How to Use the Elai AI Video MCP in LlamaIndex

Q: Can I index my Elai AI Video project history into LlamaIndex?

Yes. You can query listaivideos or listlatestvideoprojects and load the resulting metadata directly into a LlamaIndex document index for semantic search.

Q: How do I query available voices in LlamaIndex using Elai AI Video tools?

You run listavailablevoices through the MCP Server to fetch the supported languages, then use LlamaIndex's query engine to select the best match for your target audience.

Index your Elai AI Video assets and render status directly into LlamaIndex vector stores for context-aware automation.

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Connect Elai AI Video MCP to LlamaIndex

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Setup Elai AI Video with LlamaIndex

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Semantic search via MCP Server

Your LlamaIndex agent can run `list_ai_videos` and ingest the metadata of every video you have created. It embeds this information into your vector store, allowing you to run semantic queries to find past projects based on their scripts or settings. Instead of searching manually, you ask your agent which videos used a specific tone. The agent queries the index, retrieves the correct video ID, and checks its current status using `get_video_details` in seconds.

Grounding avatar selection in historical data

Avoid guessing which voice or avatar works best for a specific market. By indexing the outputs of `list_available_avatars` and `list_available_voices` into LlamaIndex, your agent matches the user's query intent against the actual capabilities of the Elai API. It matches the user's intent to pull the correct voice profile, passing those parameters straight to `create_new_ai_video` to generate the draft.

Build video generation RAG pipelines

This MCP Server allows LlamaIndex to combine external documents with video creation. Your pipeline can read a product manual, extract key steps, find a matching template with `list_video_templates`, and generate a training video. Once the draft is ready, the system automatically runs `trigger_video_rendering` and indexes the final output path from `list_successfully_rendered_videos` back into your document store for future queries.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Elai AI Video. 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 Elai AI Video MCP in LlamaIndex

Yes. You can query `list_ai_videos` or `list_latest_video_projects` and load the resulting metadata directly into a LlamaIndex document index for semantic search.

You run `list_available_voices` through the MCP Server to fetch the supported languages, then use LlamaIndex's query engine to select the best match for your target audience.

Yes. When a source document changes, your RAG pipeline can detect the update, call `create_new_ai_video`, and then call `trigger_video_rendering` to refresh the video asset.

You should decouple the rendering from the immediate query response. This lets you trigger the render, then index the video ID so a background task can check `get_video_details` later.

Yes. Your account details and keys are stored securely in Vinkius's zero-trust sandbox. The MCP Server only passes safe metadata from `get_elai_account_metadata` to LlamaIndex, keeping your master credentials hidden.

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