QStash MCP for AI. Manage reliable message flow from chat.

Getting Started and Managing Queues

When you need reliable background processing that doesn't rely on brittle cron jobs, this server connects your AI client directly to QStash. You manage the entire message lifecycle—from scheduling to failure recovery—all through natural language commands.

You can check what queues you’ve set up using list_queues, or create a new one or modify an existing queue's parameters and parallelism with upsert_queue. If you need to view the current status of any specific line, use get_queue.

For managing topics (URL Groups), you can list all configured groups using list_url_groups, then check a group’s endpoints configuration via get_url_group; if you gotta build one or update its destinations, use upsert_url_group. You can also remove specific endpoints from a topic using remove_endpoints.

Sending and Controlling Messages

You don't just send messages; you control them. To dispatch a single message with custom timing, retries, or deduplication rules, use publish_message. If you gotta blast out multiple destinations at once, batch_messages sends several messages in one request. You can also cancel specific pending transmissions using cancel_message, or stop a whole bunch of them by filtering and criteria for bulk_cancel_messages.

To ensure tasks run in strict order (FIFO) or with controlled concurrency, use enqueue_message. If you need to send messages to multiple locations without defining full topic groups first, you can also use batch_messages.

System State and Auditing Failure

When things go wrong, your data doesn't just vanish. Failed transmissions land in the Dead Letter Queue (DLQ). You can see a list of every message that failed delivery using list_dlq, grab the full details on one specific failure with get_dlq_message, or manually try sending it again by calling retry_dlq_message.

If you know something's junk, you can remove one item from the DLQ with delete_dlq_message, or wipe out every message in the entire queue at once using bulk_delete_dlq.

To keep your system running smoothly, you can monitor scheduled jobs by listing them all with list_schedules; to check a single job's parameters, use get_schedule. You control these jobs directly: pause one with pause_schedule, or restart it when needed using resume_schedule.

Advanced System Control and Maintenance

You can manage the entire system’s throughput. Check how many keys you have configured by running get_keys, or review all rate limit settings with list_flow_control_keys. To get details on a specific key, use get_flow_control_key.

If usage gets too high, don't sweat it; you can temporarily suspend a key to prevent overuse using pause_flow_control, or reactivate it later with resume_flow_control. You can set permanent rules for any rate limit key using pin_flow_control, and remove those rules by calling unpin_flow_control. If you need to reset the usage counter on a key, use reset_flow_control_rate.

For system-wide oversight, check global concurrency limits with get_global_parallelism, or review all operational history using list_logs; for granular audit data, you can also view historical logs via list_logs.

Putting It All Together: Lifecycle Management

You control the state of every component. You can halt processing on a designated line with pause_queue, or restart it immediately with resume_queue. Similarly, if a scheduled job is running too often, you can pause it using pause_schedule and resume it later with resume_schedule. When you're done testing a queue or topic group, remember that you need to delete the entire resource using delete_queue, delete_url_group, or cleaning up a background job schedule with delete_schedule.