Tolstoy MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Get Video Analytics, List Folders, List Interactive Projects, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Tolstoy 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 App Connector for LlamaIndex
The Tolstoy app connector for LlamaIndex is a standout in the Ecommerce category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import 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 Tolstoy. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Tolstoy?"
)
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 Tolstoy MCP Server
Connect your Tolstoy interactive video account to any AI agent and simplify how you build personalized video experiences, manage your media library, and track engagement through natural conversation.
LlamaIndex agents combine Tolstoy tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Video Management — List all uploaded videos and programmatically import new content via external URLs.
- Interactive Projects — Query and manage your branching video flows and personalized interactive experiences.
- Performance Tracking — Retrieve detailed analytics including plays, conversion rates, and revenue impact for your videos.
- Media Organization — List and oversee video folders to keep your marketing assets structured.
- Event Monitoring — List configured webhooks to ensure your real-time video notifications are active.
- Engagement Insights — Fetch high-level summaries of how users are interacting with your video content.
The Tolstoy MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 6 Tolstoy tools available for LlamaIndex
When LlamaIndex connects to Tolstoy through Vinkius, your AI agent gets direct access to every tool listed below — spanning interactive-video, shoppable-video, video-marketing, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Get performance metrics
List video folders
List interactive video projects
List your Tolstoy videos
List configured webhooks
Upload a new video to Tolstoy
Connect Tolstoy to LlamaIndex via MCP
Follow these steps to wire Tolstoy into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the 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 Tolstoy MCP Server
LlamaIndex provides unique advantages when paired with Tolstoy through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Tolstoy tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Tolstoy tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Tolstoy, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Tolstoy tools were called, what data was returned, and how it influenced the final answer
Tolstoy + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Tolstoy MCP Server delivers measurable value.
Hybrid search: combine Tolstoy real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Tolstoy 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 Tolstoy for fresh data
Analytical workflows: chain Tolstoy queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Tolstoy in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Tolstoy immediately.
"List all videos in my Tolstoy library."
"Show me the analytics summary for my videos."
"Upload this video to Tolstoy: 'https://vinkius.com/intro.mp4' with name 'Introduction v2'."
Troubleshooting Tolstoy MCP Server with LlamaIndex
Common issues when connecting Tolstoy to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTolstoy + LlamaIndex FAQ
Common questions about integrating Tolstoy MCP Server with LlamaIndex.