Zeplo MCP. Manage background jobs and scheduling via your AI agent.
Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Zeplo (Queue & Background Job API) lets your AI agent manage background workflows, webhooks, and scheduled tasks directly from natural language commands.
Need to process a large batch of data or run a report every night? Instead of building complex cron jobs or staring at a dashboard, you ask your agent to enqueue the job using `enqueue_request` or set up recurring triggers with `create_schedule`.
It's full lifecycle control for serverless queues.
What your AI agents can do
Cancel request
Stops a specific job that is pending or already scheduled for execution.
Create queue
Sets up an entirely new queue within your Zeplo workspace.
Create schedule
Creates a brand new recurring or one-time trigger schedule for an API endpoint.
The agent sends HTTP requests that run later, automatically retrying the job if it fails.
You define recurring or one-off delayed schedules for API endpoints using CRON-like syntax.
The agent retrieves the status, payload, and entire timeline of any specific background job ID.
You can list all queues or manually update a queue's configuration through natural language commands.
The agent pulls paginated request history and failure logs, letting you see exactly why a job failed.
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Zeplo (Queue & Background Job API) MCP Server: 17 Tools
Use these tools to manage the full lifecycle of background jobs, webhooks, and scheduled tasks directly through your AI agent.
019e5d69cancel request
Stops a specific job that is pending or already scheduled for execution.
019e5d69create queue
Sets up an entirely new queue within your Zeplo workspace.
019e5d69create schedule
Creates a brand new recurring or one-time trigger schedule for an API endpoint.
019e5d69create token
Generates and returns a unique access token for your Zeplo workspace. This token is only shown once.
019e5d69delete queue
Permanently removes an existing queue from the system.
019e5d69delete schedule
Removes a defined recurring or one-off schedule trigger.
019e5d69enqueue request
Sends a new HTTP request to be processed in the background queue, resolving it from a provided token.
019e5d69get queue
Retrieves current metrics and detailed status information for a specified queue.
019e5d69get request
Fetches the full details, including the timeline of events, for a specific background job ID.
019e5d69invite team member
Adds and invites a new user to collaborate on your Zeplo workspace team.
019e5d69list queue logs
Retrieves paginated, detailed history of all requests that have passed through a specific queue.
019e5d69list queues
Shows a list and high-level overview of all queues currently existing in your workspace.
019e5d69list schedules
Lists all active schedules, showing when they run next and their associated endpoints.
019e5d69list team
Displays a list of every member currently part of the workspace team.
019e5d69list tokens
Retrieves an overview and listing of all available access tokens in your account.
019e5d69update queue
Allows modification of a queue's settings, such as name or associated handlers.
019e5d69update schedule
Changes the timing, status (pause/resume), or target endpoint of an existing schedule.
Choose How to Get Started
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Build Your Own
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- Use this MCP plus 4,700+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
You're looking at Zeplo, and it lets your AI agent handle all your background workflows—the stuff that needs to run later or on a timer. You don't have to build complex cron jobs or stare at an admin dashboard; you just tell your agent what needs doing.
To get started, you can generate unique access credentials with create_token, and then check all the tokens you've got using list_tokens. You'll need these to connect your AI client to your workspace.
Running Background Jobs
Need to process a massive data batch? Shoot an HTTP call into the background queue using enqueue_request, and it runs automatically, even if it fails—it'll retry the job. Wanna set up recurring reports or delayed triggers? Use create_schedule to build a new schedule for any API endpoint; you can define these one-time or recurring triggers with CRON-like syntax.
If that scheduled work is done or wrong, you can clean house by calling delete_schedule. You'll find all your active schedules listed through list_schedules, which shows when they run next and what endpoints they hit.
Tracking and Debugging Workflows
You get full control over the job lifecycle. For any specific background job ID, you can fetch the total details—the payload, the whole event timeline—using get_request. If a job goes south, don't sweat it; pull paginated request history and failure logs through list_queue_logs to see exactly why it bombed. You also need to know which queues you're working with. list_queues shows you all the existing workspaces, and get_queue retrieves current metrics or detailed status for a specific one.
If you gotta change how a queue runs, update_queue lets you modify its settings like name or handlers.
Managing Triggers and Cleanup
When you're done with a job, or if it's running too long, cancel_request stops the pending work dead in its tracks. You can permanently remove an entire queue using delete_queue. If there are schedules that shouldn't exist anymore, delete_schedule removes the trigger entirely.
Workspace Management and Teams
Your agent also manages who works on your account. Use list_team to see every member currently part of the team, or invite a new collaborator with invite_team_member. If you need visibility into your system's setup, list_tokens gives you an overview and listing of all available access tokens in your account.
It's full control over both scheduled tasks and serverless queues.
How Zeplo MCP Works
- 1 Subscribe to the server and enter your Zeplo API Key.
- 2 Your AI client uses tools like
enqueue_requestorcreate_schedulevia natural language prompts. - 3 The job runs asynchronously in the background, and you use monitoring tools (e.g.,
get_request,list_queue_logs) to check its status.
The bottom line is: Your AI client treats your serverless queue infrastructure like a set of callable functions, letting you manage complex workflows without leaving the chat window.
Who Is Zeplo MCP For?
This tool is for Ops Engineers who are sick of checking multiple dashboards to see if a webhook fired correctly. It's for Backend Developers who need quick ways to test job enqueuing outside of local code runs. Product Teams use it when they need reliable, automated scheduled reports without hiring dedicated DevOps staff.
Quickly enqueue test jobs and check execution logs directly from the IDE or terminal using tools like enqueue_request.
Monitor queue health, identify failed production runs, and manually trigger retries on bad data streams using natural language commands.
Automate scheduled reports or notifications by asking the agent to set up Zeplo schedules with create_schedule.
What Changes When You Connect
- Stop digging through logs. Use
list_queue_logsto pull paginated, comprehensive request history for any queue instantly. You see the failure reason, not just that it failed. - Need a report run every Monday at 9 AM? Don't write cron jobs. Use
create_scheduleto set up reliable, recurring triggers, and your AI handles the timing. - Job failed? Instead of manual intervention, use
get_requestwith a specific job ID. You get the full request timeline—from enqueueing to final failure point—in one place. - Queue maintenance is simplified. Use
list_queuesto see all endpoints in one shot andupdate_queueif you need to change processing logic without code deployment. - Control runaway jobs with
cancel_request. If an API endpoint starts looping or doing something unexpected, your agent can halt it instantly using the job ID. - The whole process is callable. Your AI client treats scheduling (
create_schedule) and running jobs (enqueue_request) as standard functions, making complex workflows easy to build.
Real-World Use Cases
Debugging a webhook failure after deployment
The Ops Engineer pushes code that breaks the nightly user sync. Instead of manually checking the dashboard logs for the last 24 hours, they ask their agent to list_queue_logs for the 'sync' queue. The agent immediately identifies the first record showing a 500 error and uses get_request to trace back exactly which payload caused it.
Setting up automated reporting
The Product Team needs monthly usage reports, but manual scheduling is complex. They ask the agent to run a report every 1st of the month. The agent uses create_schedule with specific timing and an endpoint URL, ensuring zero human oversight until the job finishes.
Testing a new API integration
The Backend Developer needs to test a payment webhook without setting up live endpoints. They use enqueue_request, sending a mock payload via their agent. The job runs asynchronously, allowing them to check the results using get_queue status immediately.
Halting an unintended process
A scheduled background task accidentally starts processing old user data that should be archived. An administrator spots this and instructs their agent to use cancel_request on the job ID, stopping the resource drain before it gets worse.
The Tradeoffs
Treating the API like a database
Trying to list every single historical event by repeatedly calling get_queue with different date ranges and manually combining results. This is slow, error-prone, and incomplete.
→
Use list_queue_logs. This tool provides paginated request history for a queue in one structured call. It's built to handle the volume of data you need.
Relying on UI buttons
Waiting until a failure occurs, then having to manually go into the web dashboard to find and restart the job or change its schedule.
→
Use update_schedule or enqueue_request. Your agent handles these changes via natural language commands, making it part of your standard workflow.
Over-complicating scheduling
Attempting to set up multiple overlapping CRON jobs for the same endpoint using separate manual schedules, leading to resource conflicts.
→
If you need a schedule, use create_schedule. If you need more control over timing parameters or want to pause it later, manage it all via update_schedule.
When It Fits, When It Doesn't
Use this server if your core problem involves asynchronous work: jobs that must run sometime in the future, reports triggered by time, or webhooks processing data after an initial event. You need to know when and how often tasks fire, and you want visibility into failures without logging into a dedicated dashboard.
Don't use this if your requirement is simple CRUD (Create, Read, Update, Delete) on static records—use a database connector instead. Also, don't use it if you need real-time, continuous stream processing; Zeplo manages discrete jobs. If your goal is merely to list all team members or tokens, those are secondary management tools, but the core value comes from enqueue_request and create_schedule.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zeplo. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 17 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Debugging a failed background job shouldn't take 20 minutes of clicking through tabs.
Right now, if a webhook fails, you have to jump into the monitoring dashboard. You click 'Jobs,' then filter by date range, then select the queue, and finally scroll through pages of logs until you find that one 500 error code. It’s tedious, it's slow, and half the time, the log view is confusing.
With Zeplo MCP Server, your agent handles all that complexity. You just tell your client to check the queue status or run `list_queue_logs`. The result is a clean, structured payload detailing the failure reason and exactly what data caused it. No clicking required.
Zeplo (Queue & Background Job API) MCP Server: Control job flow with one prompt.
Previously, scheduling a report meant opening the scheduler UI, figuring out the CRON syntax, and manually setting up the endpoint. You were limited by the tool's interface, not your logic.
Now, you use `create_schedule` through natural language prompts. The agent handles the timing, the necessary parameters, and the execution. It’s full workflow control—just talk to it.
Common Questions About Zeplo MCP
How do I check if a background job failed using get_request? +
You pass the unique Job ID to get_request. This tool returns the complete timeline of that specific request, showing the final status (success or failure) and the error payload if it broke.
Can I list all active schedules using list_schedules? +
Yes, list_schedules provides a manifest of every schedule you've created. You can see the trigger type (CRON or one-off) and when it runs next.
What if I need to stop a job that is running now? Do I use cancel_request? +
That’s right. If you have a Job ID, cancel_request tells Zeplo to halt the execution. It's critical for preventing runaway processes.
Is there a tool to see all my queues at once? Use list_queues. +
Yes, list_queues gives you an overview of every queue in your workspace. This lets you quickly identify which pipelines are active or need attention.
I need new credentials or want to check my access limits; how do I use the `create_token` and `list_tokens` tools? +
You must first run create_token to generate a new credential set. The system immediately returns this token, so make sure you save it somewhere safe. You can then use list_tokens to see all tokens associated with your workspace.
The parameters for an existing queue need adjusting; how do I change them using the `update_queue` tool? +
update_queue lets you modify a queue's configuration—like changing retry counts or naming conventions. It keeps your historical data intact while letting you adjust its current operational settings.
I want to temporarily stop a scheduled job without deleting it; what does `update_schedule` do? +
update_schedule lets you pause, resume, or modify an existing schedule. Instead of removing the entire setup, this tool keeps the definition ready until you manually reactivate it.
What happens if I send malformed or incomplete data when calling `enqueue_request`? +
The job will fail validation before processing begins. The response payload from the API call details exactly which field was missing or improperly formatted, allowing you to correct your input immediately.
Can I schedule a job to run at a specific time in the future? +
Yes! Use the enqueue_job tool and specify a delay or a schedule parameter. You can tell the AI 'Schedule this request for tomorrow at 10am' and it will handle the timestamp conversion for Zeplo.
How do I check why a specific background job failed? +
You can use the get_job tool with the Job ID. The AI will retrieve the full execution log, including the response body and status code from your endpoint, helping you debug the failure instantly.
Is it possible to cancel a job that hasn't run yet? +
Absolutely. Use the cancel_job tool with the target Job ID. This is useful for stopping scheduled tasks or queued jobs that are no longer necessary.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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