How to Use the Attio MCP in LlamaIndex

Q: How do I build a RAG pipeline using Attio and LlamaIndex?

You use listrecords or listnotes to fetch your CRM content, then wrap those outputs in LlamaIndex Document objects. From there, you build a vector index that your agent can query semantically to find relevant sales context.

Q: Can LlamaIndex update records in Attio after a query?

Yes, your LlamaIndex agent can use updaterecord to modify attributes based on its findings. After querying your index, the agent calls this tool to write status updates or pipeline changes back to your CRM.

Q: How do I verify API access to Attio within my LlamaIndex MCP client?

You can call checkattiostatus inside your initialization script to verify that your credentials are valid. This ensures your LlamaIndex pipeline doesn't fail mid-way through a heavy indexing job.

Index your Attio CRM data directly into LlamaIndex vector stores for highly accurate sales RAG applications.

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Connect Attio MCP to LlamaIndex

Create your Vinkius account to connect Attio 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 Attio with LlamaIndex

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Index Live CRM Objects into LlamaIndex Knowledge Bases

The `list_records` tool allows your LlamaIndex RAG pipeline to pull fresh CRM data via this MCP server. This tool extracts your raw company, contact, or deal data so you can load it into vector indexes. By feeding this data into LlamaIndex document parsers, you build a searchable knowledge base of your sales pipeline. Your agent queries this index to retrieve background context on active deals without hitting API limits.

Perform Semantic Search over Attio Notes and Tasks

The `list_notes` tool retrieves historical communication logs and meeting summaries directly from your CRM. Your agent can run this alongside `list_tasks` to gather all context associated with a specific client account. Once indexed, these notes and tasks allow your agent to answer complex questions about past interactions. You can ask who last spoke to a client and get an answer grounded in actual CRM history.

Inspect CRM Schemas via this MCP Server

The `get_object` tool pulls the structural definition of your custom CRM records directly into your index pipeline. This tool ensures your RAG system understands the exact data model of your workspace before running queries. Combine this with `list_objects` to map out your entire sales data architecture for the agent. This mapping prevents formatting errors when the agent attempts to write updates back to your workspace.

Setup guide

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

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Common questions about Attio MCP in LlamaIndex

You use `list_records` or `list_notes` to fetch your CRM content, then wrap those outputs in LlamaIndex Document objects. From there, you build a vector index that your agent can query semantically to find relevant sales context.

Yes, your LlamaIndex agent can use `update_record` to modify attributes based on its findings. After querying your index, the agent calls this tool to write status updates or pipeline changes back to your CRM.

You can call `check_attio_status` inside your initialization script to verify that your credentials are valid. This ensures your LlamaIndex pipeline doesn't fail mid-way through a heavy indexing job.

Yes, you can configure your MCP tool specification to only allow specific tools like `get_list_entries` or `list_tasks`. This prevents the agent from accessing or indexing sensitive records that you want to keep private.

Yes, your CRM notes, tasks, and records are processed entirely in-memory within the Vinkius secure sandbox. This MCP server processes CRM data ephemeral-style, meaning no customer records are cached or stored on Vinkius servers during the indexing process.

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