How to Use the AudD Music Recognition MCP in LlamaIndex

Q: Can I index the output of AudD Music Recognition into a LlamaIndex vector store?

Yes, you can run recognizemusic and convert the resulting track metadata and streaming links into document nodes. LlamaIndex then indexes these nodes for semantic search and future RAG queries.

Q: How does AudD Music Recognition help with lyrics-based RAG in LlamaIndex?

Your agent uses getlyrics to retrieve full song lyrics, which LlamaIndex then chunks and embeds. This lets you build Q&A systems that can answer complex questions about song meanings and themes.

Q: Can I use AudD Music Recognition to search for tracks inside a LlamaIndex application?

Yes, the searchmusic tool allows your LlamaIndex agent to find song metadata using text queries. The agent can then index the search results to enrich your local music knowledge base.

Index song metadata and lyrics from AudD Music Recognition directly into your LlamaIndex vector stores.

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Connect AudD Music Recognition MCP to LlamaIndex

Create your Vinkius account to connect AudD Music Recognition 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 AudD Music Recognition with LlamaIndex

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Build a searchable music index in LlamaIndex

The `recognize_music` tool outputs structured artist, album, and streaming link data that your LlamaIndex MCP client converts into queryable document nodes. Instead of just identifying a song once, your agent indexes the results so you can run semantic queries on your music history later. This setup lets you query your vector store for terms like 'songs from the 90s we detected last week' without hitting the external API twice. The index stores the output of every successful lookup, creating a local, searchable cache of your audio analysis.

Query song lyrics with LlamaIndex and this MCP Server

The `get_lyrics` tool fetches complete song texts that your LlamaIndex pipeline can split, chunk, and embed into a vector database. When a user asks a question about a song's meaning, your RAG agent queries this index to find the exact stanza. If the lyrics aren't in your index yet, the agent invokes `search_lyrics` to find the song by a text fragment, pulls the full text, and indexes it on the fly. This gives your knowledge base a real-time bridge to the AudD database.

Track and index audio streams over time

The `list_monitored_streams` tool pulls your active stream configurations so LlamaIndex can index your monitoring setup. Your agent can cross-reference this list with your vector store to ensure all active broadcast feeds are properly logged. When new songs are detected via `add_monitored_stream`, the incoming metadata is fed directly into your index. This builds a historical timeline of played tracks that you can query using natural language.

Setup guide

Set up AudD Music Recognition 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 AudD Music Recognition 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 AudD Music Recognition tools.",
)
response = await agent.run("List recent AudD Music Recognition data")

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Common questions about AudD Music Recognition MCP in LlamaIndex

Yes, you can run `recognize_music` and convert the resulting track metadata and streaming links into document nodes. LlamaIndex then indexes these nodes for semantic search and future RAG queries.

Your agent uses `get_lyrics` to retrieve full song lyrics, which LlamaIndex then chunks and embeds. This lets you build Q&A systems that can answer complex questions about song meanings and themes.

Yes, the `search_music` tool allows your LlamaIndex agent to find song metadata using text queries. The agent can then index the search results to enrich your local music knowledge base.

You can use LlamaIndex's tool filtering to limit your agent's access to specific tools like `recognize_at_time` or `search_lyrics`. This prevents the agent from calling stream management tools when it only needs to identify songs.

All audio URLs and search queries are processed over encrypted connections through Vinkius. The raw audio files are analyzed on the fly and are never persisted, ensuring your proprietary media streams remain secure.

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