How to Use the VineRadar MCP in LlamaIndex

Q: How do I use LlamaIndex and VineRadar to query historical wine data?

First, you run getwinedetails for the specific wine. Then, by setting includeresources=True, those details get indexed into your vector store. You can then search this knowledge base later.

Q: Can LlamaIndex help me compare multiple VineRadar search queries?

Yes. Run searchwines and then run searchvineyards. By indexing both sets of results, you can query the combined knowledge base to find correlations between wine names and specific locations.

Ground your knowledge base in VineRadar data with LlamaIndex.

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Connect VineRadar MCP to LlamaIndex

Create your Vinkius account to connect VineRadar 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 VineRadar with LlamaIndex

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Building RAG Indexes with LlamaIndex

LlamaIndex doesn't just call tools; it indexes the results. For example, run `search_wines` and then include those resulting wine records in your knowledge base. Later, you can query this index and get answers grounded in actual API data, not some hallucination.

Indexing Varietals with LlamaIndex MCP Server

Need to keep track of all possible wine types? Use `list_wine_varietals` and set include_resources=True. This makes every varietal type part of your unified, queryable index. This is key for building a robust knowledge layer that spans multiple data sources.

Retrieving Vineyard Context with LlamaIndex

When you run `search_vineyards` by location, the resulting vineyard IDs and details get indexed. You can then query your index to ask questions like, 'What is this specific region known for?' This keeps all the operational data from VineRadar available in a searchable format.

Setup guide

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

First, you run `get_wine_details` for the specific wine. Then, by setting include_resources=True, those details get indexed into your vector store. You can then search this knowledge base later.

Since you are indexing API results, ensure that any sensitive vineyard IDs or financial records are filtered out before they become part of your queryable index. Only store what's necessary.

Yes. Run `search_wines` and then run `search_vineyards`. By indexing both sets of results, you can query the combined knowledge base to find correlations between wine names and specific locations.

Yes. The client supports filtering allowed tools, meaning you can specifically target which functions—like `get_vineyard_details`—contribute to the index structure.

The primary data types stored and queried are vineyard details (IDs, locations) and wine details (varietals, vintage years), all converted into searchable text chunks.

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