How to Use the CAMB.AI MCP in Mastra AI

Q: How does a Mastra AI agent list available CAMB.AI voices before cloning?

It calls the listclonedvoices tool. You can build a workflow where the agent first checks the output of this tool to see if a suitable voice already exists. If not, it proceeds to call createvoiceclone.

Build resilient, multi-step audio processing workflows for your backend with Mastra AI and this MCP server.

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Connect CAMB.AI MCP to Mastra AI

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Setup CAMB.AI with Mastra AI

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Automate Content Localization

Build a workflow that automatically dubs new media. A Mastra agent can be triggered when a new file appears, at which point it calls `create_dubbing` to start the translation process. It's a fire-and-forget setup. Mastra's workflow engine really shines here. You can add a conditional step that polls `get_job_status`. If the job succeeds, the agent moves the file to a CDN. If it fails, Mastra's automatic retry logic kicks in before notifying an admin.

Run Batch TTS Jobs Reliably

Your agent can process a whole queue of text-to-speech requests. It just loops through your records, calling `create_tts` on each one and storing the task IDs. This is perfect for generating audio for articles or product descriptions in bulk. If the CAMB.AI API has a temporary issue, you don't have to care. Mastra AI's exponential backoff feature handles the retries for you. Your agent can then run a separate task to check `get_tts_status` and collect all the finished audio files with `get_tts_result`.

Manage Custom Voices as Code

A Mastra AI agent can own the entire lifecycle of your custom voices. You can create a workflow that uses `create_voice_clone` to add a new voice when a new brand is onboarded, all without manual steps. This MCP Server gives your agent direct control over the voice assets. The agent can use `list_cloned_voices` to audit existing voices or check if a specific voice is ready before starting a new batch of TTS jobs. It turns voice management into a programmable, repeatable process.

Setup guide

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

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

const result = await agent.generate(
  "List recent CAMB.AI 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 CAMB.AI. 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 CAMB.AI MCP in Mastra AI

You define the logic in your Mastra workflow. After calling `create_dubbing`, use a loop with `get_job_status`. If the status becomes 'failed', you can use Mastra's built-in retry mechanism or trigger a different branch of your workflow to log the error and notify a team.

Yes. The agent can iterate through a list of text inputs and call the `create_tts` tool for each one. Because the tool is asynchronous, your agent can fire off all the requests quickly and then poll for their results later.

It calls the `list_cloned_voices` tool. You can build a workflow where the agent first checks the output of this tool to see if a suitable voice already exists. If not, it proceeds to call `create_voice_clone`.

No, it's simple. Vinkius gives you a single URL with an embedded token. You just pass that URL when you configure the MCP client in your Mastra AI agent definition. There's no OAuth dance to manage.

Vinkius provides a zero-trust environment for every request. The audio data for your dubbing or TTS job is sent through a secure endpoint and processed in an isolated, ephemeral container. The data exists only for the duration of the task.

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