How to Use the Flexport MCP in LlamaIndex

Q: Can I search Flexport purchase orders using LlamaIndex?

Yes. By indexing the output of listpurchaseorders, you can query your purchase history using natural language.

Index your Flexport logistics data with LlamaIndex to create a searchable knowledge base for your supply chain operations.

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Connect Flexport MCP to LlamaIndex

Create your Vinkius account to connect Flexport to LlamaIndex 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 Flexport with LlamaIndex

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Semantic freight search

Index the results of `list_shipments` into your vector store to query your logistics history. You can ask your agent questions about past transit times. Your agent uses this knowledge base to ground its answers in real data. No more guessing shipment ETAs based on vague reports.

Invoices as searchable knowledge

Add invoice details from `list_invoices` to your LlamaIndex knowledge base. This allows you to perform semantic searches across your entire billing history. Your agent can compare current costs against historical data retrieved via `get_invoice`. It identifies trends that manual review usually misses.

Document metadata indexing

Use `list_documents` to catalog your logistics files and index their metadata. You can search for specific documents by content or date. When you need a file, `get_document` returns it instantly. Your agent keeps the index updated as new documents hit your account.

Setup guide

Set up Flexport 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 Flexport 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 Flexport tools.",
)
response = await agent.run("List recent Flexport 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 Flexport. 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 Flexport MCP in LlamaIndex

It fetches the data via MCP tools and converts the output into embeddings. These are stored in your vector database for fast, grounded retrieval.

Yes. By indexing the output of `list_purchase_orders`, you can query your purchase history using natural language.

You control the refresh frequency. You can trigger an update whenever your agent performs a new tool call to ensure the index stays current.

Absolutely. The McpToolSpec bridges the gap, allowing your agents to query logistics data alongside your documents.

The server uses ephemeral sessions. Your shipment metadata is only indexed within your private vector store, never shared with the model provider.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

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