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

Q: How do I reliably process batch data with Zeplo (Queue & Background Job API) and Pydantic AI?

You set up a queue using createqueue. Since every result is validated by Pydantic, your agent can trust that the incoming job payload has the exact fields it expects.

Q: Can I modify or pause jobs with Zeplo (Queue & Background Job API) and Pydantic AI?

Yes. Use updateschedule to change timing, or use updatequeue for parameters. The framework validates these changes against your defined models.

Q: Where do I list all active jobs with Zeplo (Queue & Background Job API) and Pydantic AI?

Run listqueues first. Then, use the resulting queue names with other tools to manage them. The type-safe approach keeps everything predictable.

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

Use listqueuelogs. Because of the type validation, you know that every log entry returned—whether a success or failure—will have a predictable structure.

Ensure data correctness in background jobs with Pydantic AI and Zeplo (Queue & Background Job API).

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Setup Zeplo (Queue & Background Job API) with Pydantic AI

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Guarantee Data Consistency

To ensure zero silent failures, your agent uses `create_queue` to define job boundaries. Because every response is typed, the data structure must be perfect for validation. Use `update_queue` when you need to change parameters, but Pydantic makes sure those changes fit defined types.

Manage Job State Explicitly

When a job needs to stop or restart, call `cancel_request`. This provides clear state management. The agent knows exactly when the request is dead. For other state changes, you can use `delete_queue` and `delete_schedule`, knowing that these actions are fully validated before execution.

Audit Job Execution Steps

The agent uses `list_queue_logs` to get a history of job runs, validating the structure of each log entry. This prevents misinterpretation of failure reasons. Furthermore, `get_request` lets you inspect the timeline for any request, guaranteeing that all reported fields match expected types.

Setup guide

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

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "zeplo-queue-background-job-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Zeplo (Queue & Background Job API) tools.",
)

result = await agent.run("List recent Zeplo (Queue & Background Job API) transactions")
print(result.output)

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

You set up a queue using `create_queue`. Since every result is validated by Pydantic, your agent can trust that the incoming job payload has the exact fields it expects.

Yes. Use `update_schedule` to change timing, or use `update_queue` for parameters. The framework validates these changes against your defined models.

This MCP Server touches queue details and metrics. Your agent reads these types, enforcing that the reported status codes and counts adhere to strict Python models.

Run `list_queues` first. Then, use the resulting queue names with other tools to manage them. The type-safe approach keeps everything predictable.

Use `list_queue_logs`. Because of the type validation, you know that every log entry returned—whether a success or failure—will have a predictable structure.

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