CUFinder MCP Server for LlamaIndexGive LlamaIndex instant access to 13 tools to Bulk Enrich, Check Cufinder Status, Enrich Linkedin, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CUFinder as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The CUFinder app connector for LlamaIndex 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
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to CUFinder. "
"You have 13 tools available."
),
)
response = await agent.run(
"What tools are available in CUFinder?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine CUFinder tool responses with indexed documents for comprehensive, grounded answers. Connect 13 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex
When LlamaIndex 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
Verify connectivity
Enrich LinkedIn profile
Find company domain
Find email address
Find employees
Find phone number
Get account info
Get company info
Get company revenue
Get social profiles
Get tech stack
Verify email
Connect CUFinder to LlamaIndex via MCP
Follow these steps to wire CUFinder into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the CUFinder MCP Server
LlamaIndex provides unique advantages when paired with CUFinder through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine CUFinder tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain CUFinder tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query CUFinder, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what CUFinder tools were called, what data was returned, and how it influenced the final answer
CUFinder + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the CUFinder MCP Server delivers measurable value.
Hybrid search: combine CUFinder real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query CUFinder to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying CUFinder for fresh data
Analytical workflows: chain CUFinder queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for CUFinder in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with CUFinder immediately.
"Find the domain for the company 'Acme Global Solutions'."
"Show me the employees and decision makers for 'apple.com'."
"Enrich the data from this LinkedIn URL: 'https://linkedin.com/in/stevejobs'."
Troubleshooting CUFinder MCP Server with LlamaIndex
Common issues when connecting CUFinder to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCUFinder + LlamaIndex FAQ
Common questions about integrating CUFinder MCP Server with LlamaIndex.