How to Use the Chorus.ai (ZoomInfo) MCP in LlamaIndex

Q: How do I index Chorus.ai (ZoomInfo) transcripts with LlamaIndex?

Load the tools via llama-index-tools-mcp. Call getmeetingtranscript and pass the returned text directly into your document parser for embedding.

Q: Can LlamaIndex filter Chorus.ai (ZoomInfo) calls by competitor?

You start by running listtrackedcompetitors. Then, your agent cross-references those names with listchorusmeetings before indexing the relevant sessions.

Q: Does this MCP Server support indexing shared playlists?

Yes. Use listmeetingplaylists to find curated call collections. Your ingestion script can loop through them and update your vector store automatically.

Q: How do I restrict the tools my query engine can access?

Pass an allowedtools list when configuring your McpToolSpec. You might only want your agent reading getmeetingdetails instead of pulling full transcripts.

Index your Chorus.ai sales calls into LlamaIndex to build searchable RAG applications over your meeting data.

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Connect Chorus.ai (ZoomInfo) MCP to LlamaIndex

Create your Vinkius account to connect Chorus.ai (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 Chorus.ai (ZoomInfo) with LlamaIndex

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Claude

Perplexity

Build a vector store from call transcripts

Dumping raw audio files into a database does nothing. This MCP Server lets your LlamaIndex pipeline pull actual conversation text using `get_meeting_transcript`. The framework chunks that text and creates dense embeddings. Now your sales engineers can query past technical discussions and get answers grounded in real customer quotes.

Index Chorus.ai (ZoomInfo) key moments

You want to search specifically for pricing objections. Call `get_meeting_moments` to extract the exact segments where money came up. Feed those segments into your vector index. When a rep asks how to handle a specific competitor pushback, the system retrieves the winning response from last week's successful deal.

Aggregate team and competitor knowledge

RAG apps need context about who is talking. Connect the MCP Server to grab the roster via `list_chorus_users` and current threats using `list_tracked_competitors`. Combine this metadata with `list_chorus_meetings`. Your query engine now understands which rep talked to which prospect about a specific rival, making your search results highly specific.

Setup guide

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

Load the tools via `llama-index-tools-mcp`. Call `get_meeting_transcript` and pass the returned text directly into your document parser for embedding.

You start by running `list_tracked_competitors`. Then, your agent cross-references those names with `list_chorus_meetings` before indexing the relevant sessions.

Yes. Use `list_meeting_playlists` to find curated call collections. Your ingestion script can loop through them and update your vector store automatically.

Pass an `allowed_tools` list when configuring your `McpToolSpec`. You might only want your agent reading `get_meeting_details` instead of pulling full transcripts.

Vinkius drops the connection and wipes the ephemeral sandbox after every request. Your raw transcripts and user profiles stay secure in transit. You control where the resulting embeddings live.

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