Compatible with every major AI agent and IDE
What is the 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.
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
How it works
- Subscribe to this server
- Enter your Tome API Key
- Start generating presentations from Claude, Cursor, or any MCP-compatible client
No more manual slide creation. Your AI acts as a creative director, building out your vision page by page.
Who is this for?
- Content Creators — instantly scaffold stories and presentations without leaving your AI chat
- Marketing Teams — manage workspace assets and create new campaign decks via automation
- Product Managers — document features and roadmaps by generating tomes directly from project specs
Built-in capabilities (5)
Add a new page to an existing tome
Create a new tome in a workspace
Get detailed information about a specific tome
List tomes in a workspace
List Tome workspaces
Why LlamaIndex?
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.
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Data-first architecture: LlamaIndex agents combine Tome (AI Storytelling) tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain Tome (AI Storytelling) tool calls with transformations, filters, and re-rankers in a typed pipeline
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Multi-source reasoning: agents can query Tome (AI Storytelling), a vector store, and a SQL database in a single turn and synthesize results
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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) in LlamaIndex
Tome (AI Storytelling) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Tome (AI Storytelling) to LlamaIndex through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Tome (AI Storytelling) in LlamaIndex
The Tome (AI Storytelling) 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. All 5 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in LlamaIndex only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

* 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
How Vinkius secures
Tome (AI Storytelling) for LlamaIndex
Every tool call from LlamaIndex to the Tome (AI Storytelling) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I see the full structure of a specific presentation including its pages?
Yes! Use the get_tome tool with the specific Tome ID. Your agent will retrieve detailed information about the tome, including its page list and metadata.
How do I add a new slide or page to an existing project?
Simply use the add_page action. Provide the tome_id and the title for your new page, and the agent will insert it into your presentation immediately.
Can I list all the presentations I have in a specific workspace?
Yes, use the list_tomes tool. By providing the workspace_id, the agent will return a list of all tomes available in that specific environment.
How does LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query Tome (AI Storytelling) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp
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