How to Use the Notion MCP in LlamaIndex

Q: How does LlamaIndex search Notion content?

It calls search to find relevant pages by title. Then it fetches the content to build your vector index.

Q: Does LlamaIndex support deep block retrieval?

Yes, it uses retrieveblockchildren to walk the tree. This is essential for indexing content nested inside deep pages.

Index your Notion workspace into LlamaIndex to build RAG apps that ground AI responses in your actual company knowledge.

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Connect Notion MCP to LlamaIndex

Create your Vinkius account to connect Notion 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 Notion with LlamaIndex

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Semantic search across Notion with LlamaIndex

The server retrieves page content via `retrieve_page` for your index. LlamaIndex then vectorizes this data for fast, semantic retrieval. This ensures your agent answers questions using your own documents. It stops the AI from making things up.

Build knowledge graphs from Notion blocks

Use `retrieve_block_children` to map out complex page structures. You can build a hierarchy of information that the agent traverses. This gives your RAG system context. It understands how your documentation links together.

Keep your LlamaIndex knowledge base fresh

The server provides live access to your data. You can trigger `query_database` to pull in the latest project updates for your agents. Your index stays updated without manual exports. It reflects the current state of your workspace.

Setup guide

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

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Common questions about Notion MCP in LlamaIndex

It calls `search` to find relevant pages by title. Then it fetches the content to build your vector index.

Absolutely. You query the database and index the resulting rows. It makes your task lists and project trackers searchable.

Yes, it uses `retrieve_block_children` to walk the tree. This is essential for indexing content nested inside deep pages.

You provide your Notion integration token during setup. The MCP client manages the connection securely.

Your workspace data never leaves the Vinkius sandbox during indexing. We only process the page titles and block text you specifically request.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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