How to Use the Doofinder MCP in LlamaIndex

Q: How do I run a search query with Doofinder and LlamaIndex?

Use the McpToolSpec to load the Doofinder tools. Once loaded, your LlamaIndex agent can call searchkeyword to find items.

Q: Can I use Doofinder with LlamaIndex for RAG?

Absolutely. You can use the search results from searchfiltered to populate your vector index. This allows for highly relevant, live-data RAG.

Index live Doofinder data into LlamaIndex. Ground your RAG applications in real-time search results and analytics.

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Connect Doofinder MCP to LlamaIndex

Create your Vinkius account to connect Doofinder 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 Doofinder with LlamaIndex

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Vectorize search results in LlamaIndex

Turn your search index into a knowledge base. Use `get_items` to pull product data and ingest it directly into your LlamaIndex vector store for semantic retrieval. This ensures your RAG pipeline doesn't hallucinate. By grounding answers in the actual items returned by your search engine, your agents provide accurate, data-backed responses to customer questions.

Query search configurations

Understand your search structure by querying it as a document. Use `get_search_engines` to fetch your engine definitions and add them to your index. Your agent can now answer questions about how search is configured or why certain facets appear. This makes your search infrastructure part of the knowledge your agent understands.

Automated analytics indexing

Keep your knowledge base current with the latest search trends. By scheduling an agent to periodically run `get_stats`, you index the most recent search performance data. Your LlamaIndex application then uses this history to provide insights. You can ask your agent to explain shifts in search behavior based on the data it has indexed.

Setup guide

Set up Doofinder 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 Doofinder 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 Doofinder tools.",
)
response = await agent.run("List recent Doofinder data")

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Common questions about Doofinder MCP in LlamaIndex

Use the `McpToolSpec` to load the Doofinder tools. Once loaded, your LlamaIndex agent can call `search_keyword` to find items.

Absolutely. You can use the search results from `search_filtered` to populate your vector index. This allows for highly relevant, live-data RAG.

The MCP tools allow for direct retrieval. You avoid intermediate steps by feeding the output of `suggest` directly into your LlamaIndex retrieval pipeline.

The server only touches your item records and search stats. No customer PII is stored; it only processes the product catalog data you explicitly index.

Yes. The MCP server handles auth via your endpoint token. LlamaIndex only interacts with the tools, never seeing your raw credentials.

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