How to Use the CUFinder MCP in LlamaIndex

Q: How do I connect CUFinder to my LlamaIndex pipeline?

Use the BasicMCPClient to connect LlamaIndex to the MCP Server and wrap it in McpToolSpec. Then, call totoollistasync() to feed the B2B lookup tools directly into your agent.

Q: Can LlamaIndex index the outputs of CUFinder lookups?

Yes, you can parse the results of tools like enrichlinkedin or getcompanyinfo into Document objects. LlamaIndex then embeds and stores this B2B data in your vector database.

Q: Does this CUFinder MCP integration support resource loading in LlamaIndex?

Yes, setting includeresources=True allows LlamaIndex to read schema resources directly from the server. This makes it easier for your agent to understand the structure of the incoming company data.

Q: How do I filter which CUFinder tools my agent can access?

You pass an allowed tools list when initializing your McpToolSpec instance. This prevents the agent from running expensive bulk enrichments when it only needs simple email lookups.

Index live B2B contact data directly into your LlamaIndex vector store using this high-speed MCP Server.

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Connect CUFinder MCP to LlamaIndex

Create your Vinkius account to connect CUFinder 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 CUFinder with LlamaIndex

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Index Tech Stack Data into LlamaIndex Knowledge Bases

The `get_company_tech` tool pulls a target company's current software stack directly into your LlamaIndex data pipeline. By converting this raw API output into document nodes, your system indexes the tech stack for semantic search. Your query engine can then search past lookups to find which companies run specific databases or frameworks. This turns transient API calls into a searchable, local knowledge base that grounds your agent's answers.

Query Verified Contact Details via an MCP Server Agent

The `find_email` and `find_phone` tools fetch verified B2B contact info that your LlamaIndex FunctionAgent queries on demand. Instead of guessing details, the agent runs live lookups during a user conversation to retrieve accurate phone numbers and email addresses. You can restrict which tools the agent accesses by passing an allowed list to the MCP tool spec. This ensures the model only queries contact endpoints when specifically prompted for lead details.

Ground Revenue Analysis in Live CUFinder Data

The `get_company_revenue` tool retrieves financial metrics to ground your LlamaIndex synthesized responses in real data. Your RAG pipeline combines this financial intelligence with internal company documents to generate highly accurate investment memos. This setup eliminates hallucinated financial figures by forcing the LLM to read directly from the tool output. Your analysts get reports backed by verifiable revenue metrics rather than model guesses.

Setup guide

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

Use the `BasicMCPClient` to connect LlamaIndex to the MCP Server and wrap it in `McpToolSpec`. Then, call `to_tool_list_async()` to feed the B2B lookup tools directly into your agent.

Yes, you can parse the results of tools like `enrich_linkedin` or `get_company_info` into Document objects. LlamaIndex then embeds and stores this B2B data in your vector database.

Yes, setting `include_resources=True` allows LlamaIndex to read schema resources directly from the server. This makes it easier for your agent to understand the structure of the incoming company data.

You pass an allowed tools list when initializing your `McpToolSpec` instance. This prevents the agent from running expensive bulk enrichments when it only needs simple email lookups.

Vinkius secures all retrieved lead details inside isolated, single-use V8 sandboxes. No B2B contact data or phone numbers are stored on our servers, keeping your target database completely private.

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