How to Use the Axonaut MCP in Pydantic AI

Q: How do I configure Axonaut with Pydantic AI?

Run pip install "pydantic-ai-slimmcp". Use the MCPToolset class with your Vinkius URL and pass it to your Agent's toolsets array.

Enforce strict typing on your Axonaut ERP data using Pydantic AI to validate every invoice and contact.

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Connect Axonaut MCP to Pydantic AI

Create your Vinkius account to connect Axonaut 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 Axonaut with Pydantic AI

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Perplexity

Type-safe MCP Server operations

Working with ERP APIs usually means writing endless boilerplate to catch missing fields. Pydantic AI changes the math by failing loudly if `list_invoices` returns unexpected JSON structures. You define the exact schema you expect. When the agent calls `get_company`, the framework validates the response at runtime so your downstream code never chokes on a null value.

Model-agnostic data processing

Locking yourself into one LLM provider is a bad idea. This setup lets you query `list_expenses` using OpenAI today and switch to a local model tomorrow without changing your tool configuration. The unified `MCPToolset` handles the abstraction. You just point it at the Vinkius HTTP URL, and your agent knows how to execute `list_products` regardless of which underlying model is running the logic.

Reliable CRM updates

Generating action items requires precision. Your script can pull current assignments via `list_tasks` and then push new ones using `create_task`. Because every input is validated before execution, the agent cannot hallucinate required parameters. It checks the connection state with `get_account_check` first, then safely creates the record.

Setup guide

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

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

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

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Common questions about Axonaut MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"`. Use the `MCPToolset` class with your Vinkius URL and pass it to your Agent's `toolsets` array.

The framework validates whatever the server returns. When your agent invokes `list_contacts`, Pydantic ensures the output matches your defined models before passing it to your application.

Raw calls require manual error handling and parsing. This integration treats `list_orders` and `list_projects` as native Python functions with built-in runtime checks.

The toolset supports both Streamable HTTP and SSE transports. You configure this when defining the connection string in your external server setup.

We manage the connection through an isolated V8 container. When your agent queries client lists or project timelines, the data stays within a managed, ephemeral sandbox that never stores your proprietary information.

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