How to Use the Trakt MCP in AutoGen

Q: How do I use AutoGen to compare movie data with Trakt?

You can assign one agent to call getmovie and another agent to call getshow. They debate which content type is more relevant based on the initial user prompt, converging on a single, detailed answer.

Q: Can AutoGen manage personal data from Trakt?

Yes. Agents can be tasked with calling both getwatchlist and gethistory. The agents then debate the implications of comparing those two datasets to provide a better recommendation.

Q: What happens when multiple agents use Trakt for search?

If one agent uses search(type='movie') and another uses search(type='show'), they can debate the scope of the results. The final output is a comprehensive list covering all requested categories.

Debate complex requests with Trakt and AutoGen.

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Setup Trakt with AutoGen

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Consensus-Driven Content Research for AutoGen

Imagine an Agent debate: one agent calls `get_show` for basic details, while another uses `get_show_ratings` to check the average score. The final decision—the consensus answer—is a rich summary combining both pieces of data. This deliberation process is perfect when you need multiple perspectives (e.g., plot overview *and* critical reception) before answering.

Collaborative Data Gathering with Trakt MCP Server

Multiple AutoGen agents can work off the same goal. One agent might call `search` to find potential movies, and a second agent uses that list of IDs in `get_related_movies` to narrow down the best options. The system forces agents to negotiate the optimal set of tools and parameters.

Negotiating Viewing Data with AutoGen

You can set up a debate between two agents: Agent A checks `get_watchlist` for user intent, and Agent B checks `get_history` for actual viewing patterns. They argue over whether the user's current interest matches their past behavior. The result is a highly contextualized understanding of the user's needs.

Setup guide

Set up Trakt MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Trakt tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Trakt_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Trakt data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Trakt_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Trakt data")
    print(result.messages[-1].content)

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Common questions about Trakt MCP in AutoGen

You can assign one agent to call `get_movie` and another agent to call `get_show`. They debate which content type is more relevant based on the initial user prompt, converging on a single, detailed answer.

Yes. Agents can be tasked with calling both `get_watchlist` and `get_history`. The agents then debate the implications of comparing those two datasets to provide a better recommendation.

If one agent uses `search(type='movie')` and another uses `search(type='show')`, they can debate the scope of the results. The final output is a comprehensive list covering all requested categories.

Any time you need personal data, like watchlist or history, yes—you must provide an OAuth access token to the MCP Server. Agents will enforce this requirement.

The server touches authenticated user viewing records, specifically watch history and watchlist contents. These operations require passing an OAuth access token for security.

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