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

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

The listtomes tool is available. By indexing its output, you allow your agent to search for patterns or specific titles across all known presentations.

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Connect Tome (AI Storytelling) MCP to LlamaIndex

Create your Vinkius account to connect Tome (AI Storytelling) to LlamaIndex 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 LlamaIndex

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Indexing Presentation Metadata via MCP Server

Don't just query tools; index them. When you use `list_workspaces` or `get_tome`, LlamaIndex captures the returned data and indexes it into your vector store. Now, you can ask questions like, 'Which tome was created last week?' and get a grounded answer. This capability turns live API calls from the MCP Server into searchable knowledge, eliminating hallucinations when combining structured data with documents.

RAG for Tome (AI Storytelling) Data

You can combine your internal documents with the output of `add_page`. If you're writing a report on product specs and want to add a page referencing those specs, LlamaIndex indexes both sources. The final answer is grounded in both your text and the Tome data. This RAG setup makes sure that when the agent writes content, it cites verifiable information from the MCP Server results.

Querying Tome (AI Storytelling) via LlamaIndex

Instead of just executing a tool call like `list_tomes`, you ask LlamaIndex to query the *results* of that call. If your index contains old workspace lists, you can retrieve historical data about which presentations existed in past sessions. This allows developers to build robust applications that remember context and configurations over time.

Setup guide

Set up Tome (AI Storytelling) 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 Tome (AI Storytelling) 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 Tome (AI Storytelling) tools.",
)
response = await agent.run("List recent Tome (AI Storytelling) data")

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Common questions about Tome (AI Storytelling) MCP in LlamaIndex

LlamaIndex treats the MCP Server's output as source data. When you use `create_tome`, that resulting tome ID and metadata become searchable chunks in your vector index.

The server handles 'workspace IDs' and 'tome names/IDs'. LlamaIndex indexes these specific identifiers, making them available for semantic search across your knowledge base.

Yes. You pass the MCP Server tools to FunctionAgent, allowing you to build applications where live data from the server and documents are combined into one queryable index.

The `list_tomes` tool is available. By indexing its output, you allow your agent to search for patterns or specific titles across all known presentations.

You initialize the BasicMCPClient and pass the tool specifications. Then you build your RAG application around those tools, allowing live API data to ground your search results.

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