The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Loopsense listing page.

LoopSense is an open-source MCP server that closes the feedback loop for AI coding agents — giving them real-time visibility into CI results, deployments, test outcomes, and file system changes.
When an AI agent pushes code, runs tests, or triggers a deployment, LoopSense watches the downstream effects and surfaces them back to the agent. No more blind actions.
Supported sources:
Or run directly with npx:
Claude Code (one-liner):
Or add manually to your claude_desktop_config.json (or equivalent MCP host config):
Or if installed globally:
| Variable | Description |
|---|---|
GITHUB_TOKEN | GitHub personal access token for CI polling |
watch_ciWatch a GitHub Actions workflow run. Polls every 30 seconds and emits events on status changes.
watch_processSpawn a local process and capture its output and exit code.
watch_fileWatch a file or directory for changes.
watch_urlPoll an HTTP endpoint and detect status or body changes.
watch_webhookStart a local HTTP server to receive webhook payloads.
Configure your webhook sender to POST to http://localhost:9876.
check_consequencesGet events for a specific action or all recent events.
list_watchesList all active watchers.
cancel_watchStop a watcher by ID.
poll_eventsGet events since a timestamp (fallback for clients without notification support).
LoopSense exposes two MCP resources that update reactively:
loopsense://timeline/recent — last 100 events across all watchesloopsense://watches/active — all currently active watchesloopsense://consequences/{action_id} — events for a specific actionAn agent workflow might look like:
watch_ci with action_id: "my-pr-123"check_consequences with action_id: "my-pr-123"Events and watch records are persisted to ~/.loopsense/events.db (SQLite). Active watches are resumed automatically on server restart.
MIT