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

Q: Can I have an agent team research and list available projects using Tome (AI Storytelling) and CrewAI?

Yep. You set up a sequence where Agent A calls listworkspaces to scope the project, and then passes that result to Agent B for analysis.

Q: Is there a way to get an overview of all Tomes in my workspace with Tome (AI Storytelling) and CrewAI?

Simply task one agent with calling listtomes. This gives the entire team a clean, comprehensive list they can then use for further action or decision-making.

Automate Tome (AI Storytelling) content generation with specialized agents using CrewAI.

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Connect Tome (AI Storytelling) MCP to CrewAI

Create your Vinkius account to connect Tome (AI Storytelling) to CrewAI 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 Tome (AI Storytelling) with CrewAI

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Perplexity

Collaborative Content Creation via MCP Server

You can assign one agent to handle the structure and another to manage the details. Agent A uses `list_workspaces` to map out available projects, and then passes the list to Agent B for further action.

Specialized Project Metadata Retrieval with CrewAI

Don't just call a function; make agents cooperate. One agent calls `get_tome` to get deep details, while another monitors the output and validates that the data structure is correct.

Autonomous Project Lifecycle Management with MCP Server

The team can manage the entire lifecycle. Agent A runs a check using `list_tomes`, determines if new content is needed, and then directs Agent B to use `add_page`.

Setup guide

Set up Tome (AI Storytelling) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Tome (AI Storytelling) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Tome (AI Storytelling) Analyst",
    goal="Access and analyze Tome (AI Storytelling) data via MCP.",
    backstory="Expert analyst with direct Tome (AI Storytelling) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Tome (AI Storytelling) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Tome (AI Storytelling) Analyst",
        goal="Access and analyze Tome (AI Storytelling) data via MCP.",
        backstory="Expert analyst with direct Tome (AI Storytelling) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Tome (AI Storytelling) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

You assign roles. One specialized agent handles the initial setup by calling `create_tome`. A monitor agent then watches that process, ensuring the new project is ready for follow-up tasks.

Yep. You set up a sequence where Agent A calls `list_workspaces` to scope the project, and then passes that result to Agent B for analysis.

You designate a specialized 'Editor' agent. This agent uses `add_page` or `get_tome` based on the instructions, keeping all changes traceable within the crew's shared memory.

Simply task one agent with calling `list_tomes`. This gives the entire team a clean, comprehensive list they can then use for further action or decision-making.

This server touches Tome workspaces and individual tomes. These are the specific content records that your collaborative agents read, write, and share information about.

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