Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"backburner": {
"args": [
"-m",
"backburner.server"
],
"command": "python"
}
}
}Are you the author?
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Put your AI agent's slow work on the back burner. Keep cooking.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'backburner-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked backburner-mcp against OSV.dev.
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Put your AI agent's slow work on the back burner. Keep cooking.
Background Tasks ◦ Zero Infrastructure ◦ Survives Restarts ◦ Windows & Unix
📦 PyPI • 🗂️ MCP Registry • 🐛 Issues • 📄 MIT
exit_code is no longer reported for cancelled/timed-out tasks
(it was an artifact of the kill, not a real result); new animated demo below.io.github.RohitYajee8076/backburner.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.

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.
| 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 |
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.
pip install backburner-mcp
claude mcp add backburner -- python -m backburner.server
{
"mcpServers": {
"backburner": {
"command": "python",
"args": ["-m", "backburner.server"]
}
}
}
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 wi