Tome (AI Storytelling) MCP Server for LlamaIndexGive LlamaIndex instant access to 5 tools to Add Page, Create Tome, Get Tome, and more
GDPR Free for SubscribersLlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Tome (AI Storytelling) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this MCP Server for LlamaIndex
The Tome (AI Storytelling) MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 5 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Tome (AI Storytelling). "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in Tome (AI Storytelling)?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Tome (AI Storytelling) MCP Server
Connect your Tome account to any AI agent to streamline your AI-driven storytelling and presentation workflows through natural conversation.
LlamaIndex agents combine Tome (AI Storytelling) tool responses with indexed documents for comprehensive, grounded answers. Connect 5 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Workspaces & Tomes — List all accessible workspaces and fetch active tomes directly from the Tome cloud
- Tome Management — Create new tomes in specific workspaces and organize your storytelling projects
- Page Operations — Add new pages to existing tomes to expand your narrative dynamically
- Deep Inspection — Fetch complete metadata and page details for specific tomes to understand their structure
The Tome (AI Storytelling) MCP Server exposes 5 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 5 Tome (AI Storytelling) tools available for LlamaIndex
When LlamaIndex connects to Tome (AI Storytelling) through Vinkius, your AI agent gets direct access to every tool listed below — spanning storytelling, presentations, ai-content, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Add page on Tome (AI Storytelling)
Add a new page to an existing tome
Create tome on Tome (AI Storytelling)
Create a new tome in a workspace
Get tome on Tome (AI Storytelling)
Get detailed information about a specific tome
List tomes on Tome (AI Storytelling)
List tomes in a workspace
List workspaces on Tome (AI Storytelling)
List Tome workspaces
Connect Tome (AI Storytelling) to LlamaIndex via MCP
Follow these steps to wire Tome (AI Storytelling) into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Tome (AI Storytelling) MCP Server
LlamaIndex provides unique advantages when paired with Tome (AI Storytelling) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Tome (AI Storytelling) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Tome (AI Storytelling) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Tome (AI Storytelling), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Tome (AI Storytelling) tools were called, what data was returned, and how it influenced the final answer
Tome (AI Storytelling) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Tome (AI Storytelling) MCP Server delivers measurable value.
Hybrid search: combine Tome (AI Storytelling) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Tome (AI Storytelling) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Tome (AI Storytelling) for fresh data
Analytical workflows: chain Tome (AI Storytelling) queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Tome (AI Storytelling) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Tome (AI Storytelling) immediately.
"List all my Tome workspaces."
"Create a new tome titled 'Product Roadmap 2025' in workspace ws_987."
"Add a page called 'Market Analysis' to tome tome_abc123."
Troubleshooting Tome (AI Storytelling) MCP Server with LlamaIndex
Common issues when connecting Tome (AI Storytelling) to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTome (AI Storytelling) + LlamaIndex FAQ
Common questions about integrating Tome (AI Storytelling) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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