How to Use the SafeCube Container Tracking MCP in LlamaIndex

Q: Does the server check if the API is working?

Yes, the checkapistatus tool is included. You can build it into your ingestion pipeline to make sure the SafeCube service is operational before you try to fetch and index any data, preventing errors in your knowledge base.

Build a knowledge base from live shipping data and query it with LlamaIndex.

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Connect SafeCube Container Tracking MCP to LlamaIndex

Create your Vinkius account to connect SafeCube Container Tracking to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup SafeCube Container Tracking with LlamaIndex

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Index Real-Time Shipment Data

Stop just fetching data; start indexing it. With LlamaIndex, you can set up a simple ingestion pipeline. It calls `list_active_shipments` and `get_container_tracking` on a schedule, feeding the live status of your containers directly into a vector index. Now, your AI client isn't just working with transient information. It's building a searchable history of your entire logistics operation. Every location update and every event from `get_shipment_events` becomes a permanent, queryable part of your company's knowledge base.

Ground Your Agent in Logistics Reality

A RAG application is only as good as its data. This MCP server gives your LlamaIndex agent access to the ground truth. When a user asks, "What's the status of the shipment to Hamburg?", the agent can make a live call to `get_container_tracking` for the most current data. The agent then combines that live data with the historical context it has from your indexed shipment history. This means you get answers that are both up-to-the-minute accurate and rich with past performance data. It's the difference between a simple lookup and genuine insight.

Ask Your LlamaIndex Agent About Past Shipments

Once you've indexed your data from this MCP server, you can ask complex questions in plain English. For example: "Show me all shipments from last quarter that had a customs delay event." Your agent can answer that by querying the indexed output of past `get_shipment_events` calls. You're not limited to simple key-value lookups. You can perform semantic searches across your entire shipping history. This turns your raw logistics data into a strategic asset for analyzing carrier performance and supply chain weak points.

Setup guide

Set up SafeCube Container Tracking MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all SafeCube Container Tracking MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to SafeCube Container Tracking tools.",
)
response = await agent.run("List recent SafeCube Container Tracking data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by SafeCube. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about SafeCube Container Tracking MCP in LlamaIndex

You use the LlamaIndex MCP tool specification to wrap our server endpoint. This exposes the SafeCube tools like `get_container_tracking` so they can be used by a FunctionAgent or within a data ingestion pipeline to populate a vector store.

Yes, that's exactly what it's for. You can create an ingestion pipeline that uses `list_active_shipments` and `get_shipment_events` to build a knowledge base. Then, your query engine can use that data to answer user questions about your logistics.

You can set up a recurring job that calls the SafeCube tools and re-indexes the results. For near-real-time updates, your agent can be configured to call `get_container_tracking` directly before answering a query about a specific shipment.

Yes, the `check_api_status` tool is included. You can build it into your ingestion pipeline to make sure the SafeCube service is operational before you try to fetch and index any data, preventing errors in your knowledge base.

The MCP server itself is stateless. It processes requests for shipment IDs and container tracking data in a secure, isolated Vinkius environment. Where you store the indexed data on your end is your decision, but we never see or save it.

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