How to Use the Axle MCP in Pydantic AI

Validate all Axle logistics responses with Pydantic AI type safety.

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Connect Axle MCP to Pydantic AI

Create your Vinkius account to connect Axle 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 Axle with Pydantic AI

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Strict logistics types for Pydantic AI

Every tool like `get_load` or `get_vehicle` returns data that Pydantic AI checks against your schema at runtime. If the API structure changes, your agent stops immediately instead of propagating bad data. This prevents the common issue of hallucinated fields in your logistics pipeline. You get the reliability of hard types for every single fleet operation.

Manage fleet state via Pydantic AI

Update your system records using `update_load` or `update_driver_status` with full confidence in your data types. The server ensures your agent inputs match the exact specifications required by the backend. Your code remains clean and predictable. You avoid the headache of manual parsing because the framework handles the heavy lifting.

Query fleet data with Pydantic AI

Use `list_drivers` and `list_vehicles` to populate your internal models. The framework validates the incoming JSON against your Python classes automatically. This is the safest way to build logistics automation. You define the shape of your data, and the MCP Server fills it.

Setup guide

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

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

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

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Common questions about Axle MCP in Pydantic AI

Install the slim package and use the MCPToolset to connect to your server. Pass the toolset into your agent constructor to enable type-safe tool calls.

The agent will raise a validation error immediately. This stops the process before bad data can corrupt your fleet management records.

It works with both Streamable HTTP and SSE. You just need to point the toolset to your active Vinkius endpoint.

Yes. Since the framework is model-agnostic, you can swap between different LLMs while keeping your fleet tool definitions identical.

Security is enforced at the Vinkius gateway. When you access `list_documents`, the server provides a read-only stream that is validated by your local Pydantic models, keeping the raw data isolated.

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