How to Use the Freightview MCP in LlamaIndex

Q: How does LlamaIndex store historical Freightview quotes?

Your agent calls listfreightquotes to pull recent pricing data. LlamaIndex then parses these JSON payloads into document nodes and embeds them into your vector database for quick semantic retrieval.

Q: Can I query my Freightview address book using LlamaIndex RAG?

Yes, you can index your saved locations using listaddressbook. This allows your LlamaIndex agent to search for the correct warehouse address using natural language queries during the checkout process.

Q: How do I ground shipping rate decisions using Freightview and LlamaIndex?

You run requestrates to get real-time pricing from connected carriers. LlamaIndex combines these live quotes with your historical shipping indexes to help your agent select the most reliable carrier for that lane.

Q: What is the best way to monitor active shipments?

Use listshipments to get a high-level view of all active transit. The MCP client calls this tool to retrieve status updates, and the agent can call getshipmentdetails on specific tracking numbers.

Q: How is my shipment metadata protected during indexing?

Your shipment metadata fetched via getshipmentdetails remains within your local vector index or private cloud environment. This MCP Server handles your data in a secure, ephemeral sandbox that never caches or logs your logistics details.

Index live carrier rates and shipment history directly into LlamaIndex using this Freightview MCP Server.

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Connect Freightview MCP to LlamaIndex

Create your Vinkius account to connect Freightview 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 Freightview with LlamaIndex

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Index Freight Quotes for Semantic Search

The `list_freight_quotes` tool pulls your recent quoting history directly into LlamaIndex vector stores. Your agent indexes these past quotes so you can ask natural language questions about historical pricing trends. You get answers grounded in real transactional data instead of relying on general estimations. By feeding these outputs into your index, you can query past rates alongside your current carrier agreements retrieved via `list_connected_carriers`. This lets your agent identify which carriers consistently offer the best pricing for specific regional lanes.

Ground LlamaIndex Agents in Your Item Catalog

The `list_item_catalog` tool provides the exact dimensions and weights your LlamaIndex agent needs to build accurate queries. This MCP Server allows your agent to find the item, retrieve its specs, and call `request_rates` without hallucinating weights that lead to painful carrier re-weigh fees. The agent pairs this catalog data with your saved locations from `list_address_book`. The resulting rate request is highly accurate because it uses verified historical shipping profiles.

Track Shipment History with RAG

The `list_shipments` tool fetches your entire execution history to build a searchable knowledge base of past deliveries. Your LlamaIndex agent queries this index to resolve customer questions about delivery timelines or carrier performance. You no longer have to log into multiple carrier portals to find out why a shipment was delayed. The agent retrieves specific details via `get_shipment_details` to answer tracking questions instantly. This connects your offline support documents with live logistics data in a single query pipeline.

Setup guide

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

Your agent calls `list_freight_quotes` to pull recent pricing data. LlamaIndex then parses these JSON payloads into document nodes and embeds them into your vector database for quick semantic retrieval.

Yes, you can index your saved locations using `list_address_book`. This allows your LlamaIndex agent to search for the correct warehouse address using natural language queries during the checkout process.

You run `request_rates` to get real-time pricing from connected carriers. LlamaIndex combines these live quotes with your historical shipping indexes to help your agent select the most reliable carrier for that lane.

Use `list_shipments` to get a high-level view of all active transit. The MCP client calls this tool to retrieve status updates, and the agent can call `get_shipment_details` on specific tracking numbers.

Your shipment metadata fetched via `get_shipment_details` remains within your local vector index or private cloud environment. This MCP Server handles your data in a secure, ephemeral sandbox that never caches or logs your logistics details.

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