How to Use the Mio MCP in Mastra AI

Q: Does Mio allow conditional branching in Mastra AI?

Yes. By evaluating the output of getcallsummary, your agent can make decisions on whether to retry a call or notify a human.

Build automated voice workflows in Mastra AI by connecting your communication stacks.

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Connect Mio MCP to Mastra AI

Create your Vinkius account to connect Mio 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 Mio with Mastra AI

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Automate call sequences with Mastra AI

Construct multi-step agents that check call history before acting. Use `list_calls` to pull recent logs and `get_call_details` to determine if a follow-up action is required. This approach lets your workflow engine decide the next move. If a call summary indicates a missing requirement, the agent triggers the next logical step automatically.

Manage communication events with webhooks

Configure your agent to handle incoming signals using `create_webhook`. When a new event arrives, your Mastra AI workflow activates, processes the data, and updates your records. Use `list_webhooks` to verify current listeners and `delete_webhook` to clean up old triggers. This keeps your event-driven architecture lean and efficient.

Control AI voice sessions

Deploy complex agents that start and stop calls based on your own business logic. `start_ai_call` allows your agent to reach out to contacts, while `terminate_call` ensures sessions end when the objective is met. Your workflow handles the conditions for these triggers. You define the rules, and the agent executes the calls.

Setup guide

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

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

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

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Common questions about Mio MCP in Mastra AI

Add the Mio server URL to your Mastra agent configuration. You then spread the available tools into your agent definition to grant it call control.

Yes. By evaluating the output of `get_call_summary`, your agent can make decisions on whether to retry a call or notify a human.

The workflow engine manages retries natively. If a tool call fails, you can set backoff policies to try the operation again.

Your endpoint tokens are handled through secure environment variables. The server interacts only with the specific calls authorized by your agent.

The server interacts with call logs and transcript text. All processed data remains ephemeral and is not cached by the server.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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