Freightview MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Freightview as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Freightview. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Freightview?"
)
print(response)
asyncio.run(main())
* 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 Freightview MCP Server
Connect your Freightview account to any AI agent to automate your LTL (Less-Than-Truckload) freight quoting and logistics management through the Model Context Protocol (MCP). Freightview is a centralized platform that connects shippers with all their carriers in one place. This MCP server enables you to request real-time rates, monitor active shipments, and oversee your logistics network directly through natural conversation.
LlamaIndex agents combine Freightview tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
Key Features
- Real-time Quoting — Request freight rates from all your connected carriers simultaneously by providing origin and destination details.
- Shipment Tracking — List all active shipments and fetch detailed tracking metadata including current transit status and estimated delivery.
- Carrier Oversight — Access and list all carriers connected to your account to maintain full visibility of your logistics partners.
- Logistics Directory — Access your saved address book and item catalog to facilitate faster and more accurate quoting.
- Webhook Integration — Monitor active webhooks configured for real-time status updates and automated logistics notifications.
- Account Metadata — Fetch detailed account attributes and contact information to maintain full context of your shipping operations.
- Audit & History — Retrieve historical quotes and shipment details for better cost analysis and reporting.
The Freightview MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex 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 Freightview to LlamaIndex via MCP
Follow these steps to integrate the Freightview MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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 12 tools from Freightview
Why Use LlamaIndex with the Freightview MCP Server
LlamaIndex provides unique advantages when paired with Freightview through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Freightview tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Freightview tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Freightview, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Freightview tools were called, what data was returned, and how it influenced the final answer
Freightview + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Freightview MCP Server delivers measurable value.
Hybrid search: combine Freightview real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Freightview to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Freightview for fresh data
Analytical workflows: chain Freightview queries with LlamaIndex's data connectors to build multi-source analytical reports
Freightview MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Freightview to LlamaIndex via MCP:
get_account_details
Get organization attributes
get_carrier_details
Get carrier info
get_quote_details
Get quote metadata
get_shipment_details
Get shipment metadata
list_address_book
List saved addresses
list_connected_carriers
List connected carriers
list_contacts
List logistics contacts
list_freight_quotes
List recent quotes
list_item_catalog
List commonly shipped items
list_shipments
List freight shipments
list_webhooks
List active webhooks
request_rates
Request freight rates
Example Prompts for Freightview in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Freightview immediately.
"List my 5 most recent shipments and their current transit status."
"Request freight rates from 60601 to 90210 for a standard pallet."
"Show me all carriers currently connected to my account."
Troubleshooting Freightview MCP Server with LlamaIndex
Common issues when connecting Freightview to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFreightview + LlamaIndex FAQ
Common questions about integrating Freightview MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Freightview with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Freightview to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.