How to Use the Zeplo (Queue & Background Job API) MCP in Google ADK

Q: How do I set up scheduled data ingestion with Zeplo (Queue & Background Job API) and Google ADK?

You use createschedule to define the frequency. Your agent can then rely on this schedule to pull data consistently into BigQuery or other GCP services.

Q: Can I modify a job's parameters after it's been created with Zeplo (Queue & Background Job API) and Google ADK?

Yes. Use updatequeue to adjust the queue settings, or use updateschedule if you need to change the timing of a recurring job.

Q: Where do I find the list of all available queues using Zeplo (Queue & Background Job API) and Google ADK?

Run listqueues to get a full inventory. This helps you understand which job types are already active in your cloud environment.

Q: If I need historical data, how do I check logs with Zeplo (Queue & Background Job API) and Google ADK?

Use listqueuelogs. It provides paginated request history for any specific queue. This is essential when debugging complex multi-service agent calls.

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Connect Zeplo (Queue & Background Job API) MCP to Google ADK

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Setup Zeplo (Queue & Background Job API) with Google ADK

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Manage Complex Workflows

Use `create_queue` to define the structure for batch processing jobs. This allows your agent to queue up hundreds of data records for later, reliable execution. The full management suite includes `get_queue` to track metrics and `update_queue` if you need to change parameters mid-workflow.

Control Job Execution Flow

The server provides `enqueue_request`, which resolves the job from a specific token. This is key when your agent needs to pass data through multiple services, like BigQuery and Vertex AI. You can also use `cancel_request` if any step in that complex flow fails or needs immediate stoppage.

Maintain Job Visibility

Need to see what happened? Call `list_queue_logs` for a full history of job runs. It keeps an audit trail right where your agent can access it. For immediate status checks, use `get_request`, which gives you the current timeline and state of any given HTTP request.

Setup guide

Set up Zeplo (Queue & Background Job API) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Zeplo (Queue & Background Job API) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Zeplo (Queue & Background Job API)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Zeplo (Queue & Background Job API) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Zeplo (Queue & Background Job API) MCP in Google ADK

You use `create_schedule` to define the frequency. Your agent can then rely on this schedule to pull data consistently into BigQuery or other GCP services.

Yes. Use `update_queue` to adjust the queue settings, or use `update_schedule` if you need to change the timing of a recurring job.

This MCP Server touches queue metrics and scheduling parameters. Your agent reads these types to ensure background jobs align with your enterprise resource schedules.

Run `list_queues` to get a full inventory. This helps you understand which job types are already active in your cloud environment.

Use `list_queue_logs`. It provides paginated request history for any specific queue. This is essential when debugging complex multi-service agent calls.

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