How to Use the Cartesia (Voice AI) MCP in LlamaIndex

Q: How do I connect the Cartesia (Voice AI) MCP Server to LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient with your Vinkius URL, wrap it in an McpToolSpec, and call totoollistasync() to feed the tools into your FunctionAgent.

Q: Can LlamaIndex search through my Cartesia (Voice AI) agent calls?

Yes. You write a routine that uses listagentcalls to fetch recent transcripts, chunks the text, and stores it in your vector database for semantic retrieval.

Q: How do I restrict which Cartesia tools my LlamaIndex agent can use?

Pass an allowedtools list when configuring your McpToolSpec. You might restrict a public-facing agent to only use ttsbytes while blocking destructive actions like deletevoice.

Index transcripts and voice metadata into LlamaIndex to build queryable voice applications with Cartesia.

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Connect Cartesia (Voice AI) MCP to LlamaIndex

Create your Vinkius account to connect Cartesia (Voice AI) 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 Cartesia (Voice AI) with LlamaIndex

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Index voice logs and transcripts

Your application runs `stt_batch` and pipes the resulting transcript directly into your LlamaIndex vector store. LlamaIndex turns raw API output into searchable data. Instead of transcribing audio and losing the text, you capture everything. This converts conversational data into a permanent knowledge base. You pull historical interactions using `list_agent_calls`, embed the text, and allow users to query past voice sessions with semantic search. The MCP Server acts as an active data pipeline.

Query your Cartesia MCP Server config

By connecting this server to a FunctionAgent, you can call `list_voices` to ask questions about your current audio setup. Managing dozens of custom voices gets complicated fast. You need answers grounded in live data, not static configs. You never have to guess which dialect a specific voice uses. The agent fetches exact parameters with `get_voice` and cross-references them against your indexed documentation. It tells you exactly what is running in production.

Generate audio from retrieved documents

Adding Cartesia tools lets your LlamaIndex application read its findings out loud using `tts_bytes`. Retrieval usually ends with text on a screen. This MCP integration changes the final output format. You tailor the delivery based on the retrieved content. If the query pulls up a French document, the agent triggers `localize_voice` to adapt the speaker's accent before generating the final audio response. The voice matches the context.

Setup guide

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

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient` with your Vinkius URL, wrap it in an `McpToolSpec`, and call `to_tool_list_async()` to feed the tools into your `FunctionAgent`.

Yes. You write a routine that uses `list_agent_calls` to fetch recent transcripts, chunks the text, and stores it in your vector database for semantic retrieval.

Pass an `allowed_tools` list when configuring your `McpToolSpec`. You might restrict a public-facing agent to only use `tts_bytes` while blocking destructive actions like `delete_voice`.

Yes, provided you pass the file contents correctly. LlamaIndex reads the local file, converts it to the required format, and sends it to the `stt_batch` tool for processing.

Your pronunciation dictionaries and generated audio bytes run through a zero-trust architecture. Vinkius isolates the MCP Server execution, ensuring your proprietary voice models and text payloads vanish from memory instantly.

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