How to Use the Workload MCP in Pydantic AI

Q: Can I track historical Workload data using Pydantic AI?

Yes, you can call listexecutions and listlogs. Because of the type-checking layer, you know exactly what fields to expect when querying past job records, making your analysis reliable.

Q: What happens if I try to manage a Workload process with Pydantic AI?

If an agent attempts to call disableworkflow but provides malformed input data (say, an invalid workflow ID), the system catches it instantly and fails cleanly. It prevents the bad action from even being sent.

Q: What is the best way to view all active processes with Pydantic AI?

The agent calls listworkflows first, which provides a clean list of existing automations. Then, you can drill down using getworkflow(id) for specific details.

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Setup Workload with Pydantic AI

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Validate Workflow Creation and Status

When your agent creates a new process, it calls `create_workflow`, and the response is immediately checked against strict Pydantic models. This means if the API gives bad data, the agent fails loudly with a clear error, not silent corruption. Similarly, when checking status via `get_workload_status`, the received connection details (`get_connection`) are validated, guaranteeing that what your code receives is always correct.

Manage and Correct Failed Runs

If a job fails, calling `retry_execution` runs the process again. Crucially, the input and output of this retry are validated by Pydantic models, ensuring that even in failure recovery, your data remains correct. The agent can also list all past failures using `list_executions`, receiving structured, trustworthy data for debugging.

Structured Retrieval of Process Data

Listing processes is simple. The agent calls `list_workflows` to get a defined list of active automations. To check on one specifically, it uses `get_workflow`, getting the data back in a predictable, typed structure. This structured approach extends to logs via `list_logs`. You never have to worry about unexpected fields showing up.

Setup guide

Set up Workload 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": {
        "workload-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Workload tools.",
)

result = await agent.run("List recent Workload transactions")
print(result.output)

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Common questions about Workload MCP in Pydantic AI

The core benefit is data correctness. Every time your agent calls a tool—like `list_workflows` or `get_workflow`—the response payload is validated against Pydantic schemas. This eliminates runtime errors from bad API responses.

Yes, you can call `list_executions` and `list_logs`. Because of the type-checking layer, you know exactly what fields to expect when querying past job records, making your analysis reliable.

If an agent attempts to call `disable_workflow` but provides malformed input data (say, an invalid workflow ID), the system catches it instantly and fails cleanly. It prevents the bad action from even being sent.

The agent calls `list_workflows` first, which provides a clean list of existing automations. Then, you can drill down using `get_workflow(id)` for specific details.

This server deals with metadata: workflow definitions, connection parameters, execution statuses, and audit logs. Pydantic ensures the structure of this operational data is always sound.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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