How to Use the Audiomack MCP in LlamaIndex

Index Audiomack data directly into LlamaIndex. Ground your RAG applications in live music trends and artist metadata.

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Connect Audiomack MCP to LlamaIndex

Create your Vinkius account to connect Audiomack 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 Audiomack with LlamaIndex

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Index Audiomack results for LlamaIndex

Transform raw API responses into a searchable knowledge base by indexing the output of `get_artist` or `get_music_by_id`. Your LlamaIndex application gains a persistent memory of the music you process. This turns transient API data into permanent context. You can query your own history of trending music or artist uploads months later without hitting the live API again.

Ground your answers with live data

Stop guessing what is popular and let your agent query the `get_charts` tool directly. Your LlamaIndex agent creates a RAG pipeline where the context is always current. It performs a search, retrieves the latest music metadata, and uses that as the ground truth for your answers. No more hallucinations about track details or artist names.

Filter Audiomack data with LlamaIndex

Use the allowed_tools filter to restrict which parts of the music catalog your agent can access. You define the boundaries of your knowledge base by explicitly choosing which tools the agent can index. This keeps your vector store clean and focused on specific data types. You only index what you need, keeping your retrieval process fast and relevant to your music analysis.

Setup guide

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

Use the MCP tool spec to convert our tools into a format LlamaIndex understands. Once you pass them to your agent, it treats them like any other data source.

Yes, by indexing the output of your previous searches. It creates a searchable vector representation of the tracks and artists you have already fetched.

Your credentials are used only for the active session to perform authenticated actions. We isolate your keys in our secure sandbox and never persist them in your index.

It does. You can include it as a tool in your spec, allowing your agent to refine user queries before performing a full lookup.

We use a zero-trust model where data never leaves our ephemeral sandbox unless you explicitly request it. Your sensitive playlist and follow data is never written to disk by the server.

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