How to Use the Ship24 MCP in Pydantic AI

Q: How can Pydantic AI manage bulk tracking with Ship24?

Use createbulktrackers. The process is clean: you pass the list of numbers, and the server queues them up. The results are then validated against your Pydantic schema, giving you predictable data back.

Q: Does Pydantic AI validate carrier codes for Ship24?

Yes, it does. When using searchcouriers, the tool output is strictly enforced. You'll only get a clean code that matches your defined data model, eliminating guesswork.

Q: What tracking information can I get with Ship24 and Pydantic AI?

You can get everything: live updates via getlivetracking, historical records from gettrackingresults, or even the current status using findtrackerbynumber. The data is always structured.

Q: If I need to update a tracker's metadata with Ship24 and Pydantic AI?

You use updatetrackerinfo. This action updates the record, but critically, it ensures that any new data you write back into the system adheres to your defined Pydantic models.

Ensure data correctness when accessing Ship24 tracking via Pydantic AI.

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Connect Ship24 MCP to Pydantic AI

Create your Vinkius account to connect Ship24 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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Validate Carrier Information

Use `search_couriers` to find the exact codes for carriers like FedEx or Correios. This tool is critical because it guarantees the output format, so your agent always gets a clean, predictable code. It prevents errors that happen when you rely on unstructured text inputs. The Pydantic validation layer ensures the returned carrier data fits exactly into your expected Python models.

Track and List Shipments

The `list_active_trackers` tool lets you get a list of all current shipments, but the results are always validated. This means if the underlying API returns messy data, your agent doesn't crash; it fails loudly with a clear Pydantic error. It gives you confidence in the output structure every time you need to check what's currently moving.

Retrieve Comprehensive History

Need the full story? Call `get_tracking_results`. The tool fetches all historical status updates. Because this is validated, you know that even if the carrier feed is messy, your agent will only receive clean, structured data for processing. This eliminates runtime surprises and makes building reliable production tools much easier.

Setup guide

Set up Ship24 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": {
        "ship24-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Ship24 tools.",
)

result = await agent.run("List recent Ship24 transactions")
print(result.output)

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Common questions about Ship24 MCP in Pydantic AI

Use `create_bulk_trackers`. The process is clean: you pass the list of numbers, and the server queues them up. The results are then validated against your Pydantic schema, giving you predictable data back.

Yes, it does. When using `search_couriers`, the tool output is strictly enforced. You'll only get a clean code that matches your defined data model, eliminating guesswork.

You can get everything: live updates via `get_live_tracking`, historical records from `get_tracking_results`, or even the current status using `find_tracker_by_number`. The data is always structured.

You use `update_tracker_info`. This action updates the record, but critically, it ensures that any new data you write back into the system adheres to your defined Pydantic models.

The server only interacts with tracking numbers and associated logistics metadata. The use of Pydantic validation keeps the focus strictly on the structural integrity of shipment records, not personal details.

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