How to Use the Lusha MCP in LlamaIndex

Q: Can I search Lusha contacts using LlamaIndex?

Absolutely, the searchcontacts tool is built specifically for this. It returns structured data that LlamaIndex can index for semantic search.

Q: How do I check my Lusha credits in LlamaIndex?

Call the getcredits tool as part of your agent's pre-flight check. It returns your current balance so you can manage your API usage.

Index your Lusha lead data directly into LlamaIndex knowledge bases for semantic search and grounded decision making.

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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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Perplexity

Semantic Lead Indexing

Turn your prospect data into searchable knowledge. Using `search_contacts` as an MCP server tool allows LlamaIndex to ingest and index results for later querying. Your agent builds a vector store of your leads. This lets you ask complex questions about your target market that require actual contact data to answer.

Grounded Prospect Research

Stop guessing about company details. By calling `find_company` through LlamaIndex, your agent retrieves firmographic data that is immediately indexed for your RAG pipeline. Your answers are grounded in live API data. This prevents the hallucinations common in pure LLM setups when dealing with B2B contact lists.

Automated Data Retrieval

The `find_by_linkedin` tool integrates into your existing workflows to map profiles to data. LlamaIndex treats these as standard function calls. It handles the conversion of API responses into indexed documents. You get a unified interface for both your internal documents and live Lusha data.

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

Use the BasicMCPClient to connect to the Lusha server and convert the tools into a spec. This allows your agent to call them as standard functions.

Absolutely, the `search_contacts` tool is built specifically for this. It returns structured data that LlamaIndex can index for semantic search.

It persists only if you configure your index to store it. The MCP server provides the live data, and you decide what to save.

Call the `get_credits` tool as part of your agent's pre-flight check. It returns your current balance so you can manage your API usage.

Security is handled at the MCP transport layer. Your Lusha credentials are never exposed to the index, only the retrieved lead data.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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