How to Use the Tome (AI Storytelling) MCP in AutoGen

Q: Does Tome (AI Storytelling) support listing multiple tomes at once?

The listtomes tool is available. You can build a debate where different agents argue over which subset of tomes needs attention, improving decision quality.

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Setup Tome (AI Storytelling) with AutoGen

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Perplexity

Multi-Agent Consensus for MCP Server Actions

Your agents don't just call `add_page`; they debate *how* to add it. A 'Content Agent' might propose the text, while a 'Fact-Check Agent' reviews the surrounding data from `get_tome` before consensus is reached and the page is added. This negotiation process ensures that every action taken via the MCP Server tool list is vetted by multiple perspectives.

Debating Tome (AI Storytelling) Structure

Need to decide if a new project should be a separate tome or part of an existing one? You can have agents debate this using `list_tomes` and `create_tome`. The system converges on the best structural decision, not just the first available option. This is critical for complex projects where the answer isn't obvious and requires internal deliberation.

MCP Server Tool Orchestration

The `list_workspaces` tool can initiate a debate: one agent might suggest listing all workspaces, while another argues they should narrow the scope first. The resulting consensus guides the final, executed API call. This allows you to build systems where multiple agents collaboratively plan and execute multi-step reasoning.

Setup guide

Set up Tome (AI Storytelling) 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 Tome (AI Storytelling) 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="Tome (AI Storytelling)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Tome (AI Storytelling) 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="Tome (AI Storytelling)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Tome (AI Storytelling) 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 Tome. 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 Tome (AI Storytelling) MCP in AutoGen

The MCP Server provides the tools for debate. Agents can discuss whether to `create_tome` or modify an existing one, converging on a definitive action that is then executed.

The server handles 'workspace IDs' and 'tome names/IDs'. These identifiers are used as the subject of debate among agents, forcing a consensus on the correct target.

Yes. The tool list is passed directly to your AssistantAgent constructor, allowing multiple roles—like a planning agent and an execution agent—to negotiate the necessary calls.

The `list_tomes` tool is available. You can build a debate where different agents argue over which subset of tomes needs attention, improving decision quality.

You set up your agents using the provided tool list and transport mechanism. The framework then manages the multi-agent conversation until a final decision is reached.

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