How to Use the Last.fm MCP in LlamaIndex

Index your music listening history into LlamaIndex using this Last.fm MCP Server.

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Connect Last.fm MCP to LlamaIndex

Create your Vinkius account to connect Last.fm 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 Last.fm with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Index your listening history

Turn your scrobbles into a searchable knowledge base. By using `get_recent_tracks` and `get_top_albums`, you can populate your vector store with high-fidelity music data. The server makes it easy to ground your AI in your own reality. Instead of guessing your preferences, the agent queries the index created from your actual listening habits.

Build RAG apps with LlamaIndex

Combine your Last.fm data with local documents for a unified search experience. When you run `get_artist_info`, the returned metadata becomes part of your searchable index. This approach lets you ask questions about your history that the agent can answer with precision. It stops the hallucination problem by keeping the agent focused on your actual API results.

Filter your music data precisely

Use the tool list to narrow down what gets indexed. You can restrict the agent to only pull `get_top_tracks` and `get_loved_tracks` to keep your vector store lean and fast. This keeps your LlamaIndex setup efficient. You only store the specific data points that actually matter for your current project or research goals.

Setup guide

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

Install the llama-index-tools-mcp package and pass the server URL to the client. The tools will then convert into a format your agent can index.

Yes. You just need to call the tools asynchronously and ingest the resulting data. It works just like any other data source in your graph.

The server only touches your public profile metadata. Your personal account tokens are managed through the secure Vinkius endpoint, keeping your data isolated.

It does. You can enable resource access to pull data directly into your LlamaIndex knowledge base for deeper semantic searching.

Not if you index the data correctly. By grounding the agent in the output of the tools, it answers based on your actual, retrieved history.

Use it with your favorite AI tools

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