How to Use the Bandsintown MCP in LlamaIndex
Index live tour dates and venue schedules from Bandsintown directly into LlamaIndex vector stores for RAG-powered music search.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Bandsintown MCP to LlamaIndex
Create your Vinkius account to connect Bandsintown 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.
Index Live Concert Data into LlamaIndex
Index live concert schedules directly into LlamaIndex by calling `get_artist_events` to generate searchable vector nodes. When your agent calls this tool, LlamaIndex grabs the upcoming show details and indexes them directly into your vector store. This executes semantic queries across active tours without hitting the API repeatedly. You also pull venue-specific lineups using `get_venue_events` to enrich your local knowledge base. The raw JSON payload containing ticket links and artist details becomes chunked text, ready for RAG pipelines.
Semantic RAG Queries for Tour Schedules
Query the indexed output of `get_recommended_events` using natural language to perform semantic RAG searches for tour schedules. Your LlamaIndex agent searches the indexed concert data to find shows that match a specific vibe or genre. The index combines live API data with your local artist bios. To set this up, use `McpToolSpec` with the Bandsintown MCP Server to register the tools. The framework automatically formats the output of `get_artist` so your embedding models can process the artist metadata.
Context-Aware Music Recommendations
Combine historical user preferences with live venue data by running `get_venue_events` within your LlamaIndex pipeline. Your pipeline retrieves past concert attendance from your database, then queries the local index to suggest the best local gigs. If a user asks about a specific performer, the agent checks the local index first. If the data is stale, it invokes `get_artist_by_id` to pull the latest profile and updates the vector store in the background.
Set up Bandsintown MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - 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 providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto 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 Bandsintown MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
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 Bandsintown tools.",
)
response = await agent.run("List recent Bandsintown data") Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Bandsintown. 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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