How to Use the Lovo AI (Genny TTS & Voice Synthesis API) MCP in LlamaIndex

Index synthetic audio data in LlamaIndex using the Lovo AI (Genny TTS & Voice Synthesis API) MCP Server.

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Connect Lovo AI (Genny TTS & Voice Synthesis API) MCP to LlamaIndex

Create your Vinkius account to connect Lovo AI (Genny TTS & Voice Synthesis API) 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 Lovo AI (Genny TTS & Voice Synthesis API) with LlamaIndex

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Index generated speech for RAG

Turn your Lovo AI (Genny TTS & Voice Synthesis API) output into searchable knowledge. Use `create_tts_job` to generate audio, then index the results with LlamaIndex for later retrieval. This creates a system where your AI remembers what it said. You can query past synthesis jobs and reuse existing audio assets without wasting API credits.

Retrieve speaker metadata for indexing

Use `get_speaker` to pull detailed voice profiles and store them in your vector index. This allows your RAG application to search for voices based on specific attributes. Your index becomes a repository of voice capabilities. When a user asks for a specific tone, your agent finds the best match directly from your stored metadata.

Automate voice synthesis workflows

Map the output of `list_voices` to your knowledge base to keep your application aware of available speakers. LlamaIndex treats these as queryable data points. Your agent builds a mental map of your voice options. It no longer guesses which speaker to use, as it has a live index of all available voices at its disposal.

Setup guide

Set up Lovo AI (Genny TTS & Voice Synthesis API) 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 Lovo AI (Genny TTS & Voice Synthesis API) 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 Lovo AI (Genny TTS & Voice Synthesis API) tools.",
)
response = await agent.run("List recent Lovo AI (Genny TTS & Voice Synthesis API) data")

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Common questions about Lovo AI (Genny TTS & Voice Synthesis API) MCP in LlamaIndex

Yes. By using the MCP tool spec, your agent gains the ability to call synthesis functions and store the results in your knowledge base.

Configure your agent to save the output of the synthesis tool into your vector store. This makes your audio history searchable alongside your documents.

Absolutely. Your agent can list the voices, index their metadata, and then filter them based on your semantic search requirements.

It provides the raw data from the API that your agent needs to index. This ensures your knowledge base stays current with the latest voice synthesis capabilities.

The server stores no persistent record of your jobs; it only acts as a bridge. The data you choose to index remains in your own local vector store, away from external eyes.

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