AudD Music Recognition MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AudD Music Recognition as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 AudD Music Recognition. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in AudD Music Recognition?"
)
print(response)
asyncio.run(main())
* 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 AudD Music Recognition MCP Server
Equip your AI agent with the power of AudD, the leading music recognition and data platform. This integration allows your agent to identify songs from audio URLs, search for track information by title or artist, and retrieve full lyrics or snippets. Your agent can also find direct streaming links for identified tracks on platforms like Spotify and Apple Music. Whether you are identifying a background track from a video or searching for that one song with a specific lyric, your agent acts as a dedicated musicologist through natural conversation.
LlamaIndex agents combine AudD Music Recognition tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Music Recognition — Identify songs from publicly accessible audio URLs with high precision.
- Lyrics Search — Find full lyrics or search for songs using fragments of text.
- Metadata Retrieval — Fetch detailed track, artist, and album information including release dates and labels.
- Streaming Links — Get direct URLs to listen to identified tracks on major music platforms.
- Timecode Identification — Start recognition from a specific offset to identify songs in long audio files.
The AudD Music Recognition MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect AudD Music Recognition to LlamaIndex via MCP
Follow these steps to integrate the AudD Music Recognition MCP Server with LlamaIndex.
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 8 tools from AudD Music Recognition
Why Use LlamaIndex with the AudD Music Recognition MCP Server
LlamaIndex provides unique advantages when paired with AudD Music Recognition through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AudD Music Recognition tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AudD Music Recognition tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AudD Music Recognition, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AudD Music Recognition tools were called, what data was returned, and how it influenced the final answer
AudD Music Recognition + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AudD Music Recognition MCP Server delivers measurable value.
Hybrid search: combine AudD Music Recognition real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AudD Music Recognition 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 AudD Music Recognition for fresh data
Analytical workflows: chain AudD Music Recognition queries with LlamaIndex's data connectors to build multi-source analytical reports
AudD Music Recognition MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect AudD Music Recognition to LlamaIndex via MCP:
add_monitored_stream
Add an audio stream to monitor for music
get_lyrics
Get full lyrics for a specific track
list_monitored_streams
List all monitored audio streams
recognize_at_time
Useful for long files. Recognize music starting at a specific offset
recognize_music
Returns artist, title, album, and streaming links (Apple Music, Spotify, etc.). Recognize a song from an audio URL
search_lyrics
Returns matched lyrics and song metadata. Search for song lyrics by text fragment
search_music
Search for a song by text query
set_stream_callback_url
Set the webhook URL for stream monitoring results
Example Prompts for AudD Music Recognition in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AudD Music Recognition immediately.
"Identify the song at this URL: https://example.com/audio.mp3"
"Search for lyrics containing 'never gonna give you up'."
"Find the artist and album for the song 'Stairway to Heaven'."
Troubleshooting AudD Music Recognition MCP Server with LlamaIndex
Common issues when connecting AudD Music Recognition to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAudD Music Recognition + LlamaIndex FAQ
Common questions about integrating AudD Music Recognition MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect AudD Music Recognition with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect AudD Music Recognition to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.