Zeplo MCP for AI. Automate background jobs and webhooks with text commands.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
Zeplo (Queue & Background Job API) lets your AI agent schedule, enqueue, and monitor background tasks directly from natural conversation.
Stop checking dashboards to see why a webhook failed. Use this MCP to run CRON-like schedules, send web requests for later processing, or cancel jobs without touching any code.
It's full control over asynchronous workflows.
What AI agents can do with Zeplo (Queue & Background Job API) Automation
Cancel request
Stops a pending or already scheduled job from running.
Create queue
Sets up a brand new processing queue for your workspace.
Create schedule
Establishes a recurring or one-time scheduled job trigger.
Send an HTTP request to run later; the job is automatically retried if it fails.
Create scheduled jobs that trigger repeatedly, like a clockwork report or notification.
Get the current status, payload, and full history for any job using its unique ID.
Cancel pending jobs or manually kick off retries for failed tasks on demand.
List, create, update, or delete the underlying queues and schedules themselves.
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What AI agents can do with Zeplo (Queue & Background Job API) 17 Tools
These tools allow you to manage every aspect of asynchronous processing, including job enqueuing, scheduling recurring tasks, and reviewing detailed logs.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Zeplo (Queue & Background Job API) on VinkiusCancel Request
Stops a pending or already scheduled job from running.
Create Queue
Sets up a brand new processing queue for your workspace.
Create Schedule
Establishes a recurring or one-time scheduled job trigger.
Create Token
Generates and returns a new API token for the workspace that is only seen once.
Delete Queue
Removes an existing queue from your workspace entirely.
Delete Schedule
Deletes a scheduled job trigger, stopping it permanently.
Enqueue Request
Sends an HTTP request payload to be processed in the background queue immediately or later.
Get Queue
Retrieves overall metrics and details about a specific processing queue.
Get Request
Gets the detailed status, payload, and timeline for any single job execution request.
Invite Team Member
Adds a new user to your workspace team.
List Queue Logs
Retrieves paginated history and logs for all jobs within a specific queue.
List Queues
Shows a list of all queues currently set up in your workspace.
List Schedules
Displays all active scheduled jobs configured for the workspace.
List Team
Lists all members currently belonging to the workspace team.
List Tokens
Shows a list of all generated tokens for the workspace.
Update Queue
Modifies the settings or parameters of an existing queue.
Update Schedule
Changes, pauses, or resumes a scheduled job trigger.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Zeplo integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Zeplo (Queue & Background Job API), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Built on the Model Context Protocol (MCP) for 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 connection provides 17 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Problem of Hidden Failures, Solved with Vinkius AI Gateway
Today, if a critical webhook fails because of a temporary network hiccup or an invalid payload structure, you usually only get a single 'Failure' status in your dashboard. To figure out why, you have to jump into the logs, find the job ID, and manually scroll through hundreds of lines of text to see the exact error message.
With this MCP, that detective work disappears. Your agent manages the entire process. After a failure, instead of just showing 'Failed,' your agent runs `list_queue_logs` for you. You get a clear summary: what failed, why it failed, and what needs to happen next.
Getting Full Control with the Zeplo API
You no longer need separate tools or scripts for simple state changes. You can pause a scheduled job using `update_schedule`, delete an entire queue structure with `delete_queue`, or even cancel a request mid-flight with `cancel_request`. This centralizes your operational control.
What's different now is that the platform handles all this complexity for you. Your agent just needs to know which tool to call, giving you full lifecycle management in one place.
What your AI can actually do with this
This connector lets your agent handle anything that needs to happen in the background. Instead of relying on manual API calls or complicated cron scripts, you talk to your AI client and tell it what job needs doing, when it needs doing, and where the results should go. Your agent can send a request right now, queue it up for later processing with automatic retries, or even set up a recurring schedule that fires every hour.
It gives you total visibility into your entire processing pipeline, letting you list recent jobs by status or retrieve detailed timelines for any specific task. If a job fails, you don't just see the failure; you can use this MCP to manually trigger a retry and get the logs immediately.
Because all these calls run through Vinkius, your AI agent gets full visibility into every single tool call, showing exactly which jobs were enqueued and what data flowed through, so nothing happens in the dark.
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Here's how it actually works
The bottom line is that your AI client handles all the infrastructure calls; you just tell it what workflow needs building.
Tell your agent to set up a job: specifying the target endpoint (e.g., https://api.service.com/sync) and if it needs to run immediately, or if it needs to happen on a schedule.
The MCP executes the necessary API call—whether that's enqueuing via enqueue_request or setting up a recurrence using create_schedule. The job is now in Zeplo's queue.
Your agent confirms the setup and provides a Job ID, which you can then use to check progress with tools like get_request.
Who is this actually for?
Backend developers and DevOps engineers who get tired of clicking through dashboards at 2 a.m. to figure out why an automated webhook failed or why scheduled reports didn't run. It’s for anyone who needs their code logic to be managed by natural language.
Monitoring queue health and retrying production jobs using simple text commands instead of logging into a web console.
Quickly enqueuing test jobs or checking execution logs directly from the IDE when debugging asynchronous code paths.
Automating scheduled reports or notifications by asking the agent to set up Zeplo schedules without needing a developer write custom scripts.
What Changes When You Connect
Stop guessing why a webhook failed. Using list_queue_logs lets your agent pull the full history, telling you exactly what went wrong without manual log diving.
Running complex workflows is simple. You can chain this MCP with another—say, linking an API call to a CRM update—to build multi-step automations that span multiple services.
Need to run something immediately but don't want it tied to your current code deployment? Use enqueue_request to offload the job and let it retry automatically if the endpoint hiccups.
Manage scheduled tasks without deploying new cron jobs. Set up recurring reports or nightly syncs using create_schedule, all through natural conversation with your agent.
The system is always auditable. Every single action, from setting a schedule via create_schedule to retrieving data via get_request, produces a cryptographically signed audit trail.
See it in action
Handling Failed Webhooks
A payment webhook fails due to a temporary 503 error. Instead of waiting for an Ops team member, you ask your agent to check the logs using list_queue_logs. The agent finds the failure and uses its capabilities to trigger an immediate retry via enqueue_request.
Setting Up Daily Reporting
The marketing team needs a report generated every morning at 6 AM. You tell your agent this requirement, and it executes the setup using create_schedule. The next day, you simply ask the AI to verify the schedule is running.
Debugging Async Code
You write a new background worker that sometimes fails. Instead of writing boilerplate test code, you tell your agent to enqueue_request with test data and then use get_queue to monitor the job's progress in real time.
Team Onboarding
A new team member needs access to manage these jobs. You ask your agent to run the necessary setup calls, like generating a dedicated API key using create_token, so they can work immediately.
The honest tradeoffs
Hardcoding Retry Logic
Writing complex try-catch blocks in your application just to handle transient network failures or retries. This adds overhead and complexity.
Use enqueue_request with Zeplo. The system handles the retry logic, failure status updates, and exponential backoff automatically for you.
Forgetting Log Checks
A scheduled job fails silently because the endpoint changed its required payload structure, but your code doesn't check the logs.
Use list_queue_logs immediately after a failure. It gives you the full history and context for why the job failed, saving minutes of investigation.
Manual Status Checks
Having to build custom UI components that poll an external API every few seconds just to know if a long-running process finished.
Use get_request to check the status and timeline of a specific job. You can let your agent monitor this state change for you.
When It Fits, When It Doesn't
You should use this MCP when your business logic requires asynchronous, non-blocking tasks that run independently of user interaction or primary API calls. If you need to schedule something recurring (like a report), use create_schedule. If the task is simple and needs to happen exactly once, use enqueue_request. Don't use it if the operation must complete within the same 1-2 second synchronous API call; then, stick to standard direct service calls. Use this MCP when observability matters—the audit trail and log access are its core value.
Questions you might have
How do I check if a job succeeded or failed using the `get_request` tool? +
The get_request tool provides the detailed status and timeline for any job. You can read the response data to confirm success, or review the error payload if it failed.
Can I make a recurring schedule using `create_schedule`? +
Yes, you can set up schedules that run on repeating intervals (like every hour) or specific dates. Just tell your agent when and how often the job needs to fire.
If I mess up a queue, can I delete it using `delete_queue`? +
Yes, the delete_queue tool removes the entire processing queue from your workspace. Be careful; this is irreversible and stops all jobs associated with that name.
What's the difference between `enqueue_request` and scheduling? +
Enqueue_request sends a job to run at some point, but it’s not tied to a recurring clock. Scheduling uses create_schedule for tasks that must repeat over time.
What happens if I lose access to a token created with `create_token`? +
You'll need to generate a fresh one. First, use the list_tokens tool to see what credentials you have available. Then, running create_token again provides your agent with brand new keys for secure operation.
If I suspect an error in my background process, how do I debug it using `list_queue_logs`? +
Run list_queue_logs and filter the results by job ID or date range. This provides a paginated history of requests, showing exactly what data flowed through your queue and pinpointing where the failure occurred.
How can I check the overall performance or size of my queues using `get_queue`? +
The get_queue tool gives you detailed metrics on resource usage, including current pending job counts and processing throughput. This helps you gauge if your background job system is running smoothly or needs scaling.
If a scheduled task is no longer needed, how do I properly remove it with `delete_schedule`? +
You just call delete_schedule. This permanently removes the schedule definition from your workspace. It ensures that resources aren't wasted running tasks you set up and then forgot about.
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.
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