How to Use the Trakt MCP in LlamaIndex

Q: How does LlamaIndex help me query Trakt for related content?

After calling getmovie, you feed the resulting metadata into LlamaIndex. This makes the connection between a movie and its related titles searchable, allowing you to ask complex questions about content relationships.

Q: Can LlamaIndex handle user watch history with Trakt?

Yes. You run gethistory via the MCP Server, and then index the resulting list of IDs and timestamps. This lets you ask questions like, 'What did I watch right before I started watching X?' based on your indexed data.

Building RAG applications with Trakt and LlamaIndex.

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Setup Trakt with LlamaIndex

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Indexing Movie Metadata for LlamaIndex

The `get_movie` tool retrieves titles, years, overviews, and genres. By indexing this output into a vector store, you create a knowledge base that goes beyond simple retrieval. Later queries can semantically search across thousands of movie records—you're answering 'What kind of movies are like X?' rather than just asking for IDs.

Semantic Search with Trakt and MCP Server

Use the `search` tool to gather initial results, then index those result summaries. You can query past API data—like finding 'all comedy shows from the 2010s'—and get answers grounded in actual Trakt records. The combination of live API calls and indexed documents creates a highly reliable knowledge graph.

Deep Analysis using LlamaIndex

You can use `get_show` to pull full details on a TV show, including network and certification. Indexing this allows your RAG application to answer complex questions like, 'List all high-rated dramas from the ABC Network.' The MCP Server data is permanently searchable in your unified index.

Setup guide

Set up Trakt MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Trakt MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Trakt tools.",
)
response = await agent.run("List recent Trakt data")

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Common questions about Trakt MCP in LlamaIndex

After calling `get_movie`, you feed the resulting metadata into LlamaIndex. This makes the connection between a movie and its related titles searchable, allowing you to ask complex questions about content relationships.

You can index virtually anything: show overviews, episode details from `get_show_episodes`, or even full cast/crew lists from `get_movie_people`. The goal is to make that raw API data permanent and queryable.

Yes. You run `get_history` via the MCP Server, and then index the resulting list of IDs and timestamps. This lets you ask questions like, 'What did I watch right before I started watching X?' based on your indexed data.

Since the MCP Server output is written directly to the index, all answers are grounded in actual API data. You never rely on hallucinations; you use verified records.

The server touches personal viewing metadata, including watch history and watchlist items. These operations require an OAuth access token to ensure secure data retrieval.

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