How to Use the Trakt MCP in Mastra AI

Q: How do I search for content using Trakt with Mastra AI?

To start, use the search tool. It returns titles, years, ratings, genres, and synopses across different types like movies or shows. Mastra AI can manage these calls within a larger workflow if you need to chain multiple searches.

Q: Can Trakt track user watchlists with Mastra AI?

Yes, the getwatchlist tool retrieves all movies or shows added by an authenticated user. Since Mastra AI supports automatic retries, your workflow is more reliable when fetching sensitive watchlist data.

Q: Does Trakt provide enough detail for a TV show using Mastra AI?

The getshow tool gives you basics like runtime and status. For episodes, use getshowepisodes, which provides episode numbers, titles, and first aired dates—all manageable within your Mastra AI workflow.

Q: What if I need to know who directed a movie with Trakt and Mastra AI?

You'd use getmoviepeople for that. It returns name, Trakt ID, character name (if cast), and job title (for crew). You can build a failure-tolerant workflow in Mastra AI to ensure you get this critical data.

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Automating Content Retrieval from Trakt

The `search` tool lets your agent pull titles, years, ratings, genres, and synopses for movies, shows, episodes, people, or lists. You can pass the `type` parameter to narrow down results immediately. Mastra AI is built for complex workflows. Use this capability when you need a multi-step process—like searching for related content across different media types.

Handling Deep Trakt Media Metadata

When you call `get_movie` or `get_show`, your agent gets a full breakdown of content, including runtime, rating, genre, and poster URLs. Mastra AI ensures that if the initial data fetch fails, it automatically retries the request. This robust handling is perfect for production systems where reliable metadata retrieval from Trakt is non-negotiable.

Analyzing User History with Trakt MCP Server

Use `get_history` or `get_watched` to pull a user's watch history, optionally filtering by type and ID. This requires an OAuth access token. Mastra AI handles the complexity of these authenticated calls. You can build a workflow that first checks if the data exists and then processes it, even if the initial API call fails.

Setup guide

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

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

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

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Common questions about Trakt MCP in Mastra AI

To start, use the `search` tool. It returns titles, years, ratings, genres, and synopses across different types like movies or shows. Mastra AI can manage these calls within a larger workflow if you need to chain multiple searches.

Yes, the `get_watchlist` tool retrieves all movies or shows added by an authenticated user. Since Mastra AI supports automatic retries, your workflow is more reliable when fetching sensitive watchlist data.

The `get_show` tool gives you basics like runtime and status. For episodes, use `get_show_episodes`, which provides episode numbers, titles, and first aired dates—all manageable within your Mastra AI workflow.

You'd use `get_movie_people` for that. It returns name, Trakt ID, character name (if cast), and job title (for crew). You can build a failure-tolerant workflow in Mastra AI to ensure you get this critical data.

The server touches sensitive viewing history and watchlist items. When building workflows, treat the OAuth access token used for `get_watched` or `get_watchlist` as highly confidential credentials.

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