backburner-mcp 0.1.1

An MCP server that lets AI agents run long jobs in the background and collect results later

backburner

Put your AI agent's slow work on the back burner. Keep cooking.

backburner is an MCP server that gives any AI assistant (Claude, and any other MCP client) the ability to run long shell commands as background tasks — start a test suite, a build, a scrape, a batch job — then keep working and check back for the results, instead of sitting frozen until it finishes.

agent: start_task("pytest -q")        ->  { task_id: "a1b2c3d4", status: "working" }
        ... agent does other useful work ...
agent: task_status("a1b2c3d4")        ->  { status: "completed", duration_seconds: 312 }
agent: task_result("a1b2c3d4")        ->  { output: "418 passed in 311.2s" }

Why

AI agents are bad at waiting. A tool call that takes 10 minutes blocks the whole conversation — or times out and loses the work entirely. The MCP specification is formalizing a Tasks pattern for exactly this problem (extension finalized in the 2026-07-28 spec release); backburner brings that workflow to every client today via plain tools, with first-class Tasks-extension support on the roadmap.

Tools

Tool	What it does
start_task(command, cwd?, timeout_seconds?)	Run a shell command in the background, returns a task id immediately
task_status(task_id)	working / completed / failed / cancelled / timed_out / interrupted
task_result(task_id, tail_lines?)	Captured output — works mid-run too, so you can peek at progress
cancel_task(task_id)	Kill the task and its whole process tree
list_tasks(limit?)	Recent tasks, newest first

Features

• Survives restarts — tasks are tracked in SQLite under ~/.backburner/; output is captured to per-task log files. If the server dies mid-task, orphaned tasks are honestly marked interrupted, never silently lost.

• Real cancellation — kills the full process tree (worker processes included), on Windows and Unix.

• Peek at live progress — task_result on a running task returns the output so far.

• Timeouts — pass timeout_seconds and a runaway task is killed and honestly marked timed_out instead of hanging forever.

• Command policy — restrict what the AI may run with environment variables (regexes, comma-separated; deny always wins):

  BACKBURNER_ALLOW="^pytest,^npm (test|run build)"   # only these may run
  BACKBURNER_DENY="rm -rf,shutdown,format"           # these never run
  

• Zero infrastructure — stdlib only (SQLite, subprocess, threads). No Redis, no Celery, no Docker.

• Tested — a pytest suite covers the full job lifecycle: completion, failure, cancellation, timeouts, crash recovery, and the command policy.

Install

pip install backburner-mcp

Claude Code

claude mcp add backburner -- python -m backburner.server

Claude Desktop / other clients

{
  "mcpServers": {
    "backburner": {
      "command": "python",
      "args": ["-m", "backburner.server"]
    }
  }
}

Security note

backburner executes the shell commands the AI sends it, with your user's permissions. That is its job — but treat it like giving your agent a terminal. Run it only with clients whose tool-use you review/approve, prefer permission modes that require confirmation for start_task, and use BACKBURNER_ALLOW / BACKBURNER_DENY to scope what may run.

Roadmap

• Task timeouts and max-runtime limits
• Allowlist/denylist for commands
• MCP Tasks extension support (spec 2026-07-28) — native tasks/get, tasks/cancel alongside the plain tools
• Structured progress reporting (parse % / step markers from output)
• PyPI release — pip install backburner-mcp

License

MIT