How to Use the Front MCP in Mastra AI

Q: How do I register Front MCP Server tools with Mastra AI?

Install @mastra/mcp and instantiate MCPClient. Call mcpClient.listTools() and spread those tools directly into your Mastra agent's tool configuration array.

Q: Can Mastra AI handle human-in-the-loop approvals for Front actions?

Yes. You can set the requireToolApproval flag on your Mastra workflow before executing replytoconversation to ensure a human reviews any draft before it goes to a client.

Q: Can I build a multi-step triage loop using Mastra agents?

Yes. You can design a loop that uses listconversations to find unassigned tickets, evaluates them, and then uses updateconversationstatus to assign them to specific team members.

Q: Where does Front message content go during agent execution?

Message content retrieved via getmessagecontent is processed locally within your Mastra runtime environment. No message bodies or customer threads are stored on external servers, keeping your customer communications private.

Build reliable multi-step workflows that trigger Front actions using Mastra AI agents.

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Connect Front MCP to Mastra AI

Create your Vinkius account to connect Front 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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Build Front Workflows with Mastra AI

The `update_conversation_status` tool changes thread states based on automated rules you define in your Mastra AI workflows. If a high-priority customer sends an email, your Mastra agent detects the sentiment and marks the conversation as open or urgent using this MCP Server. By chaining `get_contact_info`, the agent checks the sender's details first. If the contact isn't in your database, Mastra's workflow engine automatically branches to create a new profile before updating the thread.

Sync Contacts with Automatic Retries

The `list_team_contacts` tool retrieves your entire team directory to help agents route messages to the right account owner. Mastra handles API hiccups with this MCP configuration by automatically retrying this call with exponential backoff if the network drops. Once contacts are verified, the agent uses `get_conversation_details` to assign the thread. This ensures customer messages never sit unassigned due to temporary API timeouts.

Reliable Multi-Channel Messaging via MCP

The `reply_to_conversation` tool sends outgoing messages across SMS, email, or social channels. Mastra's workflow engine lets you set up conditional branches so that if an SMS dispatch fails, the agent automatically retries via email. You can run `list_active_channels` first to identify which platforms are online. This prevents your agents from attempting to send messages on disabled channels, saving API credits.

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-alternative-mcp-client",
  servers: {
    "front-alternative-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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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Front. 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 Front MCP in Mastra AI

Install `@mastra/mcp` and instantiate `MCPClient`. Call `mcpClient.listTools()` and spread those tools directly into your Mastra agent's tool configuration array.

Yes. You can set the `requireToolApproval` flag on your Mastra workflow before executing `reply_to_conversation` to ensure a human reviews any draft before it goes to a client.

Mastra's built-in workflow engine monitors transport health. If the connection to `get_api_status` fails, the agent pauses execution and retries according to your backoff policy.

Yes. You can design a loop that uses `list_conversations` to find unassigned tickets, evaluates them, and then uses `update_conversation_status` to assign them to specific team members.

Message content retrieved via `get_message_content` is processed locally within your Mastra runtime environment. No message bodies or customer threads are stored on external servers, keeping your customer communications private.

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