Resemble AI MCP Server for LlamaIndexGive LlamaIndex instant access to 16 tools to Add Watermark, Create Clip, Create Project, and more

Name: Resemble AI
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Resemble AI as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Ask AI about this MCP Server for LlamaIndex

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The Resemble AI MCP Server for LlamaIndex is a standout in the Image Video category — giving your AI agent 16 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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ChatGPT

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+ other MCP clients

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Resemble AI. "
            "You have 16 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Resemble AI?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Resemble AI MCP Server

Connect your Resemble AI account to any AI agent to generate, manage, and protect high-fidelity synthetic speech through natural conversation.

LlamaIndex agents combine Resemble AI tool responses with indexed documents for comprehensive, grounded answers. Connect 16 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Voice Synthesis — Create high-quality audio clips from text using custom or system voices with full SSML support.
Speech-to-Speech — Transform source audio into a target voice while preserving the original emotion, intonation, and timing.
Project Organization — List, create, and manage projects to keep your audio assets and clips organized.
Voice Management — List available voices, create new custom voice profiles, and manage training recordings.
AI Safety & Security — Detect deepfakes in audio files and apply or verify digital watermarks to ensure content authenticity.

update

Update clip on Resemble AI

Update an existing clip

verify

Verify watermark on Resemble AI

Verify a watermark in an audio file

Connect Resemble AI to LlamaIndex via MCP

Follow these steps to wire Resemble AI into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 16 tools from Resemble AI

Why Use LlamaIndex with the Resemble AI MCP Server

LlamaIndex provides unique advantages when paired with Resemble AI through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Resemble AI tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Resemble AI tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Resemble AI, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Resemble AI tools were called, what data was returned, and how it influenced the final answer

Resemble AI + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Resemble AI MCP Server delivers measurable value.

Hybrid search: combine Resemble AI real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Resemble AI to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Resemble AI for fresh data

Analytical workflows: chain Resemble AI queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Resemble AI in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Resemble AI immediately.

"List all my Resemble AI projects and their UUIDs."

"Create a new audio clip in project proj_123 saying 'Welcome to the future of voice' using voice voice_789."

"Analyze this audio URL to see if it's a deepfake: https://example.com/audio.mp3"

Troubleshooting Resemble AI MCP Server with LlamaIndex

Common issues when connecting Resemble AI to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Resemble AI + LlamaIndex FAQ

Common questions about integrating Resemble AI MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Resemble AI tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.