Freightview MCP Server for OpenAI Agents SDK 12 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Freightview through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Freightview Assistant",
instructions=(
"You help users interact with Freightview. "
"You have access to 12 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Freightview"
)
print(result.final_output)
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.
The OpenAI Agents SDK auto-discovers all 12 tools from Freightview through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Freightview, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Freightview MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 12 tools from Freightview
Why Use OpenAI Agents SDK with the Freightview MCP Server
OpenAI Agents SDK provides unique advantages when paired with Freightview through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Freightview + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Freightview MCP Server delivers measurable value.
Automated workflows: build agents that query Freightview, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Freightview, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Freightview tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Freightview to resolve tickets, look up records, and update statuses without human intervention
Freightview MCP Tools for OpenAI Agents SDK (12)
These 12 tools become available when you connect Freightview to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Freightview to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Freightview + OpenAI Agents SDK FAQ
Common questions about integrating Freightview MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
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 OpenAI Agents SDK
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.