How to Use the Front MCP in Mastra AI

Q: Can a Mastra AI agent automatically archive old conversations in Front?

Yes. Set up a recurring workflow that uses searchconversations with a query like "is:open before:-7d". For each result, the agent can call updateconversationstatus to change its status to 'archived'.

Q: What's the process for routing new tickets from a specific Front inbox with Mastra AI?

Your agent can poll an inbox using listinboxthreads. For each new thread, it can use getconversationdetails to analyze the content and then updateconversationstatus to assign it to the correct teammate based on your logic. This all runs within a Mastra workflow.

Q: How do I get a list of all my contacts from Front?

Just have your agent call the listaddressbook tool. This will return your contacts, which you can then use in other workflow steps, like personalizing an automated reply.

Build resilient support workflows for Front with Mastra AI. Automate multi-step processes like ticket triage and follow-ups.

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Create Unbreakable Front Workflows

Mastra AI is built for processes that can't fail. You can chain tools from this MCP server to create a robust triage system. For example, have an agent use `list_inbox_threads` to find new conversations, then `get_conversation_details` to check for keywords. If an API call fails, Mastra's engine retries automatically. This means your workflow for routing a new ticket or sending a critical reply with `send_inbox_reply` won't just drop on the floor because of a temporary network hiccup.

Use Conditional Logic with Your Mastra AI MCP Server

This is where it gets powerful. Your agent can make decisions. After fetching messages with `list_conversation_messages`, it can decide if a reply is needed. If yes, it calls `send_inbox_reply`. If no, it uses `update_conversation_status` to archive the ticket. You can build complex logic branches. For instance: search for a VIP customer with `search_conversations`. If found, assign the ticket to a senior agent using `update_conversation_status`. If not, assign it to the general queue. Mastra handles the if/then flow.

Manage Teammates and Inboxes Programmatically

Go beyond just managing tickets. Your agent can get a list of all your support staff with `list_inbox_teammates`. This is useful for building automated assignment logic that accounts for who is actually available. You can also dynamically manage work across different teams. First, get all your inboxes with `list_shared_inboxes`. Then, based on the conversation content, your Mastra AI agent can decide which inbox is the right destination for a new issue.

Setup guide

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

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

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

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Common questions about Front MCP in Mastra AI

Create a workflow in Mastra AI that waits for a set time after a ticket is marked 'pending'. It can then use `get_conversation_details` to check the status. If it's still pending, the agent can send a reminder using `send_inbox_reply`.

Yes. Set up a recurring workflow that uses `search_conversations` with a query like `"is:open before:-7d"`. For each result, the agent can call `update_conversation_status` to change its status to 'archived'.

Your agent can poll an inbox using `list_inbox_threads`. For each new thread, it can use `get_conversation_details` to analyze the content and then `update_conversation_status` to assign it to the correct teammate based on your logic. This all runs within a Mastra workflow.

Just have your agent call the `list_address_book` tool. This will return your contacts, which you can then use in other workflow steps, like personalizing an automated reply.

Your Mastra AI agent will access only the specific Front data needed for the tools it runs, like conversation content, status, and assignee details. The MCP server handles the secure connection to Front, so your API token is never exposed in your workflow code. Data in transit is always encrypted.

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