How to Use the Spiritme MCP in Mastra AI

Q: How can I use Spiritme with the Mastra AI MCP Server?

You integrate the tools using mcpClient.listTools() into your agent's tool set. This lets your built-in workflow engine call functions like generatevideo when specific conditions are met in the pipeline.

Q: Does Spiritme support checking status in a workflow with Mastra AI?

Yes, you can use getjobstatus. If a video job gets stuck or fails halfway through, your Mastra AI workflow can check the status and automatically trigger a retry.

Q: If I build a Spiritme sequence with Mastra AI, how do I know what assets are available?

You use listtemplates or listvoices at the start of your workflow. This provides the necessary data foundation before the main generation step even begins.

Build complex, multi-step marketing workflows using the Mastra AI MCP Server.

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Setup Spiritme with Mastra AI

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Orchestrate Video Pipelines

The `generate_video` tool starts the whole process. You can build a workflow where an initial script is passed to `generate_audio`, and then that audio file feeds into another step using `generate_video`. This chaining capability handles complex, multi-step marketing content. Because it's built for Mastra AI, your workflow engine manages retries and failure handling automatically. If the video generation fails once, it can try again with different settings.

Manage Assets in Automated Workflows

When designing a complex process, you need reliable data sources. Use `list_templates` to pull down template details for your workflow engine. You also get avatar and voice lists via `list_avatars` or `list_voices`, which are critical inputs for the whole sequence. This means every step—from initial setup to final output—uses verifiable, current data.

Handle Multi-Stage Content Generation

Building a full campaign requires multiple steps. Your agent can first grab details with `get_avatar`, then use that ID in the main generation call (`generate_video`). This sequence ensures you're always using the correct, up-to-date asset data for your final product. It’s designed for complex logic: get assets -> generate content -> check status (`get_job_status`) -> finish.

Setup guide

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

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

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

You integrate the tools using `mcpClient.listTools()` into your agent's tool set. This lets your built-in workflow engine call functions like `generate_video` when specific conditions are met in the pipeline.

The server handles core content metadata: video files, template details (`get_template`), voice information, and avatar records. It's focused entirely on the marketing assets needed for your automated workflows.

Yes, you can use `get_job_status`. If a video job gets stuck or fails halfway through, your Mastra AI workflow can check the status and automatically trigger a retry.

You use `list_templates` or `list_voices` at the start of your workflow. This provides the necessary data foundation before the main generation step even begins.

This MCP Server manages structured marketing assets, including template definitions, multilingual voice profiles, and digital avatar identifiers. It's all metadata that drives content creation.

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