How to Use the Lusha MCP in LangChain

Q: Can my LangChain agent perform bulk enrichment with Lusha?

Yes, it uses the bulkenrich tool to process multiple records at once. The agent handles the input array and iterates through the results automatically.

Q: Are there limits on how many contacts I can search?

You are bound by your Lusha plan quotas. Use the getcredits tool to track your remaining balance in real-time.

Build automated lead enrichment chains in LangChain using the Lusha MCP server to turn raw prospect data into actionable pipeline.

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Connect Lusha MCP to LangChain

Create your Vinkius account to connect Lusha to LangChain 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 LangChain

Ask AI about this MCP

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Claude

Perplexity

Chainable Lead Enrichment

Feed contact data directly into your reasoning pipelines. By using `bulk_enrich` as a tool within a LangChain agent, you turn static lead lists into verified contact profiles without manual intervention. Your agent handles the sequence internally. It passes the output of one step directly into the next, ensuring your CRM stays updated with fresh, verified info.

Contextual Profile Discovery

Trigger `find_person` or `find_company` based on your agent's current task. This MCP server lets your chain perform ad-hoc lookups the moment a prospect is identified in your workflow. Latency is minimal because the agent calls the API directly. You see every request inside your LangSmith traces, giving you full visibility into exactly how your agent queries Lusha.

Real-time Credit Monitoring

Avoid hitting your API limits by checking your balance before starting a large job. The `get_credits` tool provides instant feedback to your agent. Your pipeline can decide whether to proceed or pause based on the current count. It keeps your automation running within budget without human oversight.

Setup guide

Set up Lusha MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Lusha tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "lusha-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Lusha transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

Install the necessary adapters and initialize the server using the MCP transport. Once connected, pass the tool list from the Lusha server directly into your agent definition.

Yes, it uses the `bulk_enrich` tool to process multiple records at once. The agent handles the input array and iterates through the results automatically.

The data stays within your agent's memory or is passed to your downstream database. You control the flow of information entirely.

You are bound by your Lusha plan quotas. Use the `get_credits` tool to track your remaining balance in real-time.

We use a zero-trust architecture where your session token is the only gateway. No data is cached by the server, and every request is encrypted.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python