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

Facilitate consensus decision-making between agents using Zeplo (Queue & Background Job API) with AutoGen.

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Connect Zeplo (Queue & Background Job API) MCP to AutoGen

Create your Vinkius account to connect Zeplo (Queue & Background Job API) to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Zeplo (Queue & Background Job API) with AutoGen

Ask AI about this MCP

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Claude

Perplexity

Agent Debate Over Scheduling Logic

The answer isn't obvious, and neither is the best time to run a job. You can set up an agent debate where one agent argues for `create_schedule` while another checks current queue capacity using `get_queue`. The system converges on whether a background job should be scheduled or if the request needs to wait in the queue.

Negotiate Workflow Status via MCP Server

When agents disagree on data, they can challenge each other using Zeplo's tools. A security agent might demand a token check (`list_tokens`), while a performance agent insists on checking the job timeline via `get_request`. The multi-agent framework forces deliberation until all competing viewpoints agree on the next action.

Automated Job Management for AutoGen

You can build autonomous pipelines where one agent initiates a task by calling `enqueue_request`, and another agent monitors its progress using `list_queue_logs` until completion is confirmed. This creates self-correcting systems that manage long-running jobs without human intervention.

Setup guide

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

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Zeplo (Queue & Background Job API) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Zeplo (Queue & Background Job API)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zeplo (Queue & Background Job API) data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Zeplo (Queue & Background Job API)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zeplo (Queue & Background Job API) data")
    print(result.messages[-1].content)

Get your connection token →

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

It provides concrete, measurable data points for agents to argue over. For example, one agent might challenge a plan because the queue is full, citing `get_queue` metrics.

Yes. You can make agents debate which token should be used or if a new one needs to be generated using `create_token`. This ensures secure, consensus-driven authentication.

The agents interact with structured job payloads and workspace settings. The critical data type they are debating over is system status and authorization tokens.

It's highly reliable because the agents actively check logs (`list_queue_logs`) and job timelines (`get_request`) until they achieve consensus on completion.

It allows different agents to specialize. One agent can focus solely on creating schedules, while another manages the lifecycle of queued requests.

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