How to Use the Freightview MCP in Mastra AI

Q: Can Mastra AI handle Freightview webhook configurations?

Absolutely. Your agent can call listwebhooks to verify active listeners. If a required endpoint is missing, Mastra can branch the workflow to alert your ops team.

Q: Does the Freightview MCP integration support Mastra's auto-retries?

Yes. If a call to getquotedetails fails due to a carrier timeout, the framework's workflow engine catches it and retries based on your configured backoff settings.

Automate complex freight quoting and tracking workflows with automatic retries using Mastra AI.

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Connect Freightview MCP to Mastra AI

Create your Vinkius account to connect Freightview to Mastra AI 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 Mastra AI

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Perplexity

Automate LTL Quoting in Mastra AI

Freight APIs fail constantly. Building an MCP workflow in Mastra AI means you get built-in exponential backoff when `request_rates` hits a rate limit or times out. Your agent pulls the product dimensions using `list_item_catalog`, requests the rates, and filters the results via `list_freight_quotes`. If a carrier throws an error, the workflow automatically retries without you writing custom loop logic.

Branching Logic for Shipment Failures

You need a system that reacts when things go wrong on the dock. Your Mastra workflow can poll `get_shipment_details` and branch conditionally based on the transit status. If a load is delayed, the agent automatically triggers `list_contacts` to find the warehouse manager, then sends an alert. You build resilient logistics pipelines that actually handle edge cases.

Human-in-the-Loop Carrier Selection

You probably don't want an AI booking a ten-thousand-dollar freight load completely unsupervised. This MCP Server integrates directly with Mastra's requireToolApproval feature. The agent gathers options using `list_connected_carriers` and prepares the booking. Before it finalizes anything, execution pauses. A human reviews the choice, clicks approve, and the workflow resumes.

Setup guide

Set up Freightview MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Freightview tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "freightview-mcp-client",
  servers: {
    "freightview-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Freightview Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Freightview tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Freightview transactions"
);
console.log(result.text);

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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 Mastra AI

You initialize an MCPClient with your Vinkius URL. Then you call mcpClient.listTools() and spread that array directly into your Mastra Agent's tool configuration.

Absolutely. Your agent can call list_webhooks to verify active listeners. If a required endpoint is missing, Mastra can branch the workflow to alert your ops team.

Yes. If a call to get_quote_details fails due to a carrier timeout, the framework's workflow engine catches it and retries based on your configured backoff settings.

Turn on requireToolApproval in your Mastra agent config. The execution will halt before executing the final booking step, waiting for manual human confirmation.

The server operates inside a zero-trust sandbox. When your workflow pulls logistics personnel info via list_contacts, the data is processed strictly in memory and wiped the second the session ends.

Use it with your favorite AI tools

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