How to Use the ContentGroove MCP in Mastra AI

Build reliable video workflows in Mastra AI with automated highlight generation.

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Connect ContentGroove MCP to Mastra AI

Create your Vinkius account to connect ContentGroove 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 ContentGroove with Mastra AI

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Chain video tasks in Mastra AI

Incorporate `create_media_from_url` into your Mastra agent workflows to trigger processing after an event occurs. You can set up retries if the network drops during the ingest. Use `get_media_status` as a conditional check in your pipeline. If the status returns as pending, your agent waits before attempting to fetch the finished highlights.

Automated clip management for Mastra AI

Call `list_all_clips` to get a batch of highlights for your agent to review or summarize. This makes it simple to feed video data into your LLM logic. If your agent needs specific metadata, `get_clip_details` provides the exact timing and source info. It's built for agents that need precise data to make decisions.

Scale media projects using Mastra AI

List existing projects with `list_media` to keep your agent aware of the available library. You can iterate through these projects to perform bulk reporting or cleanup. `get_media_details` gives you the full scope of a single project. This tool ensures your agent has the context needed to manage video assets without human intervention.

Setup guide

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

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

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

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Common questions about ContentGroove MCP in Mastra AI

Add the ContentGroove tools to your agent definition. Create a workflow that calls `create_media_from_url` followed by a wait-and-check loop using `get_media_status`.

Yes, you can configure your Mastra agent to catch errors from `create_direct_upload`. Implement an exponential backoff in your workflow to handle transient failures.

Absolutely. Just spread the tools from the MCP server into your agent's tool config. The framework handles the connection automatically.

Use the built-in workflow engine to branch after any tool call. If `get_media_details` returns an error, your agent can log the issue and alert an admin.

Data is isolated using per-request ephemeral tokens. Your video project identifiers are encrypted, ensuring only your authenticated agent can access your specific media assets.

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