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CUFinder MCP Server for LangChainGive LangChain instant access to 13 tools to Bulk Enrich, Check Cufinder Status, Enrich Linkedin, and more

Built by Vinkius GDPR 13 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect CUFinder through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Ask AI about this App Connector for LangChain

The CUFinder app connector for LangChain is a standout in the Productivity category — giving your AI agent 13 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "cufinder": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using CUFinder, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())
CUFinder
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About CUFinder MCP Server

Connect your CUFinder business intelligence account to any AI agent and simplify how you discover professional domains, enrich company metadata, and identify decision makers through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with CUFinder through native MCP adapters. Connect 13 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

  • Domain Discovery — Find the primary web domain for any company using only its trade name via AI.
  • Company Intelligence — Retrieve detailed metadata including industry, location, and estimated annual revenue for specific domains.
  • Employee Prospecting — List known employees and key decision makers associated with a company domain.
  • LinkedIn Enrichment — Fetch detailed contact info and professional data from specific LinkedIn profile URLs.
  • Lead Qualification — Verify company size and financial standing to prioritize your sales outreach.
  • Data Accuracy — Enhance your CRM records with verified real-time data directly from the agent.

The CUFinder MCP Server exposes 13 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 13 CUFinder tools available for LangChain

When LangChain connects to CUFinder through Vinkius, your AI agent gets direct access to every tool listed below — spanning lead-enrichment, company-intelligence, b2b-data, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

bulk_enrich

Bulk enrich

check_cufinder_status

Verify connectivity

enrich_linkedin

Enrich LinkedIn profile

find_domain

Find company domain

find_email

Find email address

find_employees

Find employees

find_phone

Find phone number

get_account

Get account info

get_company_info

Get company info

get_company_revenue

Get company revenue

get_company_socials

Get social profiles

get_company_tech

Get tech stack

verify_email

Verify email

Connect CUFinder to LangChain via MCP

Follow these steps to wire CUFinder into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save the code and run python agent.py
04

Explore tools

The agent discovers 13 tools from CUFinder via MCP

Why Use LangChain with the CUFinder MCP Server

LangChain provides unique advantages when paired with CUFinder through the Model Context Protocol.

01

The largest ecosystem of integrations, chains, and agents. combine CUFinder MCP tools with 500+ LangChain components

02

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

03

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

04

Memory and conversation persistence let agents maintain context across CUFinder queries for multi-turn workflows

CUFinder + LangChain Use Cases

Practical scenarios where LangChain combined with the CUFinder MCP Server delivers measurable value.

01

RAG with live data: combine CUFinder tool results with vector store retrievals for answers grounded in both real-time and historical data

02

Autonomous research agents: LangChain agents query CUFinder, synthesize findings, and generate comprehensive research reports

03

Multi-tool orchestration: chain CUFinder tools with web scrapers, databases, and calculators in a single agent run

04

Production monitoring: use LangSmith to trace every CUFinder tool call, measure latency, and optimize your agent's performance

Example Prompts for CUFinder in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with CUFinder immediately.

01

"Find the domain for the company 'Acme Global Solutions'."

02

"Show me the employees and decision makers for 'apple.com'."

03

"Enrich the data from this LinkedIn URL: 'https://linkedin.com/in/stevejobs'."

Troubleshooting CUFinder MCP Server with LangChain

Common issues when connecting CUFinder to LangChain through the Vinkius, and how to resolve them.

01

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

CUFinder + LangChain FAQ

Common questions about integrating CUFinder MCP Server with LangChain.

01

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.
02

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.
03

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.