How to Use the Tolstoy MCP in AutoGen

Debate content strategy: Build multi-agent funnels with AutoGen and the MCP Server.

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Connect Tolstoy MCP to AutoGen

Create your Vinkius account to connect Tolstoy to AutoGen 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 Tolstoy with AutoGen

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Claude

Perplexity

Optimize Funnel Performance

You can set up a debate where two agents argue about performance. One agent runs `get_video_analytics` to gather data, while another reviews `list_interactive_projects`. The agents then discuss which project needs the most immediate optimization based on those conflicting reports.

Validate Content Deployment

The system can simulate a content review board. A 'Content Agent' runs `upload_video` and passes the new asset ID to an 'Audit Agent.' The Audit Agent then uses `list_videos` to confirm that the file was correctly added to the existing library before declaring the deployment successful.

Manage Webhook Strategy

AutoGen excels at consensus. You can have a dedicated 'Integration Agent' run through all potential endpoints using `list_webhooks`. Then, another agent challenges those findings by asking to check specific folder contents via `list_folders`, forcing the system to reconcile conflicting or missing data.

Setup guide

Set up Tolstoy 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 Tolstoy 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="Tolstoy_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Tolstoy 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="Tolstoy_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Tolstoy. 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 Tolstoy MCP in AutoGen

You build a debate over content strategy. For example, one agent might suggest creating a new path using `list_interactive_projects`, and another agent challenges that by checking if the necessary assets are available via `list_videos`.

The ideal scenario involves conflicting requirements. Use it when you need multiple agents to argue over whether a new video should be uploaded (`upload_video`) versus if an existing one needs modification, forcing a consensus.

Yes. You can set up the system so that one agent checks the available files using `list_videos`, while another agent reviews the necessary structure using `list_folders`. They must agree on the final list before proceeding.

Absolutely. The system can simulate a performance review meeting where one agent pulls metrics via `get_video_analytics`, and another agent uses those numbers to argue for budget allocation or feature changes.

The server handles video performance analytics, folder listings, and webhook configuration. The agents use these specific outputs—like `get_video_analytics` metrics or the results of `list_webhooks`—as evidence during their deliberation.

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