How to Use the Aero Workflow MCP in Pydantic AI

Q: How does Pydantic AI connect to the Aero Workflow MCP server?

You'll use the MCPToolset class and pass it your Vinkius server URL. Then, you add that toolset to the toolsets list when you create your Agent. It's the standard, unified way to add tools in modern Pydantic AI.

Q: What happens if Aero Workflow changes its API response format?

Your Pydantic AI agent will immediately raise a ValidationError. This is the main benefit—it protects your system from unexpected API changes by enforcing a strict data contract. You'll find out about the problem right away.

Q: Can I use Pydantic AI to create new tasks in Aero Workflow?

Absolutely. Your agent can call the createnewworkflowtask tool. Pydantic AI will ensure the arguments you provide, like the subject and assigned user, match the required format before the call is even made.

Get type-safe, validated access to your Aero Workflow data. Build correct agents with Pydantic AI.

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Connect Aero Workflow MCP to Pydantic AI

Create your Vinkius account to connect Aero Workflow to Pydantic AI 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 Aero Workflow with Pydantic AI

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Validated Calls to the Aero API

Connect Pydantic AI to your Aero Workflow account and stop worrying about bad data. Every response from tools like `list_firm_customers` or `get_task_details` is automatically parsed and validated against a Pydantic model at runtime. If the Aero API ever returns an unexpected field or a malformed response, your agent will raise a `ValidationError` immediately. No silent failures, no corrupted data making its way into your system. You'll know instantly that something is wrong.

Your Pydantic AI Agent Writes to Aero

This MCP server lets your agent modify data in your practice management software. You can use `create_new_customer` to add a client or `create_new_workflow_task` to start a new job for your team. The inputs are validated just like the outputs. Because Pydantic AI is model-agnostic, you can build these agents with OpenAI, Gemini, Anthropic, or even a local model. The tools work the same way everywhere. Your agent code stays clean and portable, focused on logic, not on API quirks.

Query Your Practice as Pydantic Models

Turn your firm's operational data into clean Python objects. Your agent can call `list_workflow_tasks` and get back a list of task objects, ready to use. Same for `list_firm_team_members` or `list_time_tracking_logs`. This makes your agent code much simpler and more reliable. You're not parsing messy JSON dictionaries. You're working with typed objects, which means your IDE gives you autocomplete and your static analysis tools can catch bugs before you even run the code.

Setup guide

Set up Aero Workflow 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": {
        "aero-workflow-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Aero Workflow. 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 Aero Workflow MCP in Pydantic AI

You'll use the `MCPToolset` class and pass it your Vinkius server URL. Then, you add that toolset to the `toolsets` list when you create your `Agent`. It's the standard, unified way to add tools in modern Pydantic AI.

Your Pydantic AI agent will immediately raise a `ValidationError`. This is the main benefit—it protects your system from unexpected API changes by enforcing a strict data contract. You'll find out about the problem right away.

Absolutely. Your agent can call the `create_new_workflow_task` tool. Pydantic AI will ensure the arguments you provide, like the subject and assigned user, match the required format before the call is even made.

It works with any model that Pydantic AI supports. The MCP server is totally independent of the LLM you choose. You can swap models without changing how your agent interacts with Aero Workflow.

Your agent will access client lists, staff info, and specific task details from Aero Workflow. Vinkius processes each tool call in an isolated, ephemeral environment. Your authentication is handled by a single token, keeping your actual Aero credentials private.

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