How to Use the Lucidworks Fusion (AI Search & Discovery) MCP in LlamaIndex

Q: How do I index search results from Lucidworks Fusion (AI Search & Discovery) into LlamaIndex?

You run lw.querysearch inside your LlamaIndex data ingestion pipeline. The returned JSON payload is converted into document nodes and indexed into your local vector store automatically.

Q: Can this MCP Server pull collection metadata to guide LlamaIndex retrieval?

Yes. The agent can call lw.listcollections to discover what search indexes exist, allowing it to route user queries to the correct collection dynamically.

Q: How do I run custom Solr queries using Lucidworks Fusion (AI Search & Discovery) with LlamaIndex?

You use the lw.postcustomquery tool to pass raw JSON search payloads directly to the underlying Solr engine. The Lucidworks Fusion (AI Search & Discovery) MCP Server returns the vector search results directly to your LlamaIndex pipeline.

Index live search analytics and collection configurations from Lucidworks Fusion directly into your LlamaIndex vector store.

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Connect Lucidworks Fusion (AI Search & Discovery) MCP to LlamaIndex

Create your Vinkius account to connect Lucidworks Fusion (AI Search & Discovery) 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 Lucidworks Fusion (AI Search & Discovery) with LlamaIndex

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Index search results directly into LlamaIndex memory

The `lw.query_search` tool retrieves precise search documents from your Fusion collections based on the active query profile. LlamaIndex takes this output and indexes it directly into your local vector store for semantic search. With this MCP setup, you can use `lw.query_sorted` alongside this pipeline so your agent can sort responses before storing them. This ensures your local index always prioritizes the freshest data retrieved from the server.

Map your search architecture into a queryable index

The `lw.list_collections` tool pulls all active search collections from your Fusion instance. Your LlamaIndex agent can run this tool to understand the available data schemas before building its retrieval plan. Adding `lw.list_index_profiles` to the mix lets the agent query your indexing rules. This prevents hallucinations by giving the agent a grounded map of your exact search pipelines.

Build custom Solr vector pipelines with LlamaIndex

The `lw.post_custom_query` tool executes highly customized JSON search logic against your Solr vectors. This MCP Server lets your LlamaIndex pipelines run complex semantic searches that standard queries can't handle. You can feed the output of these custom vector searches back into your local indices. It allows you to merge enterprise search results with your private document stores.

Setup guide

Set up Lucidworks Fusion (AI Search & Discovery) 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 Lucidworks Fusion (AI Search & Discovery) 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 Lucidworks Fusion (AI Search & Discovery) tools.",
)
response = await agent.run("List recent Lucidworks Fusion (AI Search & Discovery) 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 Lucidworks Fusion. 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 Lucidworks Fusion (AI Search & Discovery) MCP in LlamaIndex

You run `lw.query_search` inside your LlamaIndex data ingestion pipeline. The returned JSON payload is converted into document nodes and indexed into your local vector store automatically.

Yes. The agent can call `lw.list_collections` to discover what search indexes exist, allowing it to route user queries to the correct collection dynamically.

You use the `lw.post_custom_query` tool to pass raw JSON search payloads directly to the underlying Solr engine. The Lucidworks Fusion (AI Search & Discovery) MCP Server returns the vector search results directly to your LlamaIndex pipeline.

Your LlamaIndex agent will receive an empty list. You can configure a fallback step that uses `lw.list_query_profiles` to try a different query profile or broaden the search parameters.

All telemetry data, search queries, and Solr payloads pass through ephemeral sandboxes. Vinkius runs these connections with zero-trust isolation, ensuring your raw data is wiped immediately after the tool execution completes.

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