How to Use the Focus Logística MCP in Mastra AI

Build resilient logistics workflows in Mastra AI with automatic retries and conditional branching for your transport documents.

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Connect Focus Logística MCP to Mastra AI

Create your Vinkius account to connect Focus Logística 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 Focus Logística with Mastra AI

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Automated transport document workflows in Mastra AI

Chain `emit_cte` and `emit_mdfe` into your Mastra agents. Define logic that checks status and retries automatically if the API returns a transient error. This prevents undelivered cargo caused by simple network blips. Your agents manage the lifecycle of the document from creation to closure.

Conditional document closure logic

Use `close_mdfe` as the final step in your agent workflow. You can gate this action based on validation checks performed by other tools. If the manifest data isn't ready, the agent skips the closure and alerts your team. It keeps your database aligned with the physical warehouse status.

Error handling for complex logistics

Implement custom retry strategies around `emit_cte`. If the API rejects the request, your agent can inspect the error and decide whether to retry or flag the record for human review. This keeps your operational queue clean. You don't have to manually verify every failed manifest entry.

Setup guide

Set up Focus Logística 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 Focus Logística 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: "focus-logistica-mcp-client",
  servers: {
    "focus-logistica-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Focus Logística MCP in Mastra AI

Add the server URL to your Mastra client configuration. The framework will automatically register the tools, allowing you to call them inside your agent definitions.

Yes, you can define exponential backoff policies within your workflow engine. This ensures that transient network issues don't stop your document emission.

Absolutely. You can link multiple tools together, such as consulting a status before deciding to cancel or close a document.

The workflow engine catches the error. You can then trigger a fallback agent or send a notification to your admin team.

All document payloads are processed in ephemeral memory. We use unique endpoint tokens so your logistics data never leaks between agents or sessions.

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