How to Use the Twenty CRM MCP in LlamaIndex

Q: Can I use LlamaIndex to list all accounts in Twenty CRM?

Yes, you can index the output of listcompanies. After indexing, you won't just get a raw list; you'll get semantic answers about those companies.

Q: If I want to check data privacy with LlamaIndex and Twenty CRM, what specific data type does it touch?

The server touches Notes records. By indexing these notes, you can build a knowledge base that lets you retrieve historical communication details while maintaining an audit trail.

Search past data and knowledge with LlamaIndex on Twenty CRM.

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Connect Twenty CRM MCP to LlamaIndex

Create your Vinkius account to connect Twenty CRM 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 Twenty CRM with LlamaIndex

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Creating searchable knowledge bases

LlamaIndex indexes your API results, making them part of a search index. You can feed it results from `list_companies` and later query the resulting vector store to find specifics about any company you've ever worked with in Twenty CRM.

Contextualizing conversations

Ever need context? Your agent combines data sources. It can take a `get_person` result and merge it with relevant notes from `list_notes`. The whole package—the person's details plus the historical notes—becomes searchable knowledge.

Querying complex relationship history

The server lets you build RAG apps. You can query, for instance, 'What opportunities were created around this specific person ID?' LlamaIndex grounds that answer in the actual data returned by `list_opportunities`.

Setup guide

Set up Twenty CRM 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 Twenty CRM 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 Twenty CRM tools.",
)
response = await agent.run("List recent Twenty CRM data")

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Common questions about Twenty CRM MCP in LlamaIndex

LlamaIndex doesn't just call tools; it indexes the outputs. This means you query past API results, getting answers grounded in actual `Company` or `Opportunity` data, not guesswork.

Yes, you can index the output of `list_companies`. After indexing, you won't just get a raw list; you'll get semantic answers about those companies.

While the primary focus is retrieval, you can combine live data (like checking `get_person` details) with historical knowledge in a single query for decision-making.

It's built for it. By turning API calls into indexed data, you combine live record access with document retrieval into one unified queryable index.

The server touches `Notes` records. By indexing these notes, you can build a knowledge base that lets you retrieve historical communication details while maintaining an audit trail.

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