How to Use the ElevenLabs MCP in LlamaIndex

Q: How do I configure ElevenLabs as a LlamaIndex tool?

Install llama-index-tools-mcp and instantiate a BasicMCPClient. Wrap it with McpToolSpec and pass the resulting tools to your FunctionAgent.

Index your generated audio history and voice profiles directly into LlamaIndex.

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Connect ElevenLabs MCP to LlamaIndex

Create your Vinkius account to connect ElevenLabs to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ElevenLabs with LlamaIndex

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Build a searchable audio archive

LlamaIndex turns your past API interactions into queryable knowledge. By connecting this MCP Server, your setup can pull records using `list_audio_history` and `get_history_item`. You load metadata about every generated file straight into a vector store. You stop losing track of old generations. A user can ask your RAG application to find the audio file where the agent explained a specific topic. The system retrieves the exact `get_download_link` from the index instead of generating a duplicate file from scratch.

Ground decisions in LlamaIndex data

Your application needs context about available voices before it speaks. LlamaIndex can ingest the output of `list_voices` and `list_models`. When a prompt requires a specific accent or tone, the system searches the index to find the perfect voice ID. The agent also checks `get_voice_settings` to understand how a specific clone is tuned. By maintaining this information in your knowledge base, your RAG pipeline makes informed choices about audio generation without constantly pinging the external API.

Generate speech from retrieved context

Once LlamaIndex retrieves the right documents, it passes that exact text to the MCP `text_to_speech` tool. Your application reads out summaries, answers, or full reports using realistic synthetic voices. You get high-quality audio grounded strictly in your indexed data. Managing the aftermath is just as important. If a document gets purged from your vector store, your agent can call `delete_history_item` or `delete_voice` to remove the associated audio assets. You keep your storage and your remote account perfectly synced.

Setup guide

Set up ElevenLabs 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 ElevenLabs 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 ElevenLabs tools.",
)
response = await agent.run("List recent ElevenLabs 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 ElevenLabs. 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 ElevenLabs MCP in LlamaIndex

Install `llama-index-tools-mcp` and instantiate a `BasicMCPClient`. Wrap it with `McpToolSpec` and pass the resulting tools to your `FunctionAgent`.

Yes. You pull your records using the history tools and index the metadata. Your users can then query the RAG system to find specific past recordings.

Retrieving a past generation via a download link costs zero characters. You only spend your quota when calling the text to speech tool to create brand new audio.

The system indexes your available voices. When a user requests a specific style, the agent performs a semantic search across that indexed list to pick the matching ID.

Your RAG documents are sent to the external API to generate audio. Vinkius runs this connection inside an ephemeral container that destroys itself after the request. None of your source text or cloned voice data is logged by the infrastructure.

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