How to Use the Intercom MCP in Mastra AI

Q: Can Mastra AI handle conditional branching based on Intercom segments?

Yes. You can write a step that calls listsegments, then branch your workflow based on whether the customer is in a high-priority tier or a free trial.

Q: How does Mastra AI recover from Intercom API failures during replies?

If replytoconversation fails due to a network glitch, Mastra uses its built-in exponential backoff to retry the call. This prevents silent failures in your automated support channels.

Build resilient support workflows that sync Intercom contacts and trigger automated replies using Mastra AI.

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Connect Intercom MCP to Mastra AI

Create your Vinkius account to connect Intercom 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 Intercom with Mastra AI

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Mastra AI MCP Server routing

Support operations break when APIs fail, which is why this integration uses `search_contacts` to find and route accounts safely. This MCP Server gives Mastra AI the tools to search accounts and route them with built-in retry logic. If an API call hits a rate limit, the framework backs off and retries automatically. Your support workflows keep running without dropping messages or losing customer context.

Automated segment triage

Let your agents categorize users dynamically by calling `list_segments` to find where a customer belongs. The agent uses the segment data to update details using `get_contact_details` to keep your CRM accurate. You define the rules in TypeScript, and Mastra handles the execution path. If a high-value customer sends a message, the workflow escalates the conversation to your team immediately.

Self-healing auto-replies

Write workflows that answer common tickets by grabbing support articles with `list_help_articles`. Use the MCP client to draft answers and send them using `reply_to_conversation`. If the customer replies with more questions, the workflow branches. It can either try a different article or hand the ticket off to a human admin by running `list_admins`.

Setup guide

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

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

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

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Common questions about Intercom MCP in Mastra AI

Instantiate the client and pass the server URL to your Mastra agent configuration. The agent instantly gets access to tools like `create_conversation` and `reply_to_conversation`.

Yes. You can write a step that calls `list_segments`, then branch your workflow based on whether the customer is in a high-priority tier or a free trial.

If `reply_to_conversation` fails due to a network glitch, Mastra uses its built-in exponential backoff to retry the call. This prevents silent failures in your automated support channels.

Yes. You can configure the framework to pause before calling `reply_to_conversation`. An admin can review the drafted response before it goes live to the customer.

All conversation transcripts and customer profiles are processed in memory within secure V8 isolates. This MCP server runs your code without writing logs or customer records to persistent storage.

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