How to integrate Elevenlabs MCP with LlamaIndex

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Introduction

This guide walks you through connecting Elevenlabs to LlamaIndex using the Composio tool router. By the end, you'll have a working Elevenlabs agent that can convert this chapter text to audio, create a custom project for my audiobook, add a new pronunciation rule for this word, clone my voice using uploaded audio samples through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a Elevenlabs account through Composio's Elevenlabs MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Elevenlabs
  • Connect LlamaIndex to the Elevenlabs MCP server
  • Build a Elevenlabs-powered agent using LlamaIndex
  • Interact with Elevenlabs through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

What is the Elevenlabs MCP server, and what's possible with it?

The Elevenlabs MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Elevenlabs account. It provides structured and secure access to your voice synthesis projects and tools, so your agent can perform actions like generating audio from text, managing custom voices, organizing projects, and fine-tuning pronunciation on your behalf.

  • Project and chapter audio conversion: Instantly convert text content from chapters or entire projects into high-quality, natural-sounding audio files.
  • Custom voice creation and management: Guide your agent to add, finalize, or share custom voices—either by uploading new samples or assembling voices from existing data.
  • Pronunciation dictionary and rule management: Improve the accuracy of speech outputs by adding pronunciation dictionaries or custom pronunciation rules directly from files or specific aliases/phonemes.
  • Project organization and automation: Let your agent create new projects, add or remove chapters, and automate speech synthesis workflows for audiobooks, podcasts, or media production.
  • Embeddable audio player generation: Enable your agent to generate AudioNative projects, creating customizable and embeddable audio players from your content with just a prompt.

Supported Tools & Triggers

Tools
Add a pronunciation dictionary from fileAdds a new pronunciation dictionary from a lexicon file to improve speech synthesis accuracy.
Add new project with attributesUse to create a new elevenlabs project for text-to-speech synthesis (e.
Add rules to the pronunciation dictionaryAdds one or more custom pronunciation rules (alias or phoneme) to an existing pronunciation dictionary.
Add sharing voiceAdds an existing, shareable voice to a specified user's elevenlabs account library under a new custom name, requiring the user's public id and the voice id.
Add a voiceAdds a custom voice, requiring a `name` and a `files` list with at least one audio sample, to initiate cloning; returns `voice id` but voice is not immediately usable for synthesis.
Convert chapter to audioConverts the textual content of a chapter, identified by `chapter id` within a `project id`, into audio format.
Convert a projectConverts an existing elevenlabs studio project, including all its chapters and using its configured settings and voices, into speech.
Create a previously generated voiceFinalizes the creation of a voice using its `generated voice id` from a previous generation step by assigning a name, description, and optional labels.
Create an AudioNative enabled projectCreates an elevenlabs audionative project, generating an embeddable audio player from a provided content file using text-to-speech, allowing customization of player appearance, audio settings, and conversion options.
Delete chapter from projectIrreversibly deletes a specific, existing chapter from an existing project, typically to remove unwanted or obsolete content.
Delete a dubbing projectPermanently deletes a dubbing project by its id; this action is irreversible and the project cannot be recovered.
Delete history itemPermanently deletes a specific history item (including its audio file and metadata) using its `history item id`; this operation is irreversible and should be used with caution.
Delete project by idUse to irreversibly delete a specific project by its `project id`; the project must exist and be accessible, and this action cannot be undone.
Delete voice samplePermanently deletes a specific voice sample for a given voice id; this action is irreversible.
Delete voice by idPermanently and irreversibly deletes a specific custom voice using its `voice id`; the voice must exist and the authenticated user must have permission to delete it.
Download history itemsDownloads audio clips from history by id(s), returning a single file or a zip archive, with an optional output format (e.
Dub a video or an audio fileDub a video or audio file into a specified target language, requiring 'file' or 'source url', 'target lang', and 'csv file' if 'mode' is 'manual'.
Edit voiceUpdates the name, audio files, description, or labels for an existing voice model specified by `voice id`.
Edit voice settingsEdits key voice settings (e.
Text to speechConverts text to speech using a specified elevenlabs voice and model, returning a downloadable audio file.
Text to speech streamConverts text to a spoken audio stream, allowing latency optimization, specific output formats (some tier-dependent), and custom pronunciations; ensure the chosen model supports text-to-speech and text is preferably under 5000 characters.
Generate a random voiceGenerates a unique, random elevenlabs text-to-speech voice based on input text and specified voice characteristics.
Get user profile by handleRetrieves the public profile information for an existing elevenlabs user based on their unique handle.
Get audio from history itemRetrieves the audio content for a specific history item from elevenlabs, using a `history item id` that must correspond to a previously generated audio.
Get sample audioRetrieves the audio for a given `sample id` that must belong to the specified `voice id`.
Get chapter by IDFetches comprehensive details for a specific chapter within a given project, including its metadata (name, id), conversion status, progress, download availability, and content statistics.
Get chapters by project idRetrieves a list of all chapters, their details, and conversion status for a project, useful for managing content or tracking progress.
Get chapter snapshotsRetrieves all saved version snapshots for a specific chapter within a given project, enabling review of its history or reversion to prior states.
Get default voice settingsRetrieves the elevenlabs text-to-speech service's default voice settings (stability, similarity boost, style, speaker boost) that are applied when no voice-specific or request-specific settings are provided.
Get dubbed audio for a languageRetrieves an existing dubbed audio file for a specific `dubbing id` and `language code`.
Get dubbing project metadataRetrieves metadata and status for a specific dubbing project by its id.
Get generated itemsRetrieves metadata for a list of generated audio items from history, supporting pagination and optional filtering by voice id.
Get history item by idRetrieves detailed information (excluding the audio file) for a specific audio generation history item from elevenlabs, using its unique id.
Get pronunciation dictionary metadataRetrieves metadata for a specific, existing pronunciation dictionary from elevenlabs using its id.
Get modelsRetrieves a detailed list of all available elevenlabs text-to-speech (tts) models and their capabilities.
Get project by IDUse to retrieve all details for a specific project, including its chapters and their conversion statuses, by providing the project's unique id.
Get projectsFetches a list of all projects and their details associated with the user's elevenlabs account; this is a read-only operation.
Get project snapshotsRetrieves all available snapshots (saved states or versions) for an existing project, enabling history tracking, version comparison, or accessing specific states for playback/processing, particularly in text-to-speech workflows.
Get pronunciation dictionariesRetrieves a paginated list of pronunciation dictionaries, used to customize how specific words or phrases are pronounced by the text-to-speech (tts) engine.
Get pronunciation dictionary versionDownloads the pronunciation lexicon specification (pls) file for an existing version of a pronunciation dictionary from elevenlabs, used to customize tts pronunciation.
Get shared voicesRetrieves a paginated and filterable list of shared voices from the elevenlabs voice library.
Get sso provider adminRetrieves the sso provider configuration for a specified workspace, typically for review purposes, and will indicate if no configuration exists.
Get dubbing transcript by languageRetrieves the textual transcript for a specified dubbing project and language, if one exists for that language in the project.
Get user infoRetrieves detailed information about the authenticated elevenlabs user's account, including subscription, usage, api key, and status.
Get user subscription infoRetrieves detailed subscription information for the currently authenticated elevenlabs user.
Get voiceRetrieves comprehensive details for a specific, existing voice by its `voice id`, optionally including its settings.
Get voices listRetrieves a list of all available voices along with their detailed attributes and settings.
Get voice settingsRetrieves the stability, similarity, style, and speaker boost settings for a specific, existing elevenlabs voice using its `voice id`.
Get API documentationRetrieves the content of the official elevenlabs api documentation page hosted on mintlify.
Remove rules from pronunciation dictionaryPermanently removes exact-match pronunciation rules from a specified elevenlabs pronunciation dictionary using a list of rule strings; non-matching rule strings are ignored and this action cannot add or modify rules.
Speech to speechConverts an input audio file to speech using a specified voice; if a `model id` is provided, it must support speech-to-speech conversion.
Speech to speech streamingConverts an input audio stream to a different voice output stream in real-time, using a specified speech-to-speech model.
Stream chapter audioStreams the audio for a specified chapter snapshot from an elevenlabs project, optionally converting the output to mpeg format.
Stream project audioStreams audio from a specific project snapshot, optionally converting it to mpeg format.
Archive project snapshotArchives an existing project snapshot by its id, creating a permanent, immutable, and typically irreversible copy of its state.
Update project pronunciation dictionariesUpdates a project's pronunciation dictionaries on elevenlabs to improve text-to-speech accuracy for specialized terms; note that while multiple dictionaries can be applied, the ui only displays the first.
Voice generation parameters retrievalFetches configurable parameters for elevenlabs voice generation, used to determine available settings (e.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Elevenlabs account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Elevenlabs

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Elevenlabs access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called elevenlabs_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["elevenlabs"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Elevenlabs actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Elevenlabs actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, elevenlabs)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Elevenlabs tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Elevenlabs database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Elevenlabs

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Elevenlabs, then start asking questions.

Complete Code

Here's the complete code to get you started with Elevenlabs and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["elevenlabs"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Elevenlabs actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Elevenlabs actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Elevenlabs to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Elevenlabs tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

How to build Elevenlabs MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Elevenlabs MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Elevenlabs MCP with Claude Agent SDK

Google ADK

Google ADK

Use Elevenlabs MCP with Google ADK

LangChain

LangChain

Use Elevenlabs MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Elevenlabs MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Elevenlabs MCP with Mastra AI

CrewAI

CrewAI

Use Elevenlabs MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Elevenlabs MCP?

With a standalone Elevenlabs MCP server, the agents and LLMs can only access a fixed set of Elevenlabs tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Elevenlabs and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Elevenlabs tools.

Can I manage the permissions and scopes for Elevenlabs while using Tool Router?

Yes, absolutely. You can configure which Elevenlabs scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Elevenlabs data and credentials are handled as safely as possible.

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