How do I find my SafeCube API Key?

Log in to your [**SafeCube account**](https://app.safecube.ai/), navigate to the 'API' or 'Integrations' section in your dashboard, and generate a new Bearer Token. Copy and paste it below.

What container formats are supported?

The API supports standard 11-character BIC codes (e.g., TCNU1234567) used by global maritime shipping lines.

Can the agent show real-time ETAs?

Yes. The `get_container_tracking` tool retrieves the current Estimated Time of Arrival (ETA) metadata for specified containers.

SafeCube Container Tracking MCP Server for Pydantic AI 4 tools — connect in under 2 minutes

Name: SafeCube Container Tracking
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 4 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect SafeCube Container Tracking through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to SafeCube Container Tracking "
            "(4 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in SafeCube Container Tracking?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About SafeCube Container Tracking MCP Server

Empower your AI agent to orchestrate your entire maritime logistics and container auditing workflow with SafeCube, the comprehensive source for real-time shipment data. By connecting the SafeCube API to your agent, you transform complex tracking searches into a natural conversation. Your agent can instantly retrieve container positions, audit active shipment statuses, and query historical tracking events without you ever touching a logistics dashboard. Whether you are managing supply chain visibility or monitoring regional port delays, your agent acts as a real-time maritime consultant, ensuring your data is always precise and up-to-the-minute.

Pydantic AI validates every SafeCube Container Tracking tool response against typed schemas, catching data inconsistencies at build time. Connect 4 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Container Auditing — Retrieve high-resolution tracking details for any maritime container by number, including status and vessel metadata.
Shipment Oversight — Audit all active shipments in your account to maintain a clear view of global logistics and scale.
Event Discovery — Retrieve detailed tracking events for specific shipment IDs to understand the temporal distribution of logistics milestones instantly.
Logistics Intelligence — Query real-time ETA and position markers to assist in deep-dive supply chain classification.
Operational Monitoring — Check API status to ensure your maritime tracking workflow is always operational.

The SafeCube Container Tracking MCP Server exposes 4 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect SafeCube Container Tracking to Pydantic AI via MCP

Follow these steps to integrate the SafeCube Container Tracking MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 4 tools from SafeCube Container Tracking with type-safe schemas

Why Use Pydantic AI with the SafeCube Container Tracking MCP Server

Pydantic AI provides unique advantages when paired with SafeCube Container Tracking through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your SafeCube Container Tracking integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your SafeCube Container Tracking connection logic from agent behavior for testable, maintainable code

SafeCube Container Tracking + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the SafeCube Container Tracking MCP Server delivers measurable value.

Type-safe data pipelines: query SafeCube Container Tracking with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple SafeCube Container Tracking tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query SafeCube Container Tracking and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock SafeCube Container Tracking responses and write comprehensive agent tests

SafeCube Container Tracking MCP Tools for Pydantic AI (4)

These 4 tools become available when you connect SafeCube Container Tracking to Pydantic AI via MCP:

check_api_status

Check if the SafeCube service is operational

get_container_tracking

Get real-time tracking data for a specific maritime container

get_shipment_events

Get a list of tracking events for a specific shipment ID

list_active_shipments

List all active shipments currently tracked in your SafeCube account

Example Prompts for SafeCube Container Tracking in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with SafeCube Container Tracking immediately.

"Track container 'TCNU1234567' using SafeCube."

"List all my active shipments."

"What are the latest events for shipment ID 'SHIP-123'?"

Troubleshooting SafeCube Container Tracking MCP Server with Pydantic AI

Common issues when connecting SafeCube Container Tracking to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

SafeCube Container Tracking + Pydantic AI FAQ

Common questions about integrating SafeCube Container Tracking MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your SafeCube Container Tracking MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.