How to Use the Freightview MCP in Pydantic AI

Type-safe logistics automation with Pydantic AI. Connect your MCP server to get validated freight data and reliable carrier insights.

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Connect Freightview MCP to Pydantic AI

Create your Vinkius account to connect Freightview 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 Freightview with Pydantic AI

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Type-safe freight quoting with Pydantic AI

Your agent calls `request_rates` and validates the structure of every quote. Because you use Pydantic models, if the API returns a malformed price or missing carrier info, the agent catches it instantly. This prevents your agent from acting on bad data. It reads `list_item_catalog` to confirm item details before sending them to the carrier, ensuring your shipping specs are always correct.

Manage carrier info in Pydantic AI agents

Your agent fetches carrier details using `get_carrier_details` and ensures the output matches your expected schema. It uses `list_connected_carriers` to keep a live list of your active partners. If a carrier's data format changes, your agent fails safely. You get a validation error instead of corrupted logic, allowing you to update your models before proceeding.

Audit shipment history with Pydantic AI

Your agent pulls shipment data using `get_shipment_details`. Every field is parsed against your Pydantic model, so you know exactly what the status, weight, and carrier info look like. Run `list_shipments` to get a bulk update of your operations. The agent checks every record for consistency, ensuring your data remains reliable for reporting and auditing.

Setup guide

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

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

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

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Common questions about Freightview MCP in Pydantic AI

Every response from the tools is checked against your defined models. If the server returns something unexpected, the agent stops, protecting your workflow from bad data.

The type-safety ensures that only expected fields reach your agent logic. This limits the surface area for errors when handling carrier information.

Define a custom exception for your agents. When a rate tool fails, catch the error and log it to your tracking system so you can audit the failed request.

Yes. The server provides structured JSON responses that map perfectly to Pydantic models. You can define your schema once and trust that the data will fit.

Access is restricted by your token. Your agent only sees what you explicitly allow through your tool definitions, and all data is validated at the boundary.

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