How to Use the Tavus Video Replicas MCP in LlamaIndex

Q: How do I track the status of a video job using Tavus Video Replicas with LlamaIndex?

After calling generatereplicavideo, capture the resulting job ID. Index this initial job request alongside subsequent calls to getvideodetails so that future queries can retrieve the current generation metadata.

Index Tavus Video Replicas data for LlamaIndex's advanced knowledge base.

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Setup Tavus Video Replicas with LlamaIndex

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Query Replica Information

Need to know the specs of a voice? Use `get_replica_details` and index that output into your vector store. You can then query past sessions, asking questions like 'What was the emotional range captured by this replica?' LlamaIndex treats every tool output—the rich JSON from `get_replica_details`—as searchable knowledge, grounding your answers in actual API data.

Search Video History

Don't rely on memory. Use `list_generated_videos` to pull a list of all content made. Indexing this list lets you query the video metadata semantically, such as 'Show me videos generated using Replica X for marketing purposes.' It’s not just fetching; it’s creating a unified, queryable index across your entire library.

Track and Validate Video Jobs

When you run `generate_replica_video`, the resulting job ID is crucial. Indexing the initial request details allows later queries to verify if the generation was successful or stalled. You can combine this with `get_video_details` results in your knowledge base. This means your RAG applications don't hallucinate; they answer based on concrete, indexed API evidence.

Setup guide

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

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Common questions about Tavus Video Replicas MCP in LlamaIndex

After calling `generate_replica_video`, capture the resulting job ID. Index this initial job request alongside subsequent calls to `get_video_details` so that future queries can retrieve the current generation metadata.

Yes. You index both your source documents and the output from tools like `list_replicas`. Your RAG application can then synthesize an answer that references both a document paragraph and specific replica details.

The best way is to index all historical tool outputs. Combine results from `list_generated_videos` and `get_video_details` into a single knowledge corpus for advanced semantic querying.

You query the index using the video ID. The system searches through the indexed `get_video_details` records and returns the associated replica name or ID, grounding the answer in the API data.

The server manages video job metadata, including script IDs and generation status. This specific generation metadata is what you must index for secure retrieval.

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