How to Use the Lusha MCP in LlamaIndex

Index verified B2B data into your LlamaIndex knowledge base for smarter RAG applications.

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Connect Lusha MCP to LlamaIndex

Create your Vinkius account to connect Lusha 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 Lusha with LlamaIndex

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Build a searchable Lusha index

Turn your prospect research into a queryable vector store. Use `prospect_new_companies` to gather data, then index the results so your RAG application can answer questions about your target market instantly. This turns raw API responses into a persistent knowledge base. You aren't just getting data; you are building a repository that grows more valuable with every search.

Ground your answers in real contact data

Stop hallucinating contact details. By using `get_person_by_email` or `get_person_by_linkedin`, your agent pulls verified info directly from the source during the retrieval phase. Your responses stay grounded in reality. The agent uses the tool to verify the person exists and retrieves the latest details before generating an answer for the user.

Manage your Lusha data access

Use the MCP server's built-in tools to filter what information reaches your index. You control exactly which firmographic data points get stored by selectively calling `enrich_company_info`. This prevents your index from getting bloated with irrelevant data. You keep your vector store lean, fast, and focused on the leads that actually matter to your business.

Setup guide

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

Yes, once you load the tools into your agent, they function as standard data sources. You can use the `McpToolSpec` to bridge the connection without writing custom boilerplate.

LlamaIndex stores the results of your tool calls. If you save the output of `enrich_person_info` to your index, you can query that data later without making a new API call.

You can index any output from the enrichment tools, including contact details and company profiles. Just ensure the returned data is formatted correctly for your specific embedding model.

Trigger a re-indexing flow by calling the enrichment tools again for existing contacts. LlamaIndex will update the entries in your vector store with the fresh data.

The server treats your data as ephemeral during the enrichment request. Once the data reaches your index, it is secured according to your own internal data governance policies.

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