How to Use the ZoomInfo MCP in LlamaIndex

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Connect ZoomInfo MCP to LlamaIndex

Create your Vinkius account to connect ZoomInfo 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 ZoomInfo with LlamaIndex

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Indexing API Search Results

Instead of just printing search output, LlamaIndex lets you index the data retrieved from `search_companies` or `search_contacts`. This means every result becomes part of your vector store's knowledge base. The next time you ask a question related to that industry, the agent doesn't guess; it retrieves the original API data directly from your indexed records. It eliminates hallucinations.

Combining Live Data with Documents

You can build RAG applications where live ZoomInfo data meets static documents. If you are analyzing a contract, for instance, the agent can cross-reference clauses against company information pulled from `enrich_company` and embed both into one queryable context. This unified index allows semantic searches that span both unstructured text (like PDFs) and highly structured API facts.

Retrieving Historical Intent Data

Don't just run a search. Query past sessions. By indexing the output of `search_intent` or `get_company_news`, you create a historical view of market shifts. You can ask, 'What was the intent signal regarding X last quarter?' and get an answer grounded in actual data. This turns temporary API calls into permanent, queryable knowledge assets.

Setup guide

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

The client indexes the output of the MCP Server. When you ask a question, LlamaIndex searches its stored index first, finding relevant `zoominfo-mcp` data before generating an answer. The result is always grounded in your API facts.

Absolutely. You build RAG applications that treat the MCP Server as a dynamic source of truth. Data from `search_contacts` can be combined with documents, giving you a comprehensive context for your LLM.

Yes. By indexing past results—like old news articles pulled by `get_company_news`—the agent can retrieve answers about market trends that happened months ago, treating the index as a time machine.

Standard search gives you raw data. LlamaIndex takes that raw data (e.g., from `search_companies`) and makes it semantically searchable, allowing you to ask complex questions about its meaning.

The server touches company records, contact names, and industry classifications. You control which of these core business facts get indexed into your knowledge base.

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