 18431 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is jobd safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: JOBD_API_TOKEN

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.5.52) reached the registry 19 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Tethered Trust

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated 3 days ago
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Tethered Trust

by tetheredtrust.com

Fit, compatibility and recall checks for AI agents before they buy: verdicts, evidence, receipts.

Runs in the cloudUpdated today
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

jobd

jobd is a job broker with an MCP server that lets AI agents submit, route and babysit jobs on a few machines of your own. Like task-spooler or pueue, but across all your machines on Tailscale or a LAN, routing each job to a worker with the VRAM free, with no cloud, Kubernetes or Slurm.

# from any machine on your tailnet:

job submit --project myproj --gpu --vram-required 16 --wait -- python train.py

→ routed to whichever worker has ≥16 GB VRAM free, streamed back to your terminal

VRAM routing tracks one GPU per host: the worker reports free memory on GPU index 0 only.

Why it exists

  • VRAM-fit routing. Each job goes to a worker whose live free VRAM, RAM, CPUs, capability tags and arch/OS fit it, instead of you guessing which box is free.
  • Preemption with a checkpoint window. A higher-priority job can preempt a running one: SIGTERM, a grace window for the workload to save into its JOBD_CHECKPOINT_DIR, then SIGKILL. A preempted job is not re-run; to resume, submit a new job pointed at the checkpoint (docs/preemption.md).
  • Survives sessions. Jobs live in the broker, not your shell. Submit, close the laptop, check back tomorrow.
  • Agent-native. An MCP server lets an AI agent submit, monitor and babysit jobs as tool calls (benchmark).
  • Yours. One broker process on a machine you own. No accounts, no telemetry, no per-GPU-hour billing. Only the optional self-update scripts go online (runbook).

Why not just use…?

ToolWhat it gives youWhy jobd instead
`nohup` / `tmux` / sshRuns a command on one boxNo queue, VRAM-aware routing, preemption, or record of what ran where
[task-spooler](https://manpages.ubuntu.com/manpages/noble/man1/tsp.1.html)A job queue on one machinejobd queues across all your machines and routes by live VRAM/CPU fit
[Pueue](https://github.com/Nukesor/pueue)A mature single-machine queue daemonPueue's README puts distributed execution out of scope; jobd is that layer, plus GPU awareness
[HyperQueue](https://github.com/It4innovations/hyperqueue)Multi-machine scheduling with HPC roots, single binaryCounts GPUs but doesn't track VRAM; no preemption/checkpoint contract or agent interface
SlurmDatacenter-grade schedulingHeavy to stand up and operate for 2–3 personal boxes
SkyPilot / dstackRuns on clouds and your own machinesSkyPilot's "existing machines" mode installs Kubernetes (k3s) on your boxes; dstack wants Docker and passwordless sudo on every host
ModalServerless GPU computeRuns on Modal's machines, not yours
RayDistributed compute; Ray Jobs runs any shell command on a Ray clusterYou first stand up and run a Ray cluster

Install

pip install jobd               # broker + CLI

pip install "jobd[mcp]" # adds the MCP server pip install "jobd[worker]" # adds the worker daemon (jobd-worker)

Requires Python ≥ 3.11. scripts/install-worker.sh sets up a worker under ~/jobd-worker with its own venv and a generated config.

Quickstart (single host)

# 1. start the broker (binds 127.0.0.1:8765 by default)

JOBD_ALLOW_NO_AUTH=1 jobd # no-auth is fine for a loopback-only broker

2. in another shell, install + start a worker pointed at it

pip install "jobd[worker]" JOBD_URL=http://127.0.0.1:8765 JOBD_WORKER_HOST=local jobd-worker

3. submit a job and wait for it

job submit --project demo --wait -- echo hello job list job logs

For a multi-host deployment (Docker broker, systemd workers, Tailscale binding, shared auth token), see docs/security.md and the templates in docker-compose.yml and scripts/. Adding a worker to a running fleet is one command:

job fleet add user@newbox      # ssh in, install pinned to the broker's version,

wire systemd units + the self-update timer,

verify it registers. job fleet status shows drift.

Day-2 operations (health, draining a worker, upgrades, token rotation, backups) are in docs/runbook.md.

How it works

The broker (FastAPI + SQLite) holds the queue, runs the matcher and resolves per-project priorities. Workers, one per host, poll it, so nothing connects in to a worker: each heartbeats its live capacity, claims jobs it can run, streams logs back and honors preemption. The argv you submit runs as-is (shell=False); job submit --stdin and the MCP jobd_submit tool are the exceptions and run a command string as bash -c . Design notes: docs/ARCHITECTURE.md.

CLI

job submit -p PROJ [options] -- CMD...       # queue a job

job list [--state STATE] [--project P] # queue + recent jobs job status ID | A [--watch] # one job, or an array's aggregate job logs ID [-n BYTES] # tail captured output job wait ID # block until terminal job cancel ID / job preempt ID # stop a job job adopt --pid N -p PROJ [--gpu] # track a process started outside jobd (Linux) job workers # fleet snapshot + health job projects list | set NAME PRI | nudge NAME DELTA job audit [--project P] [--since 24h] # event history

Job arrays and sweeps, --explain dry runs, job adopt, and a pueue/task-spooler command map: docs/cli.md.

MCP / agent integration

jobd-mcp exposes the queue as nine tools: jobd_submit, jobd_status, jobd_logs, jobd_list, jobd_cancel, jobd_preempt, jobd_events, jobd_workers, jobd_worker_delete. docs/agent-cookbook.md walks through babysitting, preemption, sweeps, and asking why a job won't schedule.

For Claude Code:

claude mcp add jobd \

--env JOBD_URL=http://127.0.0.1:8765 \ --env JOBD_API_TOKEN= \ -- jobd-mcp

Any other MCP client:

{

"mcpServers": { "jobd": { "command": "jobd-mcp", "env": { "JOBD_URL": "http://127.0.0.1:8765", "JOBD_API_TOKEN": " " } } } }

JOBD_API_TOKEN must match the broker's token, or every call returns 401. examples/claude-code-hooks/ has optional hooks that nudge (or hard-block) an agent toward submitting heavy commands through jobd.

What the benchmark shows

Claude with the server was compared with Claude using jobd's own job CLI, on 38 questions about a live broker, 3 runs each. With Claude Haiku 5.5 the server got all 38 right in every run and the CLI 29: a pre-registered win (Holm-adjusted McNemar p = 0.008). With Sonnet 5.5 it was 36 against 30 and with Opus 5.5 38 against 37, neither separated. On all three models the server's answers were cheaper and faster; with Sonnet, 0.20 times the cost and 0.17 times the time.

The chart is the Opus 5.5 run (2026-10-18). There the server's median answer cost 0.20 times as much (95% CI 0.15 to 0.28) and took 0.16 times as long (0.13 to 0.20), and it was cheaper and faster on all 38 questions. These were secondary endpoints, measured on an unreleased commit that adds a finished_at window to jobd_list. On an earlier, easier set both setups got 123 of 123.

We wrote the questions and ran the benchmark ourselves; an outside re-run is welcome. Method, controls and limitations: benchmarks/agent_qa; the Haiku and Sonnet runs: 2026-10-20 re-run.

Supported platforms

ComponentLinuxmacOSWindows
Broker (jobd)testedexpectedexpected (WSL recommended)
CLI (job) / MCP (jobd-mcp)testedexpectedexpected
Worker (jobd-worker)testeddegradeduntested

CI runs on Ubuntu with no GPU runner (VRAM paths are tested against mocks), and without a systemd user instance the worker still runs jobs but drops its memory caps, process reaping and preemption guarantees.

Configuration

Security

Run the broker on loopback or a Tailscale tailnet, never a public interface. Three stacked controls:

  1. Bind: JOBD_HOST is 127.0.0.1 by default; set it to a Tailscale address (100.64.0.0/10), never 0.0.0.0. The broker does not enforce this.
  2. Source IP: requests from outside loopback and 100.64.0.0/10 get 403 (JOBD_DISABLE_TAILNET_ACL=1 turns this off).
  3. Token: every hos

Only the beginning is shown; read the rest in the repo

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