Deezer MCP Server for LlamaIndex 14 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Deezer 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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Vinkius supports streamable HTTP and SSE.
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 Deezer. "
"You have 14 tools available."
),
)
response = await agent.run(
"What tools are available in Deezer?"
)
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 Deezer MCP Server
Connect Deezer music catalog to any AI agent and search millions of tracks, discover artists, explore albums and playlists, and check global charts through natural language.
LlamaIndex agents combine Deezer tool responses with indexed documents for comprehensive, grounded answers. Connect 14 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
- Track Search — Search millions of tracks by title, lyrics, or keywords with preview links
- Artist Discovery — Find artists, view fan counts, and get their top tracks and radio stations
- Album Browsing — Look up albums by title or artist with release dates and track listings
- Playlist Exploration — Search public playlists by mood, genre, or curator
- Charts — Check top tracks, albums, artists, and playlists globally or by country
- Genre Navigation — Browse music genres and find curated playlists per genre
The Deezer MCP Server exposes 14 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 Deezer to LlamaIndex via MCP
Follow these steps to integrate the Deezer 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 14 tools from Deezer
Why Use LlamaIndex with the Deezer MCP Server
LlamaIndex provides unique advantages when paired with Deezer through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Deezer tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Deezer tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Deezer, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Deezer tools were called, what data was returned, and how it influenced the final answer
Deezer + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Deezer MCP Server delivers measurable value.
Hybrid search: combine Deezer real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Deezer 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 Deezer for fresh data
Analytical workflows: chain Deezer queries with LlamaIndex's data connectors to build multi-source analytical reports
Deezer MCP Tools for LlamaIndex (14)
These 14 tools become available when you connect Deezer to LlamaIndex via MCP:
get_album
Get detailed album information
get_album_tracks
Get tracks from an album
get_artist
Get detailed artist information
get_artist_radio
Get artist radio tracks
get_artist_top_tracks
Get top tracks for an artist
get_chart
Optionally filter by country using ISO 3166-1 country code. Get Deezer charts
get_genre
Get genre information
get_genre_playlists
Get playlists for a genre
get_playlist
Get detailed playlist information
get_track
Get detailed track information
search_albums
Returns albums with release date, track count, and cover art info. Search for albums on Deezer
search_artists
Returns artist info including fan count, album count, and profile links. Search for artists on Deezer
search_playlists
Returns playlist info including creator, track count, and fan count. Search for playlists on Deezer
search_tracks
Returns tracks with title, artist, album, duration, and preview link. Supports pagination with limit and offset index. Search for tracks on Deezer
Example Prompts for Deezer in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Deezer immediately.
"Search for songs by Daft Punk."
"What's trending in Brazil right now?"
"Find me chill playlists for studying."
Troubleshooting Deezer MCP Server with LlamaIndex
Common issues when connecting Deezer to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDeezer + LlamaIndex FAQ
Common questions about integrating Deezer 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 Deezer 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.
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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 Deezer to LlamaIndex
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.